CN112307320A - Information pushing method and device, mobile terminal and storage medium - Google Patents
Information pushing method and device, mobile terminal and storage medium Download PDFInfo
- Publication number
- CN112307320A CN112307320A CN201910767875.7A CN201910767875A CN112307320A CN 112307320 A CN112307320 A CN 112307320A CN 201910767875 A CN201910767875 A CN 201910767875A CN 112307320 A CN112307320 A CN 112307320A
- Authority
- CN
- China
- Prior art keywords
- information
- user
- pushed
- determining
- user capacity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 59
- 238000012512 characterization method Methods 0.000 claims abstract description 186
- 238000012545 processing Methods 0.000 claims description 54
- 238000004590 computer program Methods 0.000 claims description 12
- 238000012546 transfer Methods 0.000 claims description 9
- 230000000694 effects Effects 0.000 abstract description 6
- 230000006870 function Effects 0.000 description 15
- 230000010365 information processing Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The embodiment of the disclosure discloses an information pushing method, an information pushing device, a mobile terminal and a storage medium. The information pushing method comprises the following steps: determining a model according to user capacity characterization data corresponding to a user, and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user; according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information; adjusting theoretical user capacity representation data according to actual user capacity representation data; determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and pushing the information to be pushed to the user. The technical scheme of the embodiment of the disclosure achieves the effect of pushing the information matched with the capability of the user.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computer data processing, and in particular relates to an information pushing method and device, a mobile terminal and a storage medium.
Background
With the rapid development of computer technology and networks, information push based on the internet is widely applied. The user can acquire and process information through the information push system.
When pushing information, the existing information pushing system usually pushes the same information for each user based on a fixed pushing mode.
If the processing difficulty of the information pushed by the pushing mode is higher, the enthusiasm of the user with lower information processing capability can be attacked, and if the processing difficulty is lower, the interestingness of the user with higher information processing capability can be reduced.
Disclosure of Invention
The disclosure provides an information pushing method, an information pushing device, a mobile terminal and a storage medium, so as to achieve the effect of pushing information matched with the capability of a user.
In a first aspect, an embodiment of the present disclosure provides an information pushing method, including:
determining a model according to user capacity characterization data corresponding to a user, and determining theoretical user capacity characterization data corresponding to first pushed information which has been pushed to the user;
determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information;
adjusting the theoretical user capacity representation data according to the actual user capacity representation data;
determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and
and pushing the information to be pushed to the user.
In a second aspect, an embodiment of the present disclosure further provides an information pushing apparatus, including:
the theoretical user capacity characterization data determining module is used for determining a model according to user capacity characterization data corresponding to a user and determining theoretical user capacity characterization data corresponding to first pushed information pushed to the user;
the actual user capacity representation data determining module is used for determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information;
the theoretical user capacity representation data adjusting module is used for adjusting the theoretical user capacity representation data according to the actual user capacity representation data;
the information to be pushed is determined and pushed module is used for determining the information to be pushed corresponding to the adjusted theoretical user capacity representation data; and
and pushing the information to be pushed to the user.
In a third aspect, an embodiment of the present disclosure further provides a mobile terminal, including:
one or more processing devices;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the information push method according to any embodiment of the present disclosure.
In a fourth aspect, the embodiments of the present disclosure further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the information pushing method according to any embodiment of the present disclosure.
The method comprises the steps that a model is determined according to user capacity characterization data corresponding to a user, and theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user are determined; according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information; adjusting theoretical user capacity representation data according to actual user capacity representation data; the information to be pushed corresponding to the adjusted theoretical user capability representation data is determined, and the information to be pushed is pushed to the user, so that the problem that the pushed information is difficult to match with the current information processing capability of the user in the prior art is solved, and the effect of pushing the information matched with the capability of the user is achieved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a flowchart of an information pushing method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of an information pushing method according to a second embodiment of the disclosure;
fig. 3 is a schematic structural diagram of an information pushing apparatus according to a third embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a mobile terminal according to a fourth embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
Fig. 1 is a flowchart of an information pushing method according to an embodiment of the present disclosure. The embodiment is applicable to the case of information pushing for a user, and the method may be executed by an information pushing apparatus, which may be implemented in software and/or hardware, and may be configured in a mobile terminal. As shown in fig. 1, the method may include the steps of:
s110, determining a model according to the user capacity characterization data corresponding to the user, and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user.
The first pushed information is information which is pushed to the user within a preset time period before the information is pushed this time. In this embodiment, the information may be data that is pushed to the user by the system and needs to be processed by the user. Preferably, the information may be various tasks with difficulty coefficients, wherein the tasks may be various topics with determined answers, and the like.
The user ability characterization data reflects the corresponding information processing level when the user processes the corresponding information, and exemplarily, the user ability characterization data may be a score or a grade. Different users correspond to different user capacity characterization data determination models, the user capacity characterization data determination models are related to the information and the user capacity characterization data, and each user capacity characterization data corresponding to each information is determined when the user capacity characterization data determination models are determined. Theoretical user capability characterization data can be determined using the user capability characterization data determination model and the first pushed information. The theoretical user capability representation data reflects the information processing level that the user theoretically needs to have when processing the first pushed information. The theoretical user capability characterization data is related to the first pushed information, and if the first pushed information is determined, the theoretical user capability characterization data is determined and does not change with the change of the feedback of the first pushed information.
For example, if the information is a topic, the user ability characterization data can be a theoretical score corresponding to the topic, and accordingly, the user ability characterization data determination model can be associated with the topic and the theoretical score. The first pushed information may include one or more pieces of information, and one piece of information corresponds to one piece of user capability representation data. If the first pushed information comprises a plurality of information, the theoretical user capability characterizing data may preferably be an average of the plurality of user capability characterizing data, it being understood that it may also be a sum of the plurality of user capability characterizing data.
For example, if the first pushed information is a plurality of topics, the user ability characterization data may be used to determine a model, and determine a plurality of theoretical scores corresponding to the plurality of topics, and accordingly, the theoretical user ability characterization data is an average theoretical score of the plurality of theoretical scores.
And S120, determining actual user capacity characterization data corresponding to the first pushed information according to the feedback of the user to the first pushed information.
The feedback may preferably be processing result data of the first pushed information, where the processing result data may include whether the processing result corresponding to each piece of the first pushed information is correct, may further include a processing time corresponding to the user processing each piece of the first pushed information, and the like. The actual user capability characterization data reflects the level of information processing that the user actually has when processing the first pushed information. For example, if a processing result corresponding to one piece of information is an error, the actual user capacity characterization data corresponding to the piece of information may preferably be zero or user capacity characterization data lower than a preset threshold; if the processing result corresponding to one piece of information is correct, the actual user capacity characterization data corresponding to the piece of information preferably can be user capacity characterization data corresponding to the piece of information in a user capacity characterization data determination model; if the processing time corresponding to one piece of information is long (compared with the preset time length), the actual user capacity characterization data corresponding to the piece of information can be preferably user capacity characterization data lower than a preset threshold value; if the processing time corresponding to a message is short (compared with the preset time length), the actual user capacity characterization data corresponding to the message may preferably be the user capacity characterization data which is higher than the preset threshold.
And S130, adjusting the theoretical user capacity representation data according to the actual user capacity representation data.
In this embodiment, in order to match the pushed information with the capability of the user, it is preferable that the theoretical user capability representation data be adjusted according to the actual user capability representation data, and the adjustment manner may be to increase the theoretical user capability representation data or to decrease the theoretical user capability representation data.
S140, determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and pushing the information to be pushed to the user.
Preferably, the user capability representation data determination model may be used to determine information to be pushed corresponding to the adjusted theoretical user capability representation data, where the number of the information to be pushed may be one or multiple. When the number of the information to be pushed is multiple, the multiple information to be pushed may be information with the same difficulty coefficient or information with different difficulty coefficients.
In the information push method provided by this embodiment, a model is determined according to user capability characterization data corresponding to a user, and theoretical user capability characterization data corresponding to first pushed information that has been pushed to the user is determined; according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information; adjusting theoretical user capacity representation data according to actual user capacity representation data; determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and the information to be pushed is pushed to the user, so that the problem that the pushed information is difficult to be matched with the current information processing capability of the user in the prior art is solved, and the effect of pushing the information matched with the capability of the user is achieved.
On the basis of the foregoing embodiments, further, the user capability characterization data determination model includes:
a first relationship curve between the information difficulty characterization data and the first user ability characterization data and a second relationship curve between the information complexity characterization data and the second user ability characterization data.
Taking information as a title and user capability characterization data as a score as an example, specifically explaining a user capability characterization data determination model:
the information difficulty characterization data is topic accuracy, and the topic accuracy corresponding to each topic can be preferably determined according to the historical processing result data of all users corresponding to each topic. Illustratively, the number of all users corresponding to a topic is 1000, where the historical processing result of 100 users is correct, and the historical processing result of 900 users is incorrect, and then the topic correctness rate corresponding to the topic is (100/1000) × 100% — 10%.
The first user capacity characterization data is a first theoretical score which is determined according to the historical processing result data of the user and corresponds to the title accuracy rate in a one-to-one mode, and preferably, the range of the first theoretical score can be 0-10. Illustratively, the user has a high information processing level, and for a plurality of topics with a topic accuracy rate of 10%, the historical processing result shows that the accuracy rate is 90%. At this time, if the first theoretical score corresponding to the topic accuracy rate of 10% determined by the above-described historical processing result data of all users is 1, the first theoretical score corresponding to the topic accuracy rate of 10% is 9 for the user.
The information complexity characterization data is processing time, and the processing time corresponding to each topic can be preferably determined according to the historical processing result data of all users corresponding to each topic. Illustratively, the number of all users corresponding to a topic is 1000, where the historical processing time of 100 users is 2 minutes, the historical processing time of 300 users is 3 minutes, the historical processing time of 400 users is 4 minutes, and the historical processing time of 200 users is 5 minutes, then the processing time corresponding to the topic is (100 × 2+300 × 3+400 × 4+200 × 5)/1000 ═ 3.7 minutes.
The second user ability characterization data is a second theoretical score which is determined according to the historical processing result data of the user and corresponds to the processing time in a one-to-one mode, and preferably, the range of the second theoretical score can be 1-10. Illustratively, the information processing level of the user is high, and the processing time is 2 minutes as a result of the history processing for a plurality of titles whose processing time is 3.7 minutes. At this time, if the first theoretical score corresponding to the processing time of 3.7 minutes determined by the above-described historical processing result data of all users is 4, the first theoretical score corresponding to the processing time of 3.7 minutes may be 7.4 for the user.
It should be noted that, for a new user, the titles that the system first pushes for the new user are determined according to the standard user capability characterization data determination model. The first relation curve and the second relation curve in the standard user capacity characterization data determination model are determined according to historical processing result data of all users corresponding to each topic. For example, a first theoretical score of 1 for a topic accuracy of 10% and a first theoretical score of 4 for a processing time of 3.7 minutes. And then, with the increase of the number of the processing topics of the user, on the basis of a standard user capacity characterization data determination model, adjusting the first user capacity characterization data and the second user capacity characterization data to obtain a personalized user capacity characterization data determination model corresponding to the user. The specific adjustment manner is similar to the above-described method for determining the first user capability representation data and the second user capability representation data, and is not described herein again.
On the basis of the foregoing embodiments, further, determining, according to feedback of the user on the first pushed information, actual user capability characterization data corresponding to the first pushed information includes:
determining second pushed information, wherein the second pushed information is the first pushed information with correct feedback;
according to information difficulty representation data corresponding to the second pushed information, determining a model by using user capacity representation data, and determining first user capacity representation data corresponding to the second pushed information;
according to information complexity characterization data corresponding to the first pushed information, determining a model by using user capacity characterization data, and determining second user capacity characterization data corresponding to the first pushed information;
and determining the actual user capacity representation data corresponding to the first pushed information by utilizing the first user capacity representation data, the first preset weight corresponding to the first user capacity representation data, and the second preset weight corresponding to the second user capacity representation data.
Still taking information as a topic and user capability characterization data as a score as an example, the following steps are specifically described:
determining each second topic with correct feedback, determining a model by using user capability characterization data, determining a first theoretical score of each second topic according to the topic accuracy of each second topic, and determining a second theoretical score of each first topic according to the processing time of each first topic. It can be understood that for each first topic for which the feedback is incorrect, the corresponding first theoretical score is 0.
Preferably, it can be according to w1*a+w2B determining actual user capability characterization data, wherein w1A first predetermined weight, w, corresponding to the first theoretical score2And a is a second preset weight corresponding to a second theoretical score, a is the first theoretical score, and b is the second theoretical score. Wherein for w1And w2The value of (a) is not specifically limited, and the optimal value of (b) is a value corresponding to the time when the optimal actual user capability representation data is acquired.
On the basis of the foregoing embodiments, further, if it is determined that the number of the pushed history information is higher than the preset number, a first user capability characterization data determination model is determined according to the feedback of the user on the history information and information difficulty characterization data corresponding to the history information, and the first user capability characterization data determination model is used as the user capability characterization data determination model.
The method for determining the first user capability representation data determination model is similar to the method for determining the user capability representation data determination model based on the standard user capability representation data determination model, and is not repeated here.
Example two
Fig. 2 is a flowchart of an information pushing method according to a second embodiment of the disclosure. This embodiment may be combined with each of the alternatives described above in one or more embodiments, in which embodiment,
the adjusting the theoretical user capability representation data according to the actual user capability representation data comprises:
determining a first absolute value of a difference between the actual user capability characterization data and the theoretical user capability characterization data;
adjusting the first absolute value according to a preset transfer function to obtain a second absolute value;
if the actual user capacity characterization data is larger than the theoretical user capacity characterization data, subtracting the second absolute value by using the theoretical user capacity characterization data;
and if the actual user capacity representation data is smaller than the theoretical user capacity representation data, adding the second absolute value by using the theoretical user capacity representation data.
As shown in fig. 2, the method may include the steps of:
s210, determining a model according to the user capacity characterization data corresponding to the user, and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user.
S220, according to the feedback of the user to the first pushed information, determining actual user capacity characterization data corresponding to the first pushed information.
And S230, determining a first absolute value of a difference value between the actual user capacity characterization data and the theoretical user capacity characterization data.
S240, adjusting the first absolute value according to a preset transfer function to obtain a second absolute value.
And S250, if the actual user capacity representation data is larger than the theoretical user capacity representation data, subtracting the second absolute value by using the theoretical user capacity representation data.
And S260, if the actual user capacity representation data is smaller than the theoretical user capacity representation data, adding the second absolute value by using the theoretical user capacity representation data.
And the steps of S230-S260 are based on the feedback principle, and the theoretical user capacity representation data is adjusted by using the actual user capacity representation data. The preset transfer function may preferably be a PID (proportional, integral, differential) transfer function.
If the actual user capacity representation data is larger than the theoretical user capacity representation data, it indicates that the first push information is relatively simple for the current user, and at this time, the difficulty of the first push information needs to be increased. The second absolute value is thus subtracted using the theoretical user capability characterization data.
If the actual user capacity representation data is smaller than the theoretical user capacity representation data, it indicates that the first push information is relatively difficult for the current user, and at this time, the difficulty of the first push information needs to be reduced. The second absolute value is thus added using the theoretical user capability characterization data.
S270, determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and pushing the information to be pushed to the user.
In the information push method provided by this embodiment, a model is determined according to user capability characterization data corresponding to a user, and theoretical user capability characterization data corresponding to first pushed information that has been pushed to the user is determined; according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information; determining a first absolute value of a difference between the actual user capacity characterization data and the theoretical user capacity characterization data; adjusting the first absolute value according to a preset transfer function to obtain a second absolute value; if the actual user capacity representation data is larger than the theoretical user capacity representation data, subtracting the second absolute value by using the theoretical user capacity representation data; if the actual user capacity representation data is smaller than the theoretical user capacity representation data, adding the second absolute value by using the theoretical user capacity representation data; determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and the information to be pushed is pushed to the user, so that the problem that the pushed information is difficult to be matched with the current information processing capability of the user in the prior art is solved, and the effect of pushing the information matched with the capability of the user is achieved.
On the basis of the foregoing embodiments, further, determining information to be pushed corresponding to the adjusted theoretical user capability representation data includes:
if the number of the information to be pushed is one, determining a model by using the user capacity characterization data according to the adjusted theoretical user capacity characterization data, and determining first information difficulty characterization data corresponding to the adjusted theoretical user capacity characterization data;
and determining the information to be pushed corresponding to the first information difficulty representation data according to the first information difficulty representation data.
Still taking information as a topic and user capability characterization data as a score as an example, the following steps are specifically described:
and if the number of the questions to be pushed is one, determining a model by using the user capability representation data according to the adjusted theoretical score, determining a first question accuracy corresponding to the adjusted theoretical score, and determining the questions to be pushed corresponding to the question accuracy according to the first question accuracy.
On the basis of the foregoing embodiments, further, determining information to be pushed corresponding to the adjusted theoretical user capability representation data further includes:
if the number of the information to be pushed is at least two and the information difficulty representation data corresponding to each information to be pushed is different, determining first user capacity representation data corresponding to each information to be pushed according to the adjusted theoretical user capacity representation data and the preset user capacity representation data distribution proportion;
according to first user capacity representation data corresponding to each piece of information to be pushed, determining a model by using the user capacity representation data, and determining second information difficulty representation data corresponding to each piece of adjusted theoretical user capacity representation data;
and determining the information to be pushed corresponding to each second information difficulty representation data according to each second information difficulty representation data.
Still taking information as a topic and user capability characterization data as a score as an example, the following steps are specifically described:
and determining a first theoretical score corresponding to each topic to be pushed according to the adjusted theoretical score and a preset score distribution ratio if the number of the topics to be pushed is three and the correctness rate of the topic corresponding to each topic to be pushed is different. Illustratively, the adjusted theoretical score is 7, the preset score distribution ratio is 1:2:3, and the first theoretical scores corresponding to the three to-be-pushed topics are 3.5, 7 and 10.5, respectively.
And determining a model by using the user capability characterization data according to the first theoretical score corresponding to each topic to be pushed, and determining the accuracy of a second topic corresponding to each first theoretical score. And determining the to-be-pushed topic corresponding to each second topic accuracy rate according to each second topic accuracy rate.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an information pushing apparatus according to a third embodiment of the present disclosure. The embodiment can be applied to the situation of information push for the user. The apparatus may be implemented in software and/or hardware, and may be configured in a mobile terminal. As shown in fig. 3, the apparatus may include: the system comprises a theoretical user capacity characterization data determination module 310, an actual user capacity characterization data determination module 320, a theoretical user capacity characterization data adjustment module 330 and a to-be-pushed information determination and pushing module 340.
The theoretical user capacity characterization data determining module 310 is configured to determine, according to a user capacity characterization data determination model corresponding to a user, theoretical user capacity characterization data corresponding to first pushed information that has been pushed to the user;
an actual user capability representation data determining module 320, configured to determine, according to feedback of the user on the first pushed information, actual user capability representation data corresponding to the first pushed information;
a theoretical user ability representation data adjusting module 330, configured to adjust the theoretical user ability representation data according to the actual user ability representation data;
the to-be-pushed information determining and pushing module 340 is configured to determine to-be-pushed information corresponding to the adjusted theoretical user capability representation data; and pushing the information to be pushed to the user.
In the information pushing apparatus provided in this embodiment, a theoretical user capability representation data determining module determines, according to a user capability representation data determining model corresponding to a user, theoretical user capability representation data corresponding to first pushed information that has been pushed to the user; determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information through an actual user capacity representation data determination module; adjusting the theoretical user capacity representation data according to the actual user capacity representation data through a theoretical user capacity representation data adjusting module; determining and pushing information to be pushed corresponding to the adjusted theoretical user capacity representation data through an information to be pushed determining and pushing module; and the information to be pushed is pushed to the user, so that the problem that the pushed information is difficult to be matched with the current information processing capability of the user in the prior art is solved, and the effect of pushing the information matched with the capability of the user is achieved.
On the basis of the above technical solution, optionally, the user capability characterization data determination model includes:
a first relationship curve between the information difficulty characterization data and the first user ability characterization data and a second relationship curve between the information complexity characterization data and the second user ability characterization data.
On the basis of the foregoing technical solution, optionally, the actual user capability characterization data determining module 320 may specifically include:
the second pushed information determining unit is used for determining second pushed information, wherein the second pushed information is the first pushed information which is fed back correctly;
the first user capacity characterization data determining unit is used for determining a model according to information difficulty characterization data corresponding to the second pushed information by using the user capacity characterization data to determine first user capacity characterization data corresponding to the second pushed information;
the second user capacity characterization data determining unit is used for determining second user capacity characterization data corresponding to the first pushed information by utilizing a user capacity characterization data determination model according to information complexity characterization data corresponding to the first pushed information;
and the actual user capacity characterization data determining unit is used for determining the actual user capacity characterization data corresponding to the first pushed information by using the first user capacity characterization data, the first preset weight corresponding to the first user capacity characterization data, the second user capacity characterization data and the second preset weight corresponding to the second user capacity characterization data.
On the basis of the foregoing technical solution, optionally, the theoretical user capability characterization data adjusting module 330 may specifically include:
a first absolute value determination unit for determining a first absolute value of a difference between the actual user capability characterization data and the theoretical user capability characterization data;
a second absolute value determining unit, configured to adjust the first absolute value according to a preset transfer function to obtain a second absolute value;
the subtraction operation unit is used for carrying out subtraction operation on the second absolute value by using the theoretical user capacity representation data if the actual user capacity representation data is larger than the theoretical user capacity representation data;
and the addition operation unit is used for performing addition operation on the second absolute value by using the theoretical user capacity representation data if the actual user capacity representation data is smaller than the theoretical user capacity representation data.
On the basis of the foregoing technical solution, optionally, the module 440 for determining and pushing information to be pushed may specifically include:
the first information difficulty representation data determining unit is used for determining a model according to the adjusted theoretical user capacity representation data and by using the user capacity representation data if the number of the information to be pushed is one, and determining first information difficulty representation data corresponding to the adjusted theoretical user capacity representation data;
and the first information to be pushed determining unit is used for determining the information to be pushed corresponding to the first information difficulty representation data according to the first information difficulty representation data.
On the basis of the foregoing technical solution, optionally, the module 440 for determining and pushing information to be pushed may specifically include:
the first user capacity characterization data determining unit is used for determining first user capacity characterization data corresponding to each piece of information to be pushed according to the adjusted theoretical user capacity characterization data and the preset user capacity characterization data distribution proportion if the number of the pieces of information to be pushed is at least two and the information difficulty characterization data corresponding to each piece of information to be pushed is different;
the second information difficulty representation data determining unit is used for determining a model by utilizing the user ability representation data according to the first user ability representation data corresponding to each piece of information to be pushed, and determining second information difficulty representation data corresponding to each piece of adjusted theoretical user ability representation data;
and the second information to be pushed determining unit is used for determining the information to be pushed corresponding to each second information difficulty representation data according to each second information difficulty representation data.
On the basis of the foregoing technical solution, optionally, the information pushing apparatus may further include:
and the user capacity characterization data determination model adjusting module is used for determining a first user capacity characterization data determination model according to the feedback of the user to the historical information and the information difficulty characterization data corresponding to the historical information if the number of the pushed historical information is higher than the preset number, and taking the first user capacity characterization data determination model as the user capacity characterization data determination model.
The information pushing device provided by the embodiment of the disclosure can execute the information pushing method provided by the embodiment of the disclosure, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Referring now to fig. 4, a block diagram of a mobile terminal 400 suitable for use in implementing embodiments of the present disclosure is shown. The mobile terminal in the embodiments of the present disclosure may include, but is not limited to, devices such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like. The mobile terminal shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the mobile terminal 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 406 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the mobile terminal 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 406 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the mobile terminal 400 to communicate with other devices, either wirelessly or by wire, for exchanging data. While fig. 4 illustrates a mobile terminal 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 409, or from the storage means 406, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the mobile terminal; or may exist separately and not be incorporated into the mobile terminal.
The computer readable medium carries one or more programs which, when executed by the mobile terminal, cause the mobile terminal to: determining a model according to user capacity characterization data corresponding to a user, and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user; according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information; adjusting theoretical user capacity representation data according to actual user capacity representation data; determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and pushing the information to be pushed to the user.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods, apparatus, mobile terminals, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules, units and sub-units described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware. For example, the actual user capability representation data determining module may be further described as a "module for determining actual user capability representation data corresponding to first pushed information according to feedback of the first pushed information by the user", and the second pushed information determining unit may be further described as "a unit for determining second pushed information, wherein the second pushed information is fed back as correct first pushed information".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
According to one or more embodiments of the present disclosure, an example provides an information pushing method, including:
determining a model according to user capacity characterization data corresponding to a user, and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user;
according to the feedback of the user to the first pushed information, determining actual user capacity representation data corresponding to the first pushed information;
adjusting theoretical user capacity representation data according to actual user capacity representation data;
determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and
and pushing the information to be pushed to the user.
According to one or more embodiments of the present disclosure, example two provides an information pushing method, and on the basis of the information pushing method of example one, the user capability characterization data determination model includes:
a first relationship curve between the information difficulty characterization data and the first user ability characterization data and a second relationship curve between the information complexity characterization data and the second user ability characterization data.
According to one or more embodiments of the present disclosure, example three provides an information pushing method, and on the basis of the information pushing method of example two, determining, according to feedback of a user to first pushed information, actual user capability characterization data corresponding to the first pushed information includes:
determining second pushed information, wherein the second pushed information is the first pushed information with correct feedback;
according to information difficulty representation data corresponding to the second pushed information, determining a model by using user capacity representation data, and determining first user capacity representation data corresponding to the second pushed information;
according to information complexity characterization data corresponding to the first pushed information, determining a model by using user capacity characterization data, and determining second user capacity characterization data corresponding to the first pushed information;
and determining the actual user capacity representation data corresponding to the first pushed information by utilizing the first user capacity representation data, the first preset weight corresponding to the first user capacity representation data, and the second preset weight corresponding to the second user capacity representation data.
According to one or more embodiments of the present disclosure, example four provides an information pushing method, and on the basis of the information pushing method of example one or example two, the theoretical user capability characterization data is adjusted according to the actual user capability characterization data, including:
determining a first absolute value of a difference between the actual user capacity characterization data and the theoretical user capacity characterization data;
adjusting the first absolute value according to a preset transfer function to obtain a second absolute value;
if the actual user capacity representation data is larger than the theoretical user capacity representation data, subtracting the second absolute value by using the theoretical user capacity representation data;
and if the actual user capacity representation data is smaller than the theoretical user capacity representation data, adding the second absolute value by using the theoretical user capacity representation data.
According to one or more embodiments of the present disclosure, example five provides an information pushing method, and on the basis of the information pushing method of example one or example two, determining information to be pushed corresponding to the adjusted theoretical user capability representation data includes:
if the number of the information to be pushed is one, determining a model by using the user capacity characterization data according to the adjusted theoretical user capacity characterization data, and determining first information difficulty characterization data corresponding to the adjusted theoretical user capacity characterization data;
and determining the information to be pushed corresponding to the first information difficulty representation data according to the first information difficulty representation data.
According to one or more embodiments of the present disclosure, example six provides an information pushing method, and on the basis of the information pushing method of example one or example two, the method determines information to be pushed corresponding to the adjusted theoretical user capability representation data, further includes:
if the number of the information to be pushed is at least two and the information difficulty representation data corresponding to each information to be pushed is different, determining first user capacity representation data corresponding to each information to be pushed according to the adjusted theoretical user capacity representation data and the preset user capacity representation data distribution proportion;
according to first user capacity representation data corresponding to each piece of information to be pushed, determining a model by using the user capacity representation data, and determining second information difficulty representation data corresponding to each piece of adjusted theoretical user capacity representation data;
and determining the information to be pushed corresponding to each second information difficulty representation data according to each second information difficulty representation data.
According to one or more embodiments of the present disclosure, example seven provides an information pushing method, and on the basis of the information pushing method of example one or example two, the method further includes:
and if the number of the pushed historical information is higher than the preset number, determining a first user capacity characterization data determination model according to the feedback of the user to the historical information and information difficulty characterization data corresponding to the historical information, and taking the first user capacity characterization data determination model as a user capacity characterization data determination model.
Example eight provides, in accordance with one or more embodiments of the present disclosure, an information pushing apparatus, comprising:
the theoretical user capacity characterization data determining module is used for determining a model according to user capacity characterization data corresponding to the user and determining theoretical user capacity characterization data corresponding to first pushed information which is pushed to the user;
the actual user capacity representation data determining module is used for determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information;
the theoretical user capacity representation data adjusting module is used for adjusting the theoretical user capacity representation data according to the actual user capacity representation data;
the information to be pushed is determined and pushed module, is used for confirming the information to be pushed corresponding to theoretical user ability characterization data after adjusting; and
and pushing the information to be pushed to the user.
Example nine provides, in accordance with one or more embodiments of the present disclosure, a mobile terminal, comprising:
one or more processing devices;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processing devices, the one or more processing devices are caused to implement the information push method according to any one of examples one to seven.
Example ten provides a computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the information push method as in any one of examples one to seven.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. An information pushing method, comprising:
determining a model according to user capacity characterization data corresponding to a user, and determining theoretical user capacity characterization data corresponding to first pushed information which has been pushed to the user;
determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information;
adjusting the theoretical user capacity representation data according to the actual user capacity representation data;
determining information to be pushed corresponding to the adjusted theoretical user capacity representation data; and
and pushing the information to be pushed to the user.
2. The method of claim 1, wherein the user capability characterization data determination model comprises:
a first relationship curve between the information difficulty characterization data and the first user ability characterization data and a second relationship curve between the information complexity characterization data and the second user ability characterization data.
3. The method of claim 2, wherein the determining actual user capability characterization data corresponding to the first pushed information according to the user's feedback on the first pushed information comprises:
determining second pushed information, wherein the second pushed information is the first pushed information of which the feedback is correct;
determining a model by using the user capacity characterization data according to information difficulty characterization data corresponding to the second pushed information, and determining the first user capacity characterization data corresponding to the second pushed information;
according to information complexity characterization data corresponding to the first pushed information, determining a model by using the user capacity characterization data, and determining second user capacity characterization data corresponding to the first pushed information;
and determining actual user capacity representation data corresponding to the first pushed information by using the first user capacity representation data, a first preset weight corresponding to the first user capacity representation data, and a second preset weight corresponding to the second user capacity representation data and the second user capacity representation data.
4. The method according to claim 1 or 2, wherein said adjusting said theoretical user capability characterization data based on said actual user capability characterization data comprises:
determining a first absolute value of a difference between the actual user capability characterization data and the theoretical user capability characterization data;
adjusting the first absolute value according to a preset transfer function to obtain a second absolute value;
if the actual user capacity characterization data is larger than the theoretical user capacity characterization data, subtracting the second absolute value by using the theoretical user capacity characterization data;
and if the actual user capacity representation data is smaller than the theoretical user capacity representation data, adding the second absolute value by using the theoretical user capacity representation data.
5. The method according to claim 1 or 2, wherein the determining information to be pushed corresponding to the adjusted theoretical user capability representation data comprises:
if the number of the information to be pushed is one, determining a model by using the user capability representation data according to the adjusted theoretical user capability representation data, and determining first information difficulty representation data corresponding to the adjusted theoretical user capability representation data;
and determining the information to be pushed corresponding to the first information difficulty representation data according to the first information difficulty representation data.
6. The method according to claim 1 or 2, wherein the determining the information to be pushed corresponding to the adjusted theoretical user capability representation data further comprises:
if the number of the information to be pushed is at least two and the information difficulty representation data corresponding to each information to be pushed is different, determining the first user capacity representation data corresponding to each information to be pushed according to the adjusted theoretical user capacity representation data and a preset user capacity representation data distribution proportion;
according to the first user capacity representation data corresponding to each piece of information to be pushed, determining a model by utilizing the user capacity representation data, and determining second information difficulty representation data corresponding to each piece of adjusted theoretical user capacity representation data;
and determining the information to be pushed corresponding to each second information difficulty representation data according to each second information difficulty representation data.
7. The method of claim 1 or 2, further comprising:
if the number of the pushed historical information is higher than the preset number, determining a first user capacity characterization data determination model according to the feedback of the user to the historical information and information difficulty characterization data corresponding to the historical information, and taking the first user capacity characterization data determination model as the user capacity characterization data determination model.
8. An information pushing apparatus, comprising:
the theoretical user capacity characterization data determining module is used for determining a model according to user capacity characterization data corresponding to a user and determining theoretical user capacity characterization data corresponding to first pushed information pushed to the user;
the actual user capacity representation data determining module is used for determining actual user capacity representation data corresponding to the first pushed information according to the feedback of the user to the first pushed information;
the theoretical user capacity representation data adjusting module is used for adjusting the theoretical user capacity representation data according to the actual user capacity representation data;
the information to be pushed is determined and pushed module is used for determining the information to be pushed corresponding to the adjusted theoretical user capacity representation data; and
and pushing the information to be pushed to the user.
9. A mobile terminal, characterized in that the mobile terminal comprises:
one or more processing devices;
storage means for storing one or more programs;
when executed by the one or more processing devices, cause the one or more processing devices to implement the information push method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the information push method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910767875.7A CN112307320A (en) | 2019-08-20 | 2019-08-20 | Information pushing method and device, mobile terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910767875.7A CN112307320A (en) | 2019-08-20 | 2019-08-20 | Information pushing method and device, mobile terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112307320A true CN112307320A (en) | 2021-02-02 |
Family
ID=74486809
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910767875.7A Pending CN112307320A (en) | 2019-08-20 | 2019-08-20 | Information pushing method and device, mobile terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112307320A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676334A (en) * | 2022-04-19 | 2022-06-28 | 江苏迈拓网络科技有限公司 | Personalized intelligent exercise recommendation method and recommendation system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104835087A (en) * | 2015-04-30 | 2015-08-12 | 泸州市金点教育科技有限公司 | Data processing method and apparatus for education test system |
| CN106407237A (en) * | 2015-08-03 | 2017-02-15 | 科大讯飞股份有限公司 | An online study test question recommendation method and system |
| CN106652620A (en) * | 2016-12-29 | 2017-05-10 | 广东小天才科技有限公司 | Terminal evaluation method and device |
| CN106682768A (en) * | 2016-12-08 | 2017-05-17 | 北京粉笔蓝天科技有限公司 | Prediction method, system, terminal and server of test score |
| CN109086441A (en) * | 2018-08-15 | 2018-12-25 | 北京博赛在线网络科技有限公司 | Intelligent recommendation method and system for online answer |
| CN109360457A (en) * | 2018-09-03 | 2019-02-19 | 浙江学海教育科技有限公司 | A kind of topic method for pushing, storage medium and application system |
| CN109981719A (en) * | 2017-12-28 | 2019-07-05 | 北京京东尚科信息技术有限公司 | Information processing method and its system, computer system and computer readable medium |
| CN110046147A (en) * | 2019-04-22 | 2019-07-23 | 上海乂学教育科技有限公司 | It is applicable in user's learning ability value-acquiring method and its application of Adaptable System |
-
2019
- 2019-08-20 CN CN201910767875.7A patent/CN112307320A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104835087A (en) * | 2015-04-30 | 2015-08-12 | 泸州市金点教育科技有限公司 | Data processing method and apparatus for education test system |
| CN106407237A (en) * | 2015-08-03 | 2017-02-15 | 科大讯飞股份有限公司 | An online study test question recommendation method and system |
| CN106682768A (en) * | 2016-12-08 | 2017-05-17 | 北京粉笔蓝天科技有限公司 | Prediction method, system, terminal and server of test score |
| CN106652620A (en) * | 2016-12-29 | 2017-05-10 | 广东小天才科技有限公司 | Terminal evaluation method and device |
| CN109981719A (en) * | 2017-12-28 | 2019-07-05 | 北京京东尚科信息技术有限公司 | Information processing method and its system, computer system and computer readable medium |
| CN109086441A (en) * | 2018-08-15 | 2018-12-25 | 北京博赛在线网络科技有限公司 | Intelligent recommendation method and system for online answer |
| CN109360457A (en) * | 2018-09-03 | 2019-02-19 | 浙江学海教育科技有限公司 | A kind of topic method for pushing, storage medium and application system |
| CN110046147A (en) * | 2019-04-22 | 2019-07-23 | 上海乂学教育科技有限公司 | It is applicable in user's learning ability value-acquiring method and its application of Adaptable System |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114676334A (en) * | 2022-04-19 | 2022-06-28 | 江苏迈拓网络科技有限公司 | Personalized intelligent exercise recommendation method and recommendation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110781373B (en) | List updating method and device, readable medium and electronic equipment | |
| CN112954354B (en) | Video transcoding method, device, equipment and medium | |
| CN115257727A (en) | Obstacle information fusion method and device, electronic equipment and computer readable medium | |
| CN112684899A (en) | Client operation control method and device, electronic equipment and computer readable medium | |
| CN110737846B (en) | Social interface recommendation method and device, electronic equipment and storage medium | |
| CN112379982A (en) | Task processing method and device, electronic equipment and computer readable storage medium | |
| CN111209432A (en) | Information acquisition method and device, electronic equipment and computer readable medium | |
| CN114038465B (en) | Voice processing method and device and electronic equipment | |
| CN112380883A (en) | Model training method, machine translation method, device, equipment and storage medium | |
| CN113220281A (en) | Information generation method and device, terminal equipment and storage medium | |
| CN112307320A (en) | Information pushing method and device, mobile terminal and storage medium | |
| CN111258582A (en) | Window rendering method and device, computer equipment and storage medium | |
| CN111628913B (en) | Online time length determining method and device, readable medium and electronic equipment | |
| CN110209851B (en) | Model training method and device, electronic equipment and storage medium | |
| CN111680754B (en) | Image classification method, device, electronic equipment and computer readable storage medium | |
| CN115272760A (en) | Small sample smoke image fine classification method suitable for forest fire smoke detection | |
| CN110633115A (en) | Task distribution method and device, electronic equipment and storage medium | |
| CN112200643A (en) | Article information pushing method and device, electronic equipment and computer readable medium | |
| CN112019406A (en) | Flow monitoring method and device, electronic equipment and computer readable medium | |
| CN111399730A (en) | Image processing method, image processing device, electronic equipment and computer readable storage medium | |
| CN112311840A (en) | Multi-terminal data synchronization method, device, equipment and medium | |
| CN112073202B (en) | Information generation method and device, electronic equipment and computer readable medium | |
| CN114374738B (en) | Information pushing method and device, storage medium and electronic equipment | |
| CN116755889B (en) | Data acceleration method, device and equipment applied to server cluster data interaction | |
| CN112590811B (en) | Method, apparatus, electronic device, and medium for controlling longitudinal travel of vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |