CN104391999A - Information recommendation method and device - Google Patents
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Abstract
The invention discloses an information recommendation method and an information recommendation device. The information recommendation method comprises the following steps: obtaining the current information clicked by a user, searching correlated category according to the category to which the current clicked information belongs, searching hot spot information from the information in the correlated category, and recommending the hot spot information to the user. With application of the information recommendation method and the information recommendation device, the problem that in the prior art, the information recommended to the user is inaccurate can be solved, and the effect of accurately recommending personalized information to the user can be achieved.
Description
Technical field
The present invention relates to internet arena, in particular to a kind of information recommendation method and device.
Background technology
The utilization of large data is megatrend for internet development.Current, the user data of magnanimity can be collected in website, and the access that increasing portal website selects commending system to improve user is experienced.Commending system according to the access history of user or other user data, can do personalized recommendation for each user.The news portal website of present most domestic or traditional fixed mode: homepage is divided into fixing recommendation plate, and each column has different news links, the page that all users see is all the same.But the actual interest of user is different, recommends the method for identical information can not the interest of accurate match user to the user of different interest like this, be not the recommendation done according to the interest of user, cannot reach and improve the effect that user accesses experience.
For in prior art to the inaccurate problem of user's recommendation information, at present effective solution is not yet proposed.
Summary of the invention
Fundamental purpose of the present invention is to provide a kind of information recommendation method and device, to solve in prior art to the inaccurate problem of user's recommendation information.
To achieve these goals, according to an aspect of the embodiment of the present invention, a kind of information recommendation method is provided.Information recommendation method according to the present invention comprises: the current click information obtaining user; Classification belonging to described current click information search be associated with described classification associate classification; Hot information is searched from other information of described association class; And recommend described hot information to described user.
Further, from other information of described association class, search hot information to comprise: obtain the time of described other information of association class and the click volume of described other information of association class; Obtain the weight information of described time and described click volume; The score value of described other information of association class is calculated according to described time, described click volume and described weight information; And using information the highest for described score value as described hot information.
Further, the classification belonging to described current click information search be associated with described classification associate classification before, described method also comprises: obtain in website the user click data recorded; According to the information category of user click data described in pre-defined rule identification; The information category identified is added in described user click data, obtains new click data; According to the incidence relation in described new click data, the described information category identified is associated, obtain information category contingency table.
Further, the classification belonging to described current click information is searched the classification that associates be associated with described classification and is comprised: resolve described current click information and determine the information category belonging to described current click information; The information category associated with the information category belonging to described current click information is searched from described information category contingency table.
Further, from described user click data, parse the user click data with incidence relation to comprise: obtain the multiple click datas in described user click data; Search the data recording adopting consecutive click chemical reaction in described multiple click data; And using the data of described adopting consecutive click chemical reaction as the described user click data with incidence relation.
To achieve these goals, according to the another aspect of the embodiment of the present invention, provide a kind of information recommending apparatus.Information recommending apparatus according to the present invention comprises: the first acquiring unit, for obtaining the current click information of user; First searches unit, for the classification belonging to described current click information search be associated with described classification associate classification; Second searches unit, for finding hot information from other information of described association class; And recommendation unit, for recommending described hot information to described user.
Further, described second searches unit comprises: the first acquisition module, for the click volume of time and described other information of association class of obtaining described other information of association class; Second acquisition module, for obtaining the weight information of described time and described click volume; Computing module, for calculating the score value of described other information of association class according to described time, described click volume and described weight information; And first determination module, for using information the highest for described score value as described hot information.
Further, described device also comprises: second acquisition unit, for search in the classification belonging to described current click information be associated with described classification associate classification before, obtain in website the user click data recorded; Recognition unit, for the information category according to user click data described in pre-defined rule identification; Adding device, for being added in described user click data by the information category identified, obtains new click data; Associative cell, for associating the described information category identified according to the incidence relation in described new click data, obtains information category contingency table.
Further, described first searches unit comprises: parsing module, for resolving described current click information and determining the information category belonging to described current click information; First searches module, for searching the information category associated with the information category belonging to described current click information from described information category contingency table.
Further, described associative cell comprises: the 3rd acquisition module, for obtaining the multiple click datas in described new click data; Second searches module, for searching the data recording adopting consecutive click chemical reaction in described multiple click data; And second determination module, for using the data of described adopting consecutive click chemical reaction as the described user click data with incidence relation.
According to inventive embodiments, the interested information of user is gone out according to the click behavioural analysis of user, and the hot information searched in the interested category information of user, and hot information is recommended user, the information recommending user is made to be the interested information of user, it is the recommendation information of the personalization for each user, instead of the fix information all identical to all users, thus to solve in prior art to the inaccurate problem of user's recommendation information, reach the effect of accurately recommending customized information to user.
Accompanying drawing explanation
The accompanying drawing forming a application's part is used to provide a further understanding of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the process flow diagram of the information recommendation method according to the embodiment of the present invention;
Fig. 2 is the process flow diagram of information recommendation method according to the preferred embodiment of the invention; And
Fig. 3 is the schematic diagram of the information recommending apparatus according to the embodiment of the present invention.
Embodiment
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the present invention in detail in conjunction with the embodiments.
The present invention program is understood better in order to make those skilled in the art person, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the embodiment of a part of the present invention, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, should belong to the scope of protection of the invention.
It should be noted that, term " first ", " second " etc. in instructions of the present invention and claims and above-mentioned accompanying drawing are for distinguishing similar object, and need not be used for describing specific order or precedence.Should be appreciated that the data used like this can be exchanged, in the appropriate case so that embodiments of the invention described herein.In addition, term " comprises " and " having " and their any distortion, intention is to cover not exclusive comprising, such as, contain those steps or unit that the process of series of steps or unit, method, system, product or equipment is not necessarily limited to clearly list, but can comprise clearly do not list or for intrinsic other step of these processes, method, product or equipment or unit.
Embodiments provide a kind of information recommendation method.The information that this information recommendation method can be clicked according to user be come for user's pushed information, and the information clicked due to user can embody the interest of this user, therefore by the click information of user for user's pushed information compares the interest of laminating user.In addition, the information pushed to user is hot information, and this, for content update website faster, can improve the speed that new content is pushed, such as, and the video that can upgrade in pushing video website in time or the information of news website.
Fig. 1 is the process flow diagram of the information recommendation method according to the embodiment of the present invention.As shown in Figure 1, to comprise step as follows for this information recommendation method:
Step S102, obtains the current click information of user.
Step S104, the classification belonging to current click information search be associated with classification associate classification.
Step S106, finds hot information from other information of association class.
Step S108, recommends hot information to user.
When user clicks an information of website, just can obtain the current click information of user, this click information belongs to a category information, find be associated with this classification associate classification, and find hot information to recommend user from this classification.The classification relevant to the classification belonging to current click information can comprise click information generic itself, can also comprise the classification relevant to click information generic.
The classification of information can need adjustment according to difference analysis, and such as, the comparatively macrotaxonomy that portal website A has has: news, amusement and finance and economics, has less classification under each classification.Stock, fund and gold etc. are comprised again under the classification of finance and economics.When searching association classification, according to the classification searching association classification of current click information, can also search in the larger classification belonging to current click information and search association classification
Such as, the news that user clicks is " stock market's daily turnover ", and so determine that the interested classification of this user is stock, relevant classification has fund, bank and financing etc., namely associating classification is fund, bank and financing, and the information of so recommending can be just the hot information in these classifications.In addition, the news that user clicks is " stock market's daily turnover ", the interested classification of user also can be divided into finance and economics, and the classification be so associated with finance and economics may have the classification such as science and technology, military affairs, so just from these large classes, searches hot information and recommends.
Pass through above-described embodiment, the interested information of user is gone out according to the click behavioural analysis of user, and the hot information searched in the interested category information of user, and hot information is recommended user, the information recommending user is made to be the interested information of user, it is the recommendation information of the personalization for each user, instead of the fix information all identical to all users, thus to solve in prior art to the inaccurate problem of user's recommendation information, reach the effect of accurately recommending customized information to user.
Preferably, in order to newer information first can being recommended away, when determining hot information, time of combining information and click volume judge, namely in other information of association class, find hot information to comprise: obtain the time of other information of association class and the click volume of other information of association class.The weight information of acquisition time and click volume.According to the score value of the information of time, click volume and weight information compute associations classification.Using information the highest for score value as hot information.
The time of other information of association class is the time that information produces, click volume can reflect the attention of this information, the weight information of time and click volume can do different adjustment according to different news, according to the time, click volume and weight information thereof calculate the score value of every bar information, the information that score value is the highest just can be recommended as hot information, fast new information recommendation can not only be gone out, the interest of information and the user recommended can also be made more to fit, make new information recommended give to the interested people of this information, make the recommendation of information more targeted.
When determining hot information, different classes of hot information can be added up, for the ease of searching hot information, hot information can be stored in a database, this database can record category IDs belonging to hot information, the ID of hot information and click volume.When determining the hot information searching some classifications, the ID of this hot information can be found according to the ID of information generic fast, and the information recommendation corresponding to the ID of hot information is gone out.
Preferably, the classification belonging to current click information search be associated with classification associate classification before, set up information category contingency table, so that search the information category with current click information association, namely the method also comprises: obtain the user click data recorded in website; According to the information category of pre-defined rule identification user click data; The information category identified is added in user click data, obtains new click data; According to the incidence relation in new click data, the information category identified is associated, obtain information category contingency table.
After acquisition user click data, the classification belonging to user click data is identified, the classification identified is added in user's clicks, obtain new click data.When associating information category, determine the incidence relation between corresponding information category according to the incidence relation in the new click data carrying information category between click data, also just obtain the information category contingency table of the incidence relation recorded between information category.
From user click data, parse the user click data with incidence relation comprise: obtain the multiple click datas in user click data.Search the data recording adopting consecutive click chemical reaction in multiple click data.And using the data of adopting consecutive click chemical reaction as the user click data with incidence relation.
Being deployed in JS script on website can the click data of recording user, and is found out the click data with incidence relation by parsing module.The click data with incidence relation comprises many click datas of adopting consecutive click chemical reaction, such as, user jumps to information M from information L, jump to information N again, so information L and information N be all information M associate click information, each self-corresponding classification of information L, information N and information M just has certain incidence relation.This incidence relation is recorded, just can as the information category contingency table corresponding to the incidence relation between the information category corresponding to information L, information N and information M.
Particularly, the incidence relation between information category has been stored in information category contingency table, the information category associated by current click information just can be found from information category contingency table, namely the classification belonging to current click information is searched the classification that associates be associated with classification and is comprised, and resolves current click information and determines the information category belonging to current click information.The information category associated with the information category belonging to current click information is searched from information category contingency table.
Below in conjunction with Fig. 2, the present embodiment is described.
The front end script be deployed on website can be collected user click data, and click data is sent to daily record resolution system, and obtains correlation rule.
The log statistic that background server is collected according to front end script goes out hot information.
After recommended engine receives user's click, according to user's click information, recommend online according to correlation rule and hot information, and recommendation displaying is carried out to the information of recommending, thus achieve the recommendation carrying out personalization according to the click behavior of user, to solve in prior art to the inaccurate problem of user's recommendation information, reach the effect of accurate recommendation information.
The embodiment of the present invention additionally provides a kind of information recommending apparatus.This device can realize its function by computing machine.It should be noted that, the information recommending apparatus of the embodiment of the present invention may be used for performing the information recommendation method that the embodiment of the present invention provides, and the information recommending apparatus that the information recommendation method of the embodiment of the present invention also can be provided by the embodiment of the present invention performs.
Fig. 3 is the schematic diagram of the information recommending apparatus according to the embodiment of the present invention.As shown in Figure 3, this information recommending apparatus comprises: the first acquiring unit 10, first is searched unit 30, second and searched unit 50 and recommendation unit 70, wherein:
First acquiring unit 10 is for obtaining the current click information of user.
First search unit 30 for the classification belonging to current click information search be associated with classification associate classification.
Second searches unit 50 for finding hot information from other information of association class.And
Recommendation unit 70 is for recommending hot information to user.
When user clicks an information of website, just can obtain the current click information of user, this click information belongs to a category information, find be associated with this classification associate classification, and find hot information to recommend user from this classification.The classification relevant to the classification belonging to current click information can comprise click information generic itself, can also comprise the classification relevant to click information generic.
The classification of information can need adjustment according to difference analysis, and such as, the comparatively macrotaxonomy that portal website A has has: news, amusement and finance and economics, has less classification under each classification.Stock, fund and gold etc. are comprised again under the classification of finance and economics.When searching association classification, according to the classification searching association classification of current click information, can also search in the larger classification belonging to current click information and search association classification
Such as, the news that user clicks is " stock market's daily turnover ", and so determine that the interested classification of this user is stock, relevant classification has fund, bank and financing etc., namely associating classification is fund, bank and financing, and the information of so recommending can be just the hot information in these classifications.In addition, the news that user clicks is " stock market's daily turnover ", the interested classification of user also can be divided into finance and economics, and the classification be so associated with finance and economics may have the classification such as science and technology, military affairs, so just from these large classes, searches hot information and recommends.
Pass through above-described embodiment, the interested information of user is gone out according to the click behavioural analysis of user, and the hot information searched in the interested category information of user, and hot information is recommended user, the information recommending user is made to be the interested information of user, it is the recommendation information of the personalization for each user, instead of the fix information all identical to all users, thus to solve in prior art to the inaccurate problem of user's recommendation information, reach the effect of accurately recommending customized information to user.
Preferably, in order to newer information first can being recommended away, when determining hot information, time of combining information and click volume judge, namely second search unit and comprise: the first acquisition module, for the click volume of time and other information of association class of obtaining other information of association class; Second acquisition module, for the weight information of acquisition time and click volume; Computing module, for the score value of the information according to time, click volume and weight information compute associations classification; And first determination module, for using information the highest for score value as hot information.
The time of other information of association class is the time that information produces, click volume can reflect the attention of this information, the weight information of time and click volume can do different adjustment according to different news, according to the time, click volume and weight information thereof calculate the score value of every bar information, the information that score value is the highest just can be recommended as hot information, fast new information recommendation can not only be gone out, the interest of information and the user recommended can also be made more to fit, make new information recommended give to the interested people of this information, make the recommendation of information more targeted.
When determining hot information, different classes of hot information can be added up, for the ease of searching hot information, hot information can be stored in a database, this database can record category IDs belonging to hot information, the ID of hot information and click volume.When determining the hot information searching some classifications, the ID of this hot information can be found according to the ID of information generic fast, and the information recommendation corresponding to the ID of hot information is gone out.
Preferably, set up information category contingency table, so that search the information category with current click information association, namely this device also comprises: second acquisition unit, for search in the classification belonging to current click information be associated with classification associate classification before, obtain in website the user click data recorded; Recognition unit, for the information category according to pre-defined rule identification user click data; Adding device, for adding in user click data by the information category identified, obtains new click data; Associative cell, for associating the information category identified according to the incidence relation in new click data, obtains information category contingency table.
After acquisition user click data, the classification belonging to user click data is identified, the classification identified is added in user's clicks, obtain new click data.When associating information category, determine the incidence relation between corresponding information category according to the incidence relation in the new click data carrying information category between click data, also just obtain the information category contingency table of the incidence relation recorded between information category.
Associative cell comprises: the 3rd acquisition module, for obtaining the multiple click datas in user click data; Second searches module, for searching the data recording adopting consecutive click chemical reaction in multiple click data; And second determination module, for using the data of adopting consecutive click chemical reaction as the user click data with incidence relation.
Being deployed in JS script on website can the click data of recording user, and is found out the click data with incidence relation by parsing module.The click data with incidence relation comprises many click datas of adopting consecutive click chemical reaction, such as, user jumps to information M from information L, jump to information N again, so information L and information N be all information M associate click information, each self-corresponding classification of information L, information N and information M just has certain incidence relation.This incidence relation is recorded, just can as the information category contingency table corresponding to the incidence relation between the information category corresponding to information L, information N and information M.
Particularly, the incidence relation between information category has been stored in information category contingency table, the information category associated by current click information just can be found from information category contingency table, namely first search unit and comprise: parsing module, for resolving current click information and determining the information category belonging to current click information; First searches module, for searching the information category associated with the information category belonging to current click information from information category contingency table.
It should be noted that, for aforesaid each embodiment of the method, in order to simple description, therefore it is all expressed as a series of combination of actions, but those skilled in the art should know, the present invention is not by the restriction of described sequence of movement, because according to the present invention, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in instructions all belongs to preferred embodiment, and involved action and module might not be that the present invention is necessary.
In the above-described embodiments, the description of each embodiment is all emphasized particularly on different fields, in certain embodiment, there is no the part described in detail, can see the associated description of other embodiments.
In several embodiments that the application provides, should be understood that, disclosed device, the mode by other realizes.Such as, device embodiment described above is only schematic, the such as division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of device or unit or communication connection can be electrical or other form.
The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit exists, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form of SFU software functional unit also can be adopted to realize.
If described integrated unit using the form of SFU software functional unit realize and as independently production marketing or use time, can be stored in a computer read/write memory medium.Based on such understanding, the part that technical scheme of the present invention contributes to prior art in essence in other words or all or part of of this technical scheme can embody with the form of software product, this computer software product is stored in a storage medium, comprises all or part of step of some instructions in order to make a computer equipment (can be personal computer, mobile terminal, server or the network equipment etc.) perform method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, ROM (read-only memory) (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), portable hard drive, magnetic disc or CD etc. various can be program code stored medium.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. an information recommendation method, is characterized in that, comprising:
Obtain the current click information of user;
Classification belonging to described current click information search be associated with described classification associate classification;
Hot information is searched from other information of described association class; And
Described hot information is recommended to described user.
2. method according to claim 1, is characterized in that, searches hot information and comprise from other information of described association class:
Obtain the time of described other information of association class and the click volume of described other information of association class;
Obtain the weight information of described time and described click volume;
The score value of described other information of association class is calculated according to described time, described click volume and described weight information; And
Using information the highest for described score value as described hot information.
3. method according to claim 1, is characterized in that, the classification belonging to described current click information search be associated with described classification associate classification before, described method also comprises:
Obtain the user click data recorded in website;
According to the information category of user click data described in pre-defined rule identification;
The information category identified is added in described user click data, obtains new click data;
According to the incidence relation in described new click data, the described information category identified is associated, obtain information category contingency table.
4. method according to claim 3, is characterized in that, the classification belonging to described current click information is searched the classification that associates be associated with described classification and comprised:
Resolve described current click information and determine the information category belonging to described current click information;
The information category associated with the information category belonging to described current click information is searched from described information category contingency table.
5. method according to claim 3, is characterized in that, associates the described information category identified according to the incidence relation in described new click data, obtains information category contingency table and comprises:
Obtain the multiple click datas in described new click data;
Search the data recording adopting consecutive click chemical reaction in described multiple click data; And
Using the data of described adopting consecutive click chemical reaction as the described user click data with incidence relation.
6. an information recommending apparatus, is characterized in that, comprising:
First acquiring unit, for obtaining the current click information of user;
First searches unit, for the classification belonging to described current click information search be associated with described classification associate classification;
Second searches unit, for finding hot information from other information of described association class; And
Recommendation unit, for recommending described hot information to described user.
7. device according to claim 6, is characterized in that, described second searches unit comprises:
First acquisition module, for the click volume of time and described other information of association class of obtaining described other information of association class;
Second acquisition module, for obtaining the weight information of described time and described click volume;
Computing module, for calculating the score value of described other information of association class according to described time, described click volume and described weight information; And
First determination module, for using information the highest for described score value as described hot information.
8. device according to claim 6, is characterized in that, described device also comprises:
Second acquisition unit, for search in the classification belonging to described current click information be associated with described classification associate classification before, obtain in website the user click data recorded;
Recognition unit, for the information category according to user click data described in pre-defined rule identification;
Adding device, for being added in described user click data by the information category identified, obtains new click data;
Associative cell, for associating the described information category identified according to the incidence relation in described new click data, obtains information category contingency table.
9. device according to claim 8, is characterized in that, described first searches unit comprises:
Parsing module, for resolving described current click information and determining the information category belonging to described current click information;
First searches module, for searching the information category associated with the information category belonging to described current click information from described information category contingency table.
10. device according to claim 8, is characterized in that, described associative cell comprises:
3rd acquisition module, for obtaining the multiple click datas in described new click data;
Second searches module, for searching the data recording adopting consecutive click chemical reaction in described multiple click data; And
Second determination module, for using the data of described adopting consecutive click chemical reaction as the described user click data with incidence relation.
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Denomination of invention: Audio and video file labeling method and device and information recommendation method and device Effective date of registration: 20190531 Granted publication date: 20180202 Pledgee: Shenzhen Black Horse World Investment Consulting Co., Ltd. Pledgor: Beijing Guoshuang Technology Co.,Ltd. Registration number: 2019990000503 |
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