CN108038237A - A kind of information recommendation method and system - Google Patents
A kind of information recommendation method and system Download PDFInfo
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- CN108038237A CN108038237A CN201711447182.7A CN201711447182A CN108038237A CN 108038237 A CN108038237 A CN 108038237A CN 201711447182 A CN201711447182 A CN 201711447182A CN 108038237 A CN108038237 A CN 108038237A
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- 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
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Abstract
The invention discloses a kind of information recommendation method and system, described information recommends method to include：Obtain the build-in attribute and behavior property of user；Statistical analysis processing is carried out to the build-in attribute and the behavior property, excavates user interest；Analysis mining processing is carried out to the behavior property, excavates the reading habit and consumption idea of user；According to the build-in attribute, the behavior property, the user interest, the reading habit and the consumption idea, the user's portrait and behavioural habits of user are excavated；According to the user portrait behavioural habits, default information recommendation model is called to obtain recommendation information, and user is pushed to by described.Recommend method to excavate a variety of preference informations of user by described information, the variation of recommendation information is realized, to meet the extensive reading demands of user.
Description
Technical field
The present invention relates to information recommendation field, and in particular to a kind of information recommendation method and system.
Background technology
With the rapid development of network technology, substantial amounts of information, information are produced daily on the internet, therefore, how to allow
User is quickly found out from the data of magnanimity oneself to be wanted or is adapted to the network information of oneself then to compel to be essential as technical staff
Technical problems to be solved.At present, existing information recommendation system is mainly recommended, certainly using collaborative filtering recommending, correlation rule
The method that plan tree is recommended, such as a kind of information recommendation method based on decision tree include：Behavior based on user obtains user's
Information browse records series, wherein, each information browse record that described information is browsed in records series includes the following：With
The attribute and browsing time that browsing objective is associated；Records series generation decision tree is browsed based on the described information received,
Wherein, the attribute of the different levels in attribute associated with browsing objective described in each node on behalf in the decision tree
Classification, and the first order classification of the root nodes stand the superiors of the decision tree；Based on described after the decision tree generates
The browsing time in each information browse record in information browse records series is the section associated with information browse record
Point is assigned to weighted value；Determine to recommend target based on the decision tree after weighting.But existing information recommendation method substantially uses
The algorithm of single-mode is according to the single point of interest search network information, so as to cause recommendation information more single, it is impossible to meet
The requirement read extensively of user.
The content of the invention
The object of the present invention is to provide a kind of information recommendation method and system, a variety of preference informations of user can be excavated,
The variation of recommendation information is realized, to meet the extensive reading demands of user.
To avoid above technical problem, the embodiment of the present invention provides a kind of information recommendation method, including：
Obtain the build-in attribute and behavior property of user；
Statistical analysis processing is carried out to the build-in attribute and the behavior property, excavates user interest；
Analysis mining processing is carried out to the behavior property, excavates the reading habit and consumption idea of user；
According to the build-in attribute, the behavior property, the user interest, the reading habit and the consumption view
Read, excavate the user's portrait and behavioural habits of user；
According to user portrait and the behavioural habits, default information recommendation model is called to obtain recommendation information, and
The recommendation information is pushed to user.
Preferably, it is described according to user portrait and the behavioural habits, call default information recommendation model to obtain
Recommendation information, and before the recommendation information is pushed to user, further include：
According to user portrait and the behavior property, the training default information recommendation model.
Preferably, described information recommends method to further include：
The build-in attribute and the behavior property are subjected to dividing processing and form multiple data blocks；
By the multiple data block distributed storage into default server cluster.
Preferably, it is described according to user portrait and the behavioural habits, call default information recommendation model to obtain
Recommendation information, and the recommendation information is pushed to user, specifically include：
According to the weight of each label in user portrait and user portrait, the default information is called to push away
Recommend model and the recommendation information is searched out from the Internet resources of website；
According to the behavioural habits, the weight of the behavioural habits and the recommendation information, the default letter is called
Cease recommended models generation recommendation information list；
According to the recommendation information list, the recommendation information is pushed to user.
Preferably, the build-in attribute and behavior property for obtaining user, specifically includes：
Gather the log-on message and user behaviors log of user at set time intervals, and respectively to the log-on message and
The user behaviors log carries out data prediction to obtain the build-in attribute and the behavior property.
Preferably, it is described that statistical analysis processing is carried out to the build-in attribute and the behavior property, user interest is excavated,
Specifically include：
The build-in attribute and the behavior property are carried out according to default interest analysis model textual association analysis and
Word frequency statistics processing, excavates the user interest.
Preferably, the reading habit and consumption view that analysis mining processing is carried out to the behavior property, excavates user
Read, specifically include：
The behavior property includes the period information that user reads, the arrangement for reading information used, the network information and reads
The text message of reading；
It is accustomed to period information, the arrangement for reading letter used that analysis model reads the user according to default
Breath, the network information carry out analysis mining processing, excavate the reading habit of user；
Analysis mining processing is carried out to the text message of the reading according to the default custom analysis model, excavates and uses
The consumption idea at family.
The embodiment of the present invention also provides a kind of information recommendation system, including：
Data obtaining module, for obtaining the build-in attribute and behavior property of user；
Interest digging module, for carrying out statistical analysis processing to the build-in attribute and the behavior property, excavates and uses
Family interest；
Custom excavate module, for the behavior property carry out analysis mining processing, excavate user reading habit and
Consumption idea；
Portrait excavates module, for according to the build-in attribute, the behavior property, the user interest, the reading
Custom and the consumption idea, excavate the user's portrait and behavioural habits of user；
Information recommendation module, for according to user portrait and the behavioural habits, calling default information recommendation mould
Type obtains recommendation information, and the recommendation information is pushed to user.
Preferably, described information commending system further includes training module, for according to user portrait and the behavior
Attribute, the training default information recommendation model.
Relative to the prior art, a kind of beneficial effect of information recommendation method provided in an embodiment of the present invention is：It is described
Information recommendation method includes：Obtain the build-in attribute and behavior property of user；To the build-in attribute and the behavior property into
Row statistical analysis is handled, and excavates user interest；Analysis mining processing is carried out to the behavior property, excavates the reading habit of user
And consumption idea；According to the build-in attribute, the behavior property, the user interest, the reading habit and described disappear
Take idea, excavate the user's portrait and behavioural habits of user；According to user portrait and the behavioural habits, call default
Information recommendation model obtains recommendation information, and the recommendation information is pushed to user.Recommend method can by described information
A variety of preference informations of user are excavated, the variation of recommendation information are realized, to meet the extensive reading demands of user.It is of the invention real
Apply example and a kind of information recommendation system is provided.
Brief description of the drawings
Fig. 1 is a kind of flow chart of information recommendation method provided in an embodiment of the present invention；
Fig. 2 is a kind of schematic diagram of information recommendation system provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without creative efforts
Embodiment, belongs to the scope of protection of the invention.
Referring to Fig. 1, it is a kind of flow chart of information recommendation method provided in an embodiment of the present invention, described information is recommended
Method includes：
S1：Obtain the build-in attribute and behavior property of user；
S2：Statistical analysis processing is carried out to the build-in attribute and the behavior property, excavates user interest；
S3：Analysis mining processing is carried out to the behavior property, excavates the reading habit and consumption idea of user；
S4：According to the build-in attribute, the behavior property, the user interest, the reading habit and described disappear
Take idea, excavate the user's portrait and behavioural habits of user；
S5：According to user portrait and the behavioural habits, default information recommendation model is called to obtain recommendation information,
And the recommendation information is pushed to user.
Method depth is recommended to excavate a variety of preference informations of user by described information, for example, it is the build-in attribute, described
Behavior property, the user interest, the reading habit and the consumption idea, so as to fulfill the variation of recommendation information,
To meet the extensive reading demands of user.Such as according to user portrait and the behavioural habits, depth analysis user's is more
Kind point of interest, dislike interesting point, Below-the-line type etc., recommends except news, further includes life information (air ticket, stroke, preferential popularization
Deng), action message (concert, lecture, propaganda activity etc.), so as to fulfill the variation of recommendation information.
It is described according to user portrait and the behavioural habits in a kind of optional embodiment, call default letter
Cease recommended models and obtain recommendation information, and before the recommendation information is pushed to user, further include：
According to user portrait and the behavior property, the training default information recommendation model.
In the present embodiment, due to the user portrait and the behavior property be according to collection information time constantly more
New, the default information recommendation model adjusts the ginseng of itself according to the user of renewal portrait and the behavior property
Number, realizes the self-learning optimization of the default information model, can to push away by what default information recommendation model obtained
The preference that information more meets user is recommended, put forward the accuracy of information recommendation.
In a kind of optional embodiment, described information recommends method to further include：
The build-in attribute and the behavior property are subjected to dividing processing and form multiple data blocks；
By the multiple data block distributed storage into default server cluster.
In the present embodiment, the build-in attribute and the behavior property will be divided into multiple size datas specified
Block；The default server cluster includes multiple servers, and any one of data block is stored in any one of clothes
It is engaged in the tables of data of device.By the way that the build-in attribute and behavior property distribution are stored in different servers, can drop
The requirement of the memory capacity of low server, reduces server price and maintenance cost, reduces the requirement to building environment.
It is described according to user portrait and the behavioural habits in a kind of optional embodiment, call default letter
Cease recommended models and obtain recommendation information, and the recommendation information is pushed to user, specifically include：
According to the weight of each label in user portrait and user portrait, the default information is called to push away
Recommend model and the recommendation information is searched out from the Internet resources of website；
According to the behavioural habits, the weight of the behavioural habits and the recommendation information, the default letter is called
Cease recommended models generation recommendation information list；
According to the recommendation information list, the recommendation information is pushed to user.
In this embodiment, for example, the label of user portrait includes scientific and technological fan, its weight is 90%；Male, its
Weight is 85%；After 90, its weight is 80%；……；The label and its weight then drawn a portrait according to the user, from website
Related popular article, life information, action message are retrieved in Internet resources.Read according to the behavioural habits including noon,
Its weight is 70%；Client is read, its weight for 95% ...；To the article, life information, action message being retrieved
Recommended according to the behavioural habits.
In a kind of optional embodiment, the build-in attribute and behavior property for obtaining user, specifically includes：
The log-on message and user behaviors log of user is gathered at set time intervals；
Data prediction is carried out to the log-on message and the user behaviors log to obtain the build-in attribute and institute respectively
State behavior property.
In the present embodiment, thick division, user's row can be carried out to user group according to the user's registration information
The reading information for including user for attribute (reads article's style, reading time section, reads duration, read the different grain size of article
Label etc.), interactive information (is commented on, thumbed up, collecting), and the user is excavated by analyzing the reading information and interactive information
Portrait and the behavioural habits, can enrich the label of user's portrait and the behavioural habits, realize user preference information
Depth excavate.
In the present embodiment, the data prediction include data cleansing, data integration, data conversion, data regularization with
And Data Discretization.By being converted to the log-on message and user behaviors log progress data cleansing, data integration, data,
Data regularization and Data Discretization processing obtain suitable attribute as number from the log-on message and the user behaviors log
According to the foundation of excavation.
It is described that the build-in attribute and the behavior property are carried out at statistical analysis in a kind of optional embodiment
Reason, excavates user interest, specifically includes：
The build-in attribute and the behavior property are carried out according to default interest analysis model textual association analysis and
Word frequency statistics processing, excavates the user interest.
It is described that analysis mining processing is carried out to the behavior property in a kind of optional embodiment, excavate readding for user
Custom and consumption idea are read, is specifically included：
The behavior property includes the period information that user reads, the arrangement for reading information used, the network information and reads
The text message of reading；
It is accustomed to period information, the arrangement for reading letter used that analysis model reads the user according to default
Breath, the network information carry out analysis mining processing, excavate the reading habit of user；
Analysis mining processing is carried out to the text message of the reading according to the default custom analysis model, excavates and uses
The consumption idea at family.
Referring to Fig. 2, it is a kind of schematic diagram of information recommendation system provided in an embodiment of the present invention, described information is recommended
System, including：
Data obtaining module 1, for obtaining the build-in attribute and behavior property of user；
Interest digging module 2, for carrying out statistical analysis processing to the build-in attribute and the behavior property, excavates and uses
Family interest；
Custom excavate module 3, for the behavior property carry out analysis mining processing, excavate user reading habit and
Consumption idea；
Portrait excavates module 4, for according to the build-in attribute, the behavior property, the user interest, the reading
Custom and the consumption idea, excavate the user's portrait and behavioural habits of user；
Information recommendation module 5, for according to user portrait and the behavioural habits, calling default information recommendation mould
Type obtains recommendation information, and the recommendation information is pushed to user.
A variety of preference informations of user are excavated by described information commending system depth, for example, it is the build-in attribute, described
Behavior property, the user interest, the reading habit and the consumption idea, so as to fulfill the variation of recommendation information,
To meet the extensive reading demands of user.Such as according to user portrait and the behavioural habits, depth analysis user's is more
Kind point of interest, dislike interesting point, Below-the-line type etc., recommends except news, further includes life information (air ticket, stroke, preferential popularization
Deng), action message (concert, lecture, propaganda activity etc.), realize the variation of recommendation information.
In a kind of optional embodiment, described information commending system further includes training module, for according to the user
Portrait and the behavior property, the training default information recommendation model.
In the present embodiment, due to the user portrait and the behavior property be according to collection information time constantly more
New, the default information recommendation model adjusts the ginseng of itself according to the user of renewal portrait and the behavior property
Number, realizes the self-learning optimization of the default information model, can to push away by what default information recommendation model obtained
The preference that information more meets user is recommended, put forward the accuracy of information recommendation.
In a kind of optional embodiment, described information commending system further includes：
Data segmentation module, multiple data are formed for the build-in attribute and the behavior property to be carried out dividing processing
Block；
Data memory module, is same as the multiple data block distributed storage into default server cluster.
In the present embodiment, the build-in attribute and the behavior property will be divided into multiple size datas specified
Block；The default server cluster includes multiple servers, and any one of data block is stored in any one of clothes
It is engaged in the tables of data of device.By the way that the build-in attribute and behavior property distribution are stored in different servers, can drop
The requirement of the memory capacity of low server, reduces server price and maintenance cost, reduces the requirement to building environment.
In a kind of optional embodiment, described information recommending module includes：
Recommendation information acquisition module, for the power according to each label in user portrait and user portrait
Weight, calls the default information recommendation model to search out the recommendation information from the Internet resources of website；
Recommendation information List Generating Module, for according to the behavioural habits, the weight of the behavioural habits and described
Recommendation information, calls the default information recommendation model generation recommendation information list；
Recommendation information pushing module, for according to the recommendation information list, the recommendation information to be pushed to user.
In this embodiment, for example, the label of user portrait includes scientific and technological fan, its weight is 90%；Male, its
Weight is 85%；After 90, its weight is 80%；……；The label and its weight then drawn a portrait according to the user, from website
Related popular article, life information, action message are retrieved in Internet resources.Read according to the behavioural habits including noon,
Its weight is 70%；Client is read, its weight for 95% ...；To the article, life information, action message being retrieved
Recommended according to the behavioural habits.
In a kind of optional embodiment, described information acquisition module includes：
Raw data acquisition module, for gathering the log-on message and user behaviors log of user at set time intervals；
Attribute data generation module, for the log-on message and the user behaviors log are carried out respectively data prediction with
Obtain the build-in attribute and the behavior property.
In the present embodiment, thick division, user's row can be carried out to user group according to the user's registration information
The reading information for including user for attribute (reads article's style, reading time section, reads duration, read the different grain size of article
Label etc.), interactive information (is commented on, thumbed up, collecting), and the user is excavated by analyzing the reading information and interactive information
Portrait and the behavioural habits, can enrich the label of user's portrait and the behavioural habits, realize user preference information
Depth excavate.
In the present embodiment, the data prediction include data cleansing, data integration, data conversion, data regularization with
And Data Discretization.By being converted to the log-on message and user behaviors log progress data cleansing, data integration, data,
Data regularization and Data Discretization processing obtain suitable attribute as number from the log-on message and the user behaviors log
According to the foundation of excavation.
In a kind of optional embodiment, the interest digging module is used for according to default interest analysis model to described
Build-in attribute and the behavior property carry out textual association analysis and word frequency statistics processing, excavate the user interest.
In a kind of optional embodiment, the custom, which excavates module, includes reading habit excavation module and consumption idea
Excavate module；
The behavior property includes the period information that user reads, the arrangement for reading information used, the network information and reads
The text message of reading；
The reading habit excavates module, and the period for being read according to default custom analysis model to the user believes
Breath, the arrangement for reading information used, the network information carry out analysis mining processing, excavate the reading habit of user；
The consumption idea excavates module, for the text envelope according to the default custom analysis model to the reading
Breath carries out analysis mining processing, excavates the consumption idea of user.
Relative to the prior art, a kind of beneficial effect of information recommendation method provided in an embodiment of the present invention is：It is described
Information recommendation method includes：Obtain the build-in attribute and behavior property of user；To the build-in attribute and the behavior property into
Row statistical analysis is handled, and excavates user interest；Analysis mining processing is carried out to the behavior property, excavates the reading habit of user
And consumption idea；According to the build-in attribute, the behavior property, the user interest, the reading habit and described disappear
Take idea, excavate the user's portrait and behavioural habits of user；According to user portrait and the behavioural habits, call default
Information recommendation model obtains recommendation information, and the recommendation information is pushed to user.Recommend method can by described information
A variety of preference informations of user are excavated, the variation of recommendation information are realized, to meet the extensive reading demands of user.It is of the invention real
Apply example and a kind of information recommendation system is provided.
Above is the preferred embodiment of the present invention, it is noted that for those skilled in the art,
Various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as this hair
Bright protection domain.
Claims (9)
- A kind of 1. information recommendation method, it is characterised in that including：Obtain the build-in attribute and behavior property of user；Statistical analysis processing is carried out to the build-in attribute and the behavior property, excavates user interest；Analysis mining processing is carried out to the behavior property, excavates the reading habit and consumption idea of user；According to the build-in attribute, the behavior property, the user interest, the reading habit and the consumption idea, Excavate the user's portrait and behavioural habits of user；According to user portrait and the behavioural habits, default information recommendation model is called to obtain recommendation information, and by institute State recommendation information and be pushed to user.
- 2. information recommendation method as claimed in claim 1, it is characterised in that described according to user portrait and the behavior Custom, calls default information recommendation model to obtain recommendation information, and before the recommendation information is pushed to user, also wraps Include：According to user portrait and the behavior property, the training default information recommendation model.
- 3. information recommendation method as claimed in claim 1, it is characterised in that further include：The build-in attribute and the behavior property are subjected to dividing processing and form multiple data blocks；By the multiple data block distributed storage into default server cluster.
- 4. information recommendation method as claimed in claim 1, it is characterised in that described according to user portrait and the behavior Custom, calls default information recommendation model to obtain recommendation information, and the recommendation information is pushed to user, specifically includes：According to the weight of each label in user portrait and user portrait, the default information recommendation mould is called Type searches out the recommendation information from the Internet resources of website；According to the behavioural habits, the weight of the behavioural habits and the recommendation information, the default information is called to push away Recommend model generation recommendation information list；According to the recommendation information list, the recommendation information is pushed to user.
- 5. information recommendation method as claimed in claim 1, it is characterised in that the build-in attribute and behavior category for obtaining user Property, specifically include：The log-on message and user behaviors log of user is gathered at set time intervals；Data prediction is carried out to the log-on message and the user behaviors log to obtain the build-in attribute and the row respectively For attribute.
- 6. information recommendation method as claimed in claim 1, it is characterised in that described to the build-in attribute and the behavior category Property carry out statistical analysis processing, excavate user interest, specifically include：Textual association analysis and word frequency are carried out to the build-in attribute and the behavior property according to default interest analysis model Statistical disposition, excavates the user interest.
- 7. information recommendation method as claimed in claim 1, it is characterised in that described that analysis mining is carried out to the behavior property Processing, excavates the reading habit and consumption idea of user, specifically includes：The behavior property includes the period information that user reads, the arrangement for reading information, the network information and the reading that use Text message；According to it is default custom analysis model the user is read period information, the arrangement for reading information used, institute State the network information and carry out analysis mining processing, excavate the reading habit of user；Analysis mining processing is carried out to the text message of the reading according to the default custom analysis model, excavates user's Consumption idea.
- A kind of 8. information recommendation system, it is characterised in that including：Data obtaining module, for obtaining the build-in attribute and behavior property of user；Interest digging module, for carrying out statistical analysis processing to the build-in attribute and the behavior property, it is emerging to excavate user Interest；Custom excavates module, for carrying out analysis mining processing to the behavior property, excavates reading habit and the consumption of user Idea；Portrait excavates module, for according to the build-in attribute, the behavior property, the user interest, the reading habit And the consumption idea, excavate user user portrait and behavioural habits；Information recommendation module, for according to user portrait and the behavioural habits, calling default information recommendation model to obtain Recommendation information is taken, and the recommendation information is pushed to user.
- 9. information recommendation system as claimed in claim 8, it is characterised in that described information commending system further includes trained mould Block, for according to user portrait and the behavior property, the training default information recommendation model.
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| CN109145210A (en) * | 2018-08-17 | 2019-01-04 | 腾讯科技（深圳）有限公司 | A kind of method, apparatus and storage medium of information recommendation |
| CN109254990A (en) * | 2018-09-11 | 2019-01-22 | 北京唐冠天朗科技开发有限公司 | A kind of method and system of information source acquisition and dynamic analysis |
| CN109284448A (en) * | 2018-09-28 | 2019-01-29 | Oppo广东移动通信有限公司 | Information recommendation method, device, terminal and storage medium |
| CN109318902A (en) * | 2018-09-27 | 2019-02-12 | 上海蔚来汽车有限公司 | User's operation bootstrap technique, system and controller based on user's operation behavior |
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| CN109977301A (en) * | 2019-02-28 | 2019-07-05 | 贵阳学院 | A kind of user's use habit method for digging |
| CN110059177A (en) * | 2019-04-24 | 2019-07-26 | 南京传唱软件科技有限公司 | A kind of activity recommendation method and device based on user's portrait |
| CN110188276A (en) * | 2019-05-31 | 2019-08-30 | 秒针信息技术有限公司 | Data sending device, method, electronic equipment and computer readable storage medium |
| WO2020082596A1 (en) * | 2018-10-23 | 2020-04-30 | 深圳壹账通智能科技有限公司 | Data processing-based automatic user profile generating method and system |
| CN112653908A (en) * | 2020-12-21 | 2021-04-13 | 四川长虹电器股份有限公司 | Intelligent television media asset real-time recommendation method |
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