CN108960961A - A kind of books recommended method and books recommender system - Google Patents
A kind of books recommended method and books recommender system Download PDFInfo
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- CN108960961A CN108960961A CN201810558631.3A CN201810558631A CN108960961A CN 108960961 A CN108960961 A CN 108960961A CN 201810558631 A CN201810558631 A CN 201810558631A CN 108960961 A CN108960961 A CN 108960961A
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Abstract
This application discloses a kind of books recommended method and books recommender systems, this method comprises: being retrieved as the score of the topic of user's recommendation；According to the score, for user's recommended book information, the short slab in terms of the knowledge of user is able to reflect out due to above-mentioned score, or it is able to reflect out the direction for the knowledge that user requires supplementation with, therefore according to it is above-mentioned be scored at user's recommended book information when, the matching rate of the demand of the book information and user of recommendation is relatively high, so that user is after buying books according to the book information of recommendation, the matching rate of the books and self-demand that buy is also relatively high.
Description
Technical field
This application involves computer fields, in particular to a kind of books recommended method and books recommender system.
Background technique
With the continuous development of Internet technology, the theory of knowledge payment deepens continuously the popular feeling, and user can be in shopping platform
Upper purchase books enrich itself, but user may understanding to itself it is insufficient, or only according to the books on shopping platform
Promotional content purchase, so that the books of purchase and the matching degree itself needed are relatively low.
Summary of the invention
The main purpose of the application is to provide a kind of books recommended method and books recommender system, so that user's purchase
The matching degree of books and self-demand is relatively high.
To achieve the goals above, this application provides a kind of books recommended methods, which comprises
It is retrieved as the score of the topic of user's recommendation；
It is user's recommended book information according to the score.
Optionally, the score of the topic for being retrieved as user's recommendation, comprising:
Obtain the interest mark of user's selection；
The interest is identified into corresponding topic and is sent to the user；
Comparing the user is the answer and target answer that the topic provides, to obtain comparison result；
According to the comparison result, it is determined as the score for the topic that the user recommends.
It is optionally, described that the corresponding topic of interest mark is sent to the user, comprising:
Determine that the interest identifies at least one corresponding targets option；
According to default allocation rule, division arithmetic is carried out to the number of preset threshold and the targets option, it is each to determine
The corresponding topic number of targets option；
Each targets option is selected from the corresponding topic of each targets option according to the corresponding topic number of each targets option
The topic of corresponding number, is sent to user for the topic that each targets option corresponds to number.
Optionally, the interest mark includes character recognition and label.
Optionally, described according to the comparison result, it is determined as the score for the topic that the user recommends, comprising:
Determine the comparison result of the corresponding topic of each targets option；
According to the comparison result of the corresponding topic of each targets option and the score value for the corresponding topic distribution of each targets option,
Determine that each targets option corresponds to the score of topic.
Optionally, described according to the score, it is user's recommended book information, comprising:
The score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option；
Determine the least targets option of score；
According to default recommendation rules, the book information of the corresponding specified quantity of the least targets option of score is recommended into institute
State user.
To achieve the goals above, this application provides a kind of books recommender system, the books recommender system includes:
Acquiring unit, the score of the topic for being retrieved as user's recommendation；
Recommendation unit, for being user's recommended book information according to the score.
Optionally, when being used to be retrieved as the score of topic of user's recommendation in the acquiring unit, it is specifically used for:
Obtain the interest mark of user's selection；
The interest is identified into corresponding topic and is sent to the user；
Comparing the user is the answer and target answer that the topic provides, to obtain comparison result；
According to the comparison result, it is determined as the score for the topic that the user recommends.
Optionally, when the acquiring unit is used to the interest identifying corresponding topic and is sent to the user, tool
Body is used for:
Determine that the interest identifies at least one corresponding targets option；
According to default allocation rule, division arithmetic is carried out to the number of preset threshold and the targets option, it is each to determine
The corresponding topic number of targets option；
Each targets option is selected from the corresponding topic of each targets option according to the corresponding topic number of each targets option
The topic of corresponding number, is sent to user for the topic that each targets option corresponds to number.
The interest mark includes character recognition and label.
Optionally, it is used to be determined as according to the comparison result topic that the user recommends in the acquiring unit
When score, it is specifically used for:
Determine the comparison result of the corresponding topic of each targets option；
According to the comparison result of the corresponding topic of each targets option and the score value for the corresponding topic distribution of each targets option,
Determine that each targets option corresponds to the score of topic.
Optionally, it is used in the recommendation unit according to the score, when being user's recommended book information, comprising:
The score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option；
Determine the least targets option of score；
According to default recommendation rules, the book information of the corresponding specified quantity of the least targets option of score is recommended into institute
State user.
The technical solution that embodiments herein provides can include the following benefits:
It in this application, is to be recommended when for user's recommended book information according to the score for being the topic that user recommends,
It is able to reflect out the short slab in terms of the knowledge of user due to above-mentioned score, or is able to reflect out the knowledge that user requires supplementation with
Direction, thus according to it is above-mentioned be scored at user's recommended book information when, the matching of the demand of the book information and user of recommendation
Rate is relatively high so that user according to the book information of recommendation buy books after, the books and self-demand that buy
Matching rate it is also relatively high.
Detailed description of the invention
The attached drawing constituted part of this application is used to provide further understanding of the present application, so that the application's is other
Feature, objects and advantages become more apparent upon.The illustrative examples attached drawing and its explanation of the application is for explaining the application, not
Constitute the improper restriction to the application.In the accompanying drawings:
Fig. 1 is a kind of flow diagram of books recommended method provided by the present application；
Fig. 2 is the flow diagram of another books recommended method provided by the present application；
Fig. 3 is the flow diagram of another books recommended method provided by the present application；
Fig. 4 is the flow diagram of another data recommendation method provided by the present application；
Fig. 5 is the flow diagram of another books recommended method provided by the present application；
Fig. 6 is a kind of structural scheme of mechanism of books recommender system provided by the present application.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
The embodiment of the application a part, instead of all the embodiments.Based on the embodiment in the application, ordinary skill people
Member's every other embodiment obtained without making creative work, all should belong to the model of the application protection
It encloses.
It should be noted that the description and claims of this application and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to embodiments herein described herein.In addition, term " includes " and " tool
Have " and their any deformation, it is intended that cover it is non-exclusive include, for example, containing a series of steps or units
Process, method, system, product or equipment those of are not necessarily limited to be clearly listed step or unit, but may include without clear
Other step or units listing to Chu or intrinsic for these process, methods, product or equipment.
In this application, term " on ", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outside",
" in ", "vertical", "horizontal", " transverse direction ", the orientation or positional relationship of the instructions such as " longitudinal direction " be orientation based on the figure or
Positional relationship.These terms are not intended to limit indicated dress primarily to better describe the application and embodiment
Set, element or component must have particular orientation, or constructed and operated with particular orientation.
Also, above-mentioned part term is other than it can be used to indicate that orientation or positional relationship, it is also possible to for indicating it
His meaning, such as term " on " also are likely used for indicating certain relations of dependence or connection relationship in some cases.For ability
For the those of ordinary skill of domain, the concrete meaning of these terms in this application can be understood as the case may be.
In addition, term " installation ", " setting ", " being equipped with ", " connection ", " connected ", " socket " shall be understood in a broad sense.For example,
It may be a fixed connection, be detachably connected or monolithic construction；It can be mechanical connection, or electrical connection；It can be direct phase
It even, or indirectly connected through an intermediary, or is two connections internal between device, element or component.
For those of ordinary skills, the concrete meaning of above-mentioned term in this application can be understood as the case may be.
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.The application is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.
Fig. 1 be a kind of flow diagram of books recommended method provided by the present application, as shown in Figure 1, this method include with
Lower step:
101, it is retrieved as the score of the topic of user's recommendation.
Specifically, user, when buying books on books recommender system (such as shopping platform of books), books recommend system
System recommends a certain number of topics to user to understand the knowledge requirement of user again or in order to understand the knowledge short slab of user
Mesh can be set according to actual needs about specific topic, such as: be to user recommend financial sector topic or
Topic and topic in terms of user recommends history are related to depth etc. can be according to the feedback information of user or can basis
Recommended in terms of interest, the specific mode for recommending topic is not specifically limited herein.
It 102, is user's recommended book information according to the score.
Specifically, be to be recommended when for user's recommended book information according to the score for being the topic that user recommends, due to
Above-mentioned score is able to reflect out the short slab in terms of the knowledge of user, or is able to reflect out the side for the knowledge that user requires supplementation with
To, thus according to it is above-mentioned be scored at user's recommended book information when, the matching rate of the demand of the book information and user of recommendation
It is relatively high, so that user is after buying books according to the book information of recommendation, the books that buy and self-demand
Matching rate is also relatively high.
For example, can first determine the score section where the score after obtaining above-mentioned score, then acquisition is in advance
The book information of score section configuration, then the book information of acquisition is recommended into use, it should be noted that above-mentioned is only the application
A kind of example of the recommended book information proposed, is not defined the implementation of the application, in practical applications, can be with
Specific books recommendation rules are set as needed, specific books recommendation rules are not specifically limited herein, the principle of the application
It is that the topic that user recommends is scored at user's recommended book information according to being, therefore the with good grounds topic for user's recommendation of institute
The specific implementation for being scored at user's recommended book information should belong to the protection scope of the application.
In a feasible embodiment, Fig. 2 is the process signal of another books recommended method provided by the present application
Figure, as shown in Fig. 2, can specifically be realized by following steps when executing step 101:
201, the interest mark of user's selection is obtained.
Specifically, books recommender system can recommend interest to identify for user, user can select according to the demand of itself
Corresponding interest mark, so that books recommender system gets the interest mark of user's selection.
For example, interest mark can be the head portrait mark of the personage with social effectiveness, and different personages
Different fields can be corresponded to, for example, interest mark may include the head portrait mark of Ma Yun, the head portrait mark of Yi Zhongtian and Ba Fei
Special head portrait mark etc., wherein Ma Yun represents internet area, and Yi Zhongtian represents history field, and Ba Feite represents financial field,
User can select corresponding head portrait to identify according to itself interested field, can also be to certain on the basis of above-mentioned example
A field is finely divided, and by taking history field as an example, which can also head portrait mark (its known Qing Dynasty's age including Yan Chongnian
Historical knowledge), the head portrait of Yi Zhongtian mark (historical knowledge of its known the period of Three Kingdoms), (its is ripe for the head portrait mark of Yuan Tengfei
Know the historical knowledge in Song dynasty's period), it, can also be according to some personage institute with social effectiveness on the basis of above-mentioned example
Some well known historical personage or certain section of historical events are segmented again, and in this not go into detail.
On the basis of above-mentioned example, books recommender system can recommend multiple head portrait marks for user, and user can root
Corresponding head portrait mark is selected according to self-demand, by taking history field as an example, if user is desired general to history progress one
Solution can choose the head portrait mark of Yi Zhongtian in history field；If user wants to understand the period of Three Kingdoms, can choose
The head portrait of the period of Three Kingdoms corresponding Yi Zhongtian under history field in each period of history identifies；If user wants to some history
Personage or some historical events understand, then can choose and identify with head portrait known to the historical personage or the historical events.
It should be noted that above-mentioned is only the illustrative description provided, the application is not specifically limited, the application
Purpose in order to make interest mark indicate some field, perhaps indicate each subdomains under some field or indicate certain height
Different branches under field, so that user selects corresponding interest to identify according to self-demand, to make books recommender system
User recommends to need the higher book information of matching rate with it, and the specific pattern and interest mark about interest mark correspond to
Field or some field under subdomains again or some branch of some subdomains can be set according to actual needs,
It is not specifically limited herein.
202, the interest is identified into corresponding topic and is sent to the user.
Specifically, selecting topic identifying in preset topic for the interest after determining the interest mark of user's selection
It is sent to user, wherein preset topic quantity is identified for the interest and specific topic is not specifically limited herein, also,
The particular number of topic about selection can be set according to actual needs, be also not specifically limited herein.
203, the user is the answer and target answer that the topic provides, to obtain comparison result.
Specifically, user, after getting the topic recommended for it, user can select answer, example according to the requirement of topic
Such as, topic can be True-False, and user can carry out to misinterpretation the topic, alternatively, topic can be multiple-choice question, Yong Huke
Think that the topic selects candidate answers, it should be noted that it is above-mentioned to be merely exemplary description, not to the application's
Specific to limit, the concrete type about topic is not specifically limited herein.
After user chooses answer, just whether books recommender system need to judge answer that user selects for a certain topic
Really, judgment mode can be to be in advance that the topic sets target answer, can after obtaining the answer that user is topic selection
To select answer to be compared with the target answer and user set for the topic as the topic, if unanimously, it is determined that user
The answer selected for the topic is correct, if it is inconsistent, it is wrong that determining user, which is the answer that the topic selects,.
204, according to the comparison result, it is determined as the score for the topic that the user recommends.
For example, if the score value of the topic is 5 points, by taking True-False as an example, when the answer of user is correct, then really
Determine user and be scored at 5 points on the topic, when the answer of user is mistake, it is determined that user is scored on the topic
0 point, by taking multiple-choice question as an example, when the answer of user is correct, it is determined that user is scored at 5 points on the topic, works as user
Answer when being mistake, it is determined that user is scored at 0 point on the topic, or can be according to the answer and mesh that user selects
The degree of closeness of mark answer determines score of the user on the topic, then distribute score value in advance for each answer of the topic,
After user selects the answer of the topic, which answer determine user's selection is, then that the corresponding score value of the answer is true
Be set to the score of the topic, it should be noted that it is above-mentioned to be merely exemplary explanation, about the specific score value distributed for topic with
And the score mode and standard of a certain topic, it can be set, be not specifically limited herein according to actual needs.
In a feasible embodiment, Fig. 3 is the process signal of another books recommended method provided by the present application
Figure, as shown in figure 3, can be realized by following steps when executing step 202:
301, determine that the interest identifies at least one corresponding targets option.
Specifically, different interest marks can correspond to different number and different types of targets option, or can also be with
The targets option of corresponding identical quantity and identical type, wherein targets option can be linked up to enable power dimension, such as logical capability
Ability, understandability, analysis ability etc., and allocative abilities dimension can be identified for the interest according to the characteristics of different interest mark
Degree.
It should be noted that the value volume and range of product that interest identifies corresponding targets option can be set according to actual needs
It is fixed, it is not specifically limited herein.
302, according to allocation rule is preset, division arithmetic is carried out to the number of preset threshold and the targets option, with true
Determine the corresponding topic number of each targets option.
For example, being in advance interest flag preset threshold, the corresponding target choosing of some interest mark is being determined
Xiang Hou carries out division arithmetic to the number of the preset threshold and targets option, to determine each target choosing under interest mark
The number of the corresponding topic of item, for example, it includes first object option, the choosing of the second target that some interest, which identifies corresponding targets option,
Item and third targets option, it is 25 which, which identifies corresponding preset threshold, is determining each targets option according to the above method
After topic number, the corresponding topic number of first object option can be 8, and the corresponding topic number of the second targets option can be with
It is 8, the corresponding topic number of third targets option can be 9, if the corresponding preset threshold of interest mark is 24,
First object option, the second targets option and third targets option can respectively correspond 8 topics.
It should be noted that above-mentioned is only a kind of illustrative implementation provided by the present application, the application is not done
Specific to limit, the specific implementation about default allocation rule can be set according to actual needs, is not specifically limited herein.
It needing to be noted again that, different interest identifies corresponding preset threshold can be different, or can also be identical,
Interest identifies corresponding preset threshold and can be set according to actual needs, is not specifically limited herein.
303, each target is selected from the corresponding topic of each targets option according to the corresponding topic number of each targets option
Option corresponds to the topic of number, and the topic that each targets option corresponds to number is sent to user.
For example, a certain amount of topic is configured in advance for each targets option, for example, being in advance first object option configuration
20 topics configure 30 topics in advance for the second targets option, are in advance 20 topics of third option configuration, each determining
It is the topic of the corresponding number of selection in the topic of each targets option configuration in advance after the corresponding topic number of targets option, such as:
The corresponding topic number of first object option is 8, and the corresponding topic number of the second targets option is 8, third targets option
It is then 8 topics of selection in 20 topics of first object option configuration in advance, preparatory when corresponding topic number is 9
It is configured for the second targets option and selects 8 topics in 30 topics, be selection 9 in 20 topics of third option configuration in advance
Topic, it should be noted that multiple-choice question purpose mode can make to randomly select, or can also select according to certain rules
It takes, the mode for being specifically chosen topic is not specifically limited herein.
In a feasible embodiment, the mark of interest shown in Fig. 2 or Fig. 3 can be character recognition and label.
Specifically, character recognition and label may include: the image of the image identification of Ma Yun, the image identification of Yi Zhongtian and Ba Feite
Mark etc., wherein the image identification of Ma Yun represents internet area, and the image identification of Yi Zhongtian represents history field, Ba Feite
Image identification represent financial field, user can select corresponding image identification according to the demand of itself, then according to user
Corresponding book information is recommended user by the image identification of selection, about the specific recommended method of book information, is had above-mentioned
It is described in detail, in this not go into detail.
In a feasible embodiment, Fig. 4 is the process signal of another data recommendation method provided by the present application
Figure, as shown in figure 4, can be realized by following steps when executing step 204:
401, the comparison result of the corresponding topic of each targets option is determined.
402, according to the comparison result of the corresponding topic of each targets option with for the corresponding topic distribution of each targets option
Score value determines that each targets option corresponds to the score of topic.
For example, if targets option includes first object option and the second targets option, wherein first object option
Corresponding topic includes the first topic and the second topic, and the corresponding topic of the second targets option includes third topic, the 4th topic
The first topic and the second topic are determined after obtaining user and being the answer that the first topic is provided to the 5th topic with the 5th topic
Corresponding answer, and third topic is determined to the corresponding answer of the 5th topic, then in the case where determining first object option
First topic and the corresponding score of the second topic, and determine that the third topic under the second targets option is corresponding to the 5th topic
Score, to determine the corresponding scoring event of each targets option.
It is described in detail, does not do herein specific above-mentioned about for topic distribution score value and mode and the score mode of topic
It limits.
In a feasible embodiment, Fig. 5 is the flow diagram of another books recommended method provided by the present application,
As shown in figure 5, can be realized by following steps when executing step 101:
501, the score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option.
502, the least targets option of score is determined.
503, according to recommendation rules are preset, the book information of the corresponding specified quantity of the least targets option of score is recommended
To the user.
Specifically, can determine each target after determining the score of the corresponding topic of each targets option and the topic
The total score of option, the least targets option of score can show that user requires supplementation with the direction of knowledge or can show that user
The short slab of knowledge, therefore the book information under the least targets option of score can be recommended into user, also, in recommended book
When can recommend according to preset specified quantity, wherein when recommending the books of specified quantity to user, can be pushed away according to default
Regular recommendation is recommended, for example, the least targets option of score is first object option, and is in advance the number of first object option configuration
It is believed that breath includes 20, if specified quantity is 10,10 books can be selected at random in above-mentioned 20 books and recommended
To user, or according to the sales volume of above-mentioned 20 books, the books that sales volume comes preceding 10 are recommended into user, above-mentioned is only example
The preset recommendation rules of explanation of property, naturally it is also possible to which the recommendation rules including other ways of recommendation, specific recommendation rules can
To be set according to the actual situation, it is not specifically limited herein.
Fig. 6 is a kind of structural scheme of mechanism of books recommender system provided by the present application, as shown in fig. 6, the books recommend system
System includes:
Acquiring unit 61, the score of the topic for being retrieved as user's recommendation；
Recommendation unit 62, for being user's recommended book information according to the score.
In a feasible embodiment, it is used to be retrieved as the score of the topic of user's recommendation in the acquiring unit 61
When, it is specifically used for:
Obtain the interest mark of user's selection；
The interest is identified into corresponding topic and is sent to the user；
Comparing the user is the answer and target answer that the topic provides, to obtain comparison result；
According to the comparison result, it is determined as the score for the topic that the user recommends.
In a feasible embodiment, it is used to the interest identifying corresponding topic in the acquiring unit 61 and sends out
When giving the user, it is specifically used for:
Determine that the interest identifies at least one corresponding targets option；
According to default allocation rule, division arithmetic is carried out to the number of preset threshold and the targets option, it is each to determine
The corresponding topic number of targets option；
Each targets option is selected from the corresponding topic of each targets option according to the corresponding topic number of each targets option
The topic of corresponding number, is sent to user for the topic that each targets option corresponds to number.
In a feasible embodiment, the interest mark includes character recognition and label.
In a feasible embodiment, it is used to be determined as institute according to the comparison result in the acquiring unit 61
When stating the score of the topic of user's recommendation, it is specifically used for:
Determine the comparison result of the corresponding topic of each targets option；
According to the comparison result of the corresponding topic of each targets option and the score value for the corresponding topic distribution of each targets option,
Determine that each targets option corresponds to the score of topic.
In a feasible embodiment, it is used in the recommendation unit 62 according to the score, is user's recommendation
When nationality information, comprising:
The score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option；
Determine the least targets option of score；
According to default recommendation rules, the book information of the corresponding specified quantity of the least targets option of score is recommended into institute
State user.
About the system in above-described embodiment, wherein each unit executes the concrete mode of operation in related this method
Embodiment in be described in detail, no detailed explanation will be given here.
It in this application, is to be recommended when for user's recommended book information according to the score for being the topic that user recommends,
It is able to reflect out the short slab in terms of the knowledge of user due to above-mentioned score, or is able to reflect out the knowledge that user requires supplementation with
Direction, thus according to it is above-mentioned be scored at user's recommended book information when, the matching of the demand of the book information and user of recommendation
Rate is relatively high so that user according to the book information of recommendation buy books after, the books and self-demand that buy
Matching rate it is also relatively high.
The foregoing is merely preferred embodiment of the present application, are not intended to limit this application, for the skill of this field
For art personnel, various changes and changes are possible in this application.Within the spirit and principles of this application, made any to repair
Change, equivalent replacement, improvement etc., should be included within the scope of protection of this application.
Claims (12)
1. a kind of books recommended method, which is characterized in that the described method includes:
It is retrieved as the score of the topic of user's recommendation；
It is user's recommended book information according to the score.
2. the method as described in claim 1, which is characterized in that the score of the topic for being retrieved as user's recommendation, comprising:
Obtain the interest mark of user's selection；
The interest is identified into corresponding topic and is sent to the user；
Comparing the user is the answer and target answer that the topic provides, to obtain comparison result；
According to the comparison result, it is determined as the score for the topic that the user recommends.
3. method according to claim 2, which is characterized in that it is described by the interest identify corresponding topic be sent to it is described
User, comprising:
Determine that the interest identifies at least one corresponding targets option；
According to default allocation rule, division arithmetic is carried out to the number of preset threshold and the targets option, with each target of determination
The corresponding topic number of option；
According to the corresponding topic number of each targets option, from the corresponding topic of each targets option, select each targets option corresponding
The topic that each targets option corresponds to number is sent to user by the topic of number.
4. method as claimed in claim 2 or claim 3, which is characterized in that the interest mark includes character recognition and label.
5. method as claimed in claim 3, which is characterized in that it is described according to the comparison result, it is determined as the user and pushes away
The score for the topic recommended, comprising:
Determine the comparison result of the corresponding topic of each targets option；
According to the comparison result of the corresponding topic of each targets option and the score value for the corresponding topic distribution of each targets option, determine
Each targets option corresponds to the score of topic.
6. method as claimed in claim 5, which is characterized in that it is described according to the score, it is user's recommended book information, packet
It includes:
The score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option；
Determine the least targets option of score；
According to default recommendation rules, the book information of the corresponding specified quantity of the least targets option of score is recommended into the use
Family.
7. a kind of books recommender system, which is characterized in that the books recommender system includes:
Acquiring unit, the score of the topic for being retrieved as user's recommendation；
Recommendation unit, for being user's recommended book information according to the score.
8. books recommender system as claimed in claim 7, which is characterized in that pushed away in the acquiring unit for being retrieved as user
When the score for the topic recommended, it is specifically used for:
Obtain the interest mark of user's selection；
The interest is identified into corresponding topic and is sent to the user；
Comparing the user is the answer and target answer that the topic provides, to obtain comparison result；
According to the comparison result, it is determined as the score for the topic that the user recommends.
9. books recommender system as claimed in claim 8, which is characterized in that be used in the acquiring unit by the interest mark
When knowing corresponding topic and being sent to the user, it is specifically used for:
Determine that the interest identifies at least one corresponding targets option；
According to default allocation rule, division arithmetic is carried out to the number of preset threshold and the targets option, with each target of determination
The corresponding topic number of option；
According to the corresponding topic number of each targets option, from the corresponding topic of each targets option, select each targets option corresponding
The topic that each targets option corresponds to number is sent to user by the topic of number.
10. books recommender system as claimed in claim 8 or 9, which is characterized in that the interest mark includes character recognition and label.
11. books recommender system as claimed in claim 9, which is characterized in that be used in the acquiring unit according to the ratio
Compared with as a result, being specifically used for when being determined as the score for the topic that the user recommends:
Determine the comparison result of the corresponding topic of each targets option；
According to the comparison result of the corresponding topic of each targets option and the score value for the corresponding topic distribution of each targets option, determine
Each targets option corresponds to the score of topic.
12. books recommender system as claimed in claim 11, which is characterized in that be used to be obtained according to described in the recommendation unit
Point, when being user's recommended book information, comprising:
The score for corresponding to topic to each targets option carries out add operation, obtains the corresponding score of each targets option；
Determine the least targets option of score；
According to default recommendation rules, the book information of the corresponding specified quantity of the least targets option of score is recommended into the use
Family.
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| CN113987353A (en) * | 2021-10-29 | 2022-01-28 | 掌阅科技股份有限公司 | Book recommendation method, computing device and storage medium |
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