CN110297966A - Content recommendation method and device for community's class application program - Google Patents
Content recommendation method and device for community's class application program Download PDFInfo
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- CN110297966A CN110297966A CN201910335296.5A CN201910335296A CN110297966A CN 110297966 A CN110297966 A CN 110297966A CN 201910335296 A CN201910335296 A CN 201910335296A CN 110297966 A CN110297966 A CN 110297966A
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Abstract
This application discloses a kind of content recommendation methods and device for community's class application program.This method comprises: carrying out user's classification processing to the user in community, the user data by classification is obtained；Classifying content processing is carried out to the content in community according to label, obtains the content-data with label；According to the user data by classification, recommended priority is determined to the content-data with label.Present application addresses the ineffective technical problems of commending contents in community's class application program.Recommended and accurate recommendation by the customized content that the application realizes community's class application program.
Description
Technical field
This application involves community class commending contents fields, in particular to a kind of in community's class application program
Hold recommended method and device.
Background technique
Community's class application program, refer to the ends of the earth, cat flutter, bean cotyledon, know etc. can be posted, money order receipt to be signed and returned to the sender, chat community
Class application.
Inventors have found that recommended article content is single in community's class application program, different user group cannot be sufficiently excited
Community attributes further also lack the ability of intelligent recommendation.
For the ineffective problem of commending contents in community's class application program in the related technology, not yet propose at present effective
Solution.
Summary of the invention
The main purpose of the application is to provide a kind of content recommendation method and device for community's class application program, with
Solve the problems, such as that commending contents are ineffective in community's class application program.
To achieve the goals above, it according to the one aspect of the application, provides a kind of for community's class application program
Content recommendation method.
Content recommendation method according to the application ... for community's class application program includes: to carry out to the user in community
User's classification processing obtains the user data by classification；Classifying content processing is carried out to the content in community according to label, is obtained
To the content-data for having label；According to the user data by classification, the content-data with label is determined
Recommended priority.
Further, in community user carry out user's classification processing, obtain include by the user data of classification: it is right
User in community carries out first user's classification processing, second user classification processing and third user's classification processing；Described
One user's classification processing, for using the user of community's class application program to divide as a kind of user using new registration or for the first time
Class obtains the user data by the classification；The second user classification processing, for that will refer to that the amount of posting is not more than in community
Default post or registers number of days and classifies no more than the user of registration number of days threshold value as a kind of user threshold value, obtain by
The user data of the classification；The third user classification processing, for the amount of posting to be not less than default threshold value and the registration day of posting
Number is classified not less than the user of registration number of days threshold value as a kind of user, and the user data by the classification is obtained.
Further, classifying content processing is carried out to the content in community according to label, obtains the content number with label
According to include: according to publisher area, publication content said module, publication content quality as label in community content progress
Classifying content processing, obtains the content-data with label, wherein and each model content has at least one label in community,
Any one label belongs at least one model.
Further, according to the user data by classification, the content-data with label is determined and is recommended
After priority, further includes: the user behavior for obtaining user in community is accustomed to data；It is accustomed to data according to the user behavior,
Target user after from the corresponding content-data with label of matching to the user data by classification pushes away
It recommends.
Further, according to the user data by classification, the content-data with label is determined and is recommended
After priority, further includes: record the preset browsing parameter it has been recommended that in content；Configure the weight in the preset browsing parameter
The factor；Weight ranking is calculated according to the weight factor and the preset browsing parameter；It will be forward in the weight ranking
The content is as the alternating content for pushing model content next time.
Further, according to the user data by classification, the content-data with label is determined and is recommended
After priority, further includes: judgement is it has been recommended that whether content has browsed；If it is determined that it has been recommended that content has browsed, then root
Relevant content information is obtained according to the high label of user's frequency of use；The relevant content information of acquisition is generated as the community
Middle model is recommended to user again as the content-data with label；It traverses and recycles aforesaid operations until user ties
Beam browsing.
To achieve the goals above, it according to the another aspect of the application, provides a kind of in community's class application program
Hold recommendation apparatus.
It include: user's categorization module according to the content recommendation device in community's class application program of the application, for society
User in area carries out user's classification processing, obtains the user data by classification；Content, classification module, for according to label pair
Content in community carries out classifying content processing, obtains the content-data with label；Priority configuration module, for according to institute
The user data by classification is stated, recommended priority is determined to the content-data with label.
Further, described device further include: habit recommending module, the user behavior for obtaining user in community are accustomed to
Data；According to user behavior habit data, after the corresponding content-data with label of matching excessively to the warp
Target user in the user data of class recommends.
Further, described device further include: feedback recommendation module, for recording it has been recommended that the preset browsing in content is joined
Number；Configure the weight factor in the preset browsing parameter；It is calculated according to the weight factor and the preset browsing parameter
Weight ranking；Using the content forward in the weight ranking as the alternating content for pushing model content next time.
Further, described device further include: supplement recommending module, for judging it has been recommended that whether content has browsed；
If it is determined that then obtaining relevant content information according to the high label of user's frequency of use it has been recommended that content has browsed；It will acquisition
The relevant content information be generated as model in the community, pushed away again to user as the content-data with label
It recommends；It traverses and recycles aforesaid operations until user terminates to browse.
It is used for the content recommendation method and device of community's class application program in the embodiment of the present application, using in community
User carry out user's classification processing, obtain the mode of the user data by classification, by according to label to the content in community
Classifying content processing is carried out, the content-data with label is obtained, has been reached according to the user data by classification, to institute
It states the content-data with label and determines the purpose of recommended priority, recommend and accurate recommendation to realize customized content
Technical effect, and then solve the technical problem that commending contents are ineffective in community's class application program.
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 illustrated according to the content recommendation method process for community's class application program in the application first embodiment
Figure；
Fig. 2 is illustrated according to the content recommendation method process for community's class application program in the application second embodiment
Figure；
Fig. 3 is illustrated according to the content recommendation method process for community's class application program in the application 3rd embodiment
Figure；
Fig. 4 is illustrated according to the content recommendation method process for community's class application program in the application fourth embodiment
Figure；
Fig. 5 is according to the content recommendation device structural representation for community's class application program in the application first embodiment
Figure；
Fig. 6 is according to the content recommendation device structural representation for community's class application program in the application second embodiment
Figure；
Fig. 7 is according to the content recommendation device structural representation for community's class application program in the application first embodiment
Figure；
Fig. 8 is according to the content recommendation device structural representation for community's class application program in the application second embodiment
Figure；
Fig. 9 is according to the content recommendation device structural representation for community's class application program in the application 3rd embodiment
Figure；
Figure 10 is shown according to the content recommendation device structure for community's class application program in the application fourth embodiment
It is intended to.
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.
Content recommendation method of one of the embodiment of the present application for community's class application program has as follows a little: 1) adopting
Recommended with customizing, specific commending contents may be implemented to specific user in different labelings and user's classification.2) it pushes away
It recommends more precisely: by the user feedback analysis mechanisms of user behavior analysis and content, preferential recommendation may be implemented to user and feel emerging
The content of interest.3) content is richer: using crawler technology, the user experience of infinite contents browsing may be implemented.
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.
As shown in Figure 1, this method includes the following steps, namely S102 to step S106:
Step S102 carries out user's classification processing to the user in community, obtains the user data by classification；
User in the community refers to the user in community's class application program.
User's classification processing, which refers to, to be carried out user grouping according to different dimensions or classifies according to different type.
The user data obtained after classification, meets relevant configuration requirement.
Step S104 carries out classifying content processing to the content in community according to label, obtains the content number with label
According to；
When carrying out classifying content processing to the content in community according to label a content can be corresponded to according to multiple labels
That is a model.
It can also be corresponded to according to a label when carrying out classifying content processing to the content in community according to label in multiple
Holding is multiple models.
The content-data that label is had described in obtaining after classification, meets relevant configuration requirement.
Step S106 determines the content-data with label and recommends according to the user data by classification
Priority.
According to the user data by classification, the content-data with label is recommended, the way of recommendation
It can be obtained after recommended priority has been determined.
Specifically, different recommended priorities, the higher content of priority are defined to the content-data with label
Preferential recommendation is to the user data by classification.
It can be seen from the above description that the application realizes following technical effect:
It is used for the content recommendation method and device of community's class application program in the embodiment of the present application, using in community
User carry out user's classification processing, obtain the mode of the user data by classification, by according to label to the content in community
Classifying content processing is carried out, the content-data with label is obtained, has been reached according to the user data by classification, to institute
It states the content-data with label and determines the purpose of recommended priority, recommend and accurate recommendation to realize customized content
Technical effect, and then solve the technical problem that commending contents are ineffective in community's class application program.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in Fig. 2, using the user in community
Family classification processing, obtain include by the user data of classification:
Step 202, first user's classification processing, second user classification processing and third is carried out to the user in community to use
Family classification processing；
Step 204, the first user classification processing, for by new registration or for the first time using the use of community's class application program
Family is classified as a kind of user, obtains the user data by the classification；
Step 206, the second user classification processing, for that will refer to the amount of posting no more than default threshold value of posting in community
Or registration number of days is classified no more than the user of registration number of days threshold value as a kind of user, and the user by the classification is obtained
Data；
Step 208, the third user classification processing, for the amount of posting to be not less than default threshold value and the registration number of days of posting
User not less than registration number of days threshold value classifies as a kind of user, obtains the user data by the classification.
Specifically, by being divided into three categories all users: new user, either shallow user, depth user.
The new user refers to new registration or is initially opened the user of community's class application program；
The either shallow user refers to that the amount of posting is less than some threshold value M, or registration number of days is less than N days users；
The depth user refers to that the amount of posting is more than or equal to M and registers the user that number of days is more than or equal to N days.
Classified based on above-mentioned user, realizes different recommendation logics.
It should be noted that user's classification in embodiments herein is not limited to aforesaid way, as long as can
Meet different recommendation logics.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 3, according to label in community
Hold and carry out classifying content processing, obtaining the content-data with label includes:
Step 302, regional according to publisher, publication content said module, publication content quality are as label in community
Content carry out classifying content processing, obtain the content-data with label, wherein each model content has at least in community
One label, any one label belong at least one model.
Specifically, content is subjected to detailed-oriented classification, publisher area, content said module, content by different labels
Quality all can serve as specific label, and a model content is under the jurisdiction of multiple labels, and a label also may include multiple notes
Son.
In addition, the recommended method classified by user oriented, defines different push away to the content-data with label
Priority is recommended, the higher content priority of priority recommends the user data by classification.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 4, according to the use by classification
User data, after determining recommended priority to the content-data with label, further includes:
Step 402, the user behavior for obtaining user in community is accustomed to data；
Step 404, data are accustomed to according to the user behavior, it is backward matches the corresponding content-data with label
Target user in the user data by classification recommends.
Specifically, under the premise of not influencing user experience, the keyword used when the search of each user, application are acquired
The information such as the click event of programs menu navigation, the stay time at different interfaces, analyze the behavioural habits of user, then match phase
The label substance answered, recommends designated user.
It should be noted that in the embodiment of the present application user behavior habit data be not limited to it is above-mentioned, as long as can
The requirement of associated user's habit data.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 5, according to the use by classification
User data, after determining recommended priority to the content-data with label, further includes:
Step 502, the preset browsing parameter it has been recommended that in content is recorded；
Step 504, the weight factor in the preset browsing parameter is configured；
Step 506, weight ranking is calculated according to the weight factor and the preset browsing parameter；
Step 508, using the content forward in the weight ranking as in the candidate for pushing model content next time
Hold.
Specifically, existing caching technology is used to the content recommended away by the recommended method based on user feedback
The parameters such as PV, UV of each model, reply number, content residence time, opening rate are recorded, and these parameter settings are weighed
Repeated factor finally calculates a comprehensive weight ranking, then says that weight model content in the top is pushed as next group
The candidate of content.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in fig. 6, according to the use by classification
User data, after determining recommended priority to the content-data with label, further includes:
Step 602, judgement is it has been recommended that whether content has browsed；
Step 604, it if it is determined that it has been recommended that content has browsed, is then obtained according to the high label of user's frequency of use related
Content information；
Step 606, the relevant content information of acquisition is generated as model in the community, has label as described
Content-data again to user recommend；
Step 608, it traverses and recycles aforesaid operations until user terminates to browse.
Specifically, the recommendation added machinery based on crawler technology is used in the above method, if recommending user's
Content is all over by user's browsing, system can in server-side using crawler system according to the common label removal search engine of user or
Person's specified sites acquire relevant content information, then say that collected content automatically generates by server-side background program and are
The model of system, and accomplish fluently corresponding label, then recommends user, thus realize provide the user with infinite browse content can
Energy.
It should be noted that step shown in the flowchart of the accompanying drawings can be in such as a group of computer-executable instructions
It is executed in computer system, although also, logical order is shown in flow charts, and it in some cases, can be with not
The sequence being same as herein executes shown or described step.
According to the embodiment of the present application, additionally provide a kind of for implementing the above-mentioned commending contents for community's class application program
The device of method, as shown in fig. 7, the device includes: user's categorization module 10, for carrying out user's classification to the user in community
Processing, obtains the user data by classification；Content, classification module 20, for carrying out content to the content in community according to label
Classification processing obtains the content-data with label；Priority configuration module 30, for according to the number of users by classification
According to determining recommended priority to the content-data with label.
User in community described in user's categorization module 10 of the embodiment of the present application refers in community's class application program
User.
User's classification processing, which refers to, to be carried out user grouping according to different dimensions or classifies according to different type.
The user data obtained after classification, meets relevant configuration requirement.
Classifying content processing is carried out to the content in community according to label in the content, classification module 20 of the embodiment of the present application
When can be according to corresponding one model of a content of multiple labels.
It can also be corresponded to according to a label when carrying out classifying content processing to the content in community according to label in multiple
Holding is multiple models.
The content-data that label is had described in obtaining after classification, meets relevant configuration requirement.
According to the user data by classification in the priority configuration module 30 of the embodiment of the present application, had to described
The content-data of label is recommended, and the way of recommendation can obtain after recommended priority has been determined.
Specifically, different recommended priorities, the higher content of priority are defined to the content-data with label
Preferential recommendation is to the user data by classification.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 8, described device further include: habit pushes away
Module 40 is recommended, the user behavior for obtaining user in community is accustomed to data；It is accustomed to data according to the user behavior, matches phase
Target user after from the content-data with label answered to the user data by classification recommends.
In the habit recommending module 40 of the embodiment of the present application specifically, under the premise of not influencing user experience, acquisition is every
The letters such as the stay time of click event, different interfaces that keyword, the application menu used when the search of a user navigates
Breath, analyzes the behavioural habits of user, then matches corresponding label substance, recommend designated user.
It should be noted that in the embodiment of the present application user behavior habit data be not limited to it is above-mentioned, as long as can
The requirement of associated user's habit data.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in figure 9, described device further include: feedback pushes away
Module 50 is recommended, for recording the preset browsing parameter it has been recommended that in content；Configure the weight factor in the preset browsing parameter；
Weight ranking is calculated according to the weight factor and the preset browsing parameter；By in the weight ranking it is forward it is described in
Hold as the alternating content for pushing model content next time.
In the feedback recommendation module 50 of the embodiment of the present application specifically, by the recommended method based on user feedback, to pushing away
The content recommended away, using existing caching technology to PV, UV of each model, reply number, content residence time, opening rate etc.
Parameter is recorded, and to these parameter setting weight factors, finally calculates a comprehensive weight ranking, then says that weight is arranged
Candidate of the forward model content of name as next group push content.
According to the embodiment of the present application, as preferred in the present embodiment, as shown in Figure 10, described device further include: supplement
Recommending module 60, for judging it has been recommended that whether content has browsed；If it is determined that it has been recommended that content has browsed, then according to
Frequency of use high label in family obtains relevant content information；The relevant content information of acquisition is generated as note in the community
Son is recommended to user again as the content-data with label；Traverse and recycle aforesaid operations until user terminate it is clear
It lookes at.
In the supplement recommending module 60 of the embodiment of the present application specifically, the recommendation based on crawler technology is used in the above method
Content augmentation mechanism, if the content for recommending user is all over by user's browsing, system can use crawler system in server-side
Relevant content information is acquired according to the common label removal search engine of user or specified sites, then passes through server-side backstage
Program says that collected content automatically generates the model of this system, and accomplishes fluently corresponding label, then recommends user, thus real
Now provide the user with the possibility of infinite browse content.
Obviously, those skilled in the art should be understood that each module of above-mentioned the application or each step can be with general
Computing device realize that they can be concentrated on a single computing device, or be distributed in multiple computing devices and formed
Network on, optionally, they can be realized with the program code that computing device can perform, it is thus possible to which they are stored
Be performed by computing device in the storage device, perhaps they are fabricated to each integrated circuit modules or by they
In multiple modules or step be fabricated to single integrated circuit module to realize.In this way, the application be not limited to it is any specific
Hardware and software combines.
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 (10)
1. a kind of content recommendation method for community's class application program characterized by comprising
User's classification processing is carried out to the user in community, obtains the user data by classification；
Classifying content processing is carried out to the content in community according to label, obtains the content-data with label；
According to the user data by classification, recommended priority is determined to the content-data with label.
2. content recommendation method according to claim 1, which is characterized in that carried out at user's classification to the user in community
Reason, obtain include by the user data of classification:
First user's classification processing, second user classification processing and third user's classification processing are carried out to the user in community；
The first user classification processing, for using the user of community's class application program to use as one kind using new registration or for the first time
Family is classified, and the user data by the classification is obtained；
The second user classification processing, for that will refer to the amount of posting no more than default threshold value or the registration number of days of posting in community
User no more than registration number of days threshold value classifies as a kind of user, obtains the user data by the classification；
The third user classification processing is not less than registration day for being not less than the amount of posting to preset to post threshold value and register number of days
The user of number threshold value classifies as a kind of user, obtains the user data by the classification.
3. content recommendation method according to claim 1, which is characterized in that according to label in the content progress in community
Hold classification processing, obtaining the content-data with label includes:
According to publisher area, publication content said module, publication content quality as label in the content progress in community
Hold classification processing, obtain the content-data with label, wherein each model content has at least one label in community, appoints
One one label belongs at least one model.
4. content recommendation method according to claim 1, which is characterized in that according to it is described by classification user data,
After determining recommended priority to the content-data with label, further includes:
The user behavior for obtaining user in community is accustomed to data；
It is accustomed to data according to the user behavior, matches after the content-data with label to described by classification
User data in target user recommend.
5. content recommendation method according to claim 1, which is characterized in that according to it is described by classification user data,
After determining recommended priority to the content-data with label, further includes:
Record the preset browsing parameter it has been recommended that in content；
Configure the weight factor in the preset browsing parameter；
Weight ranking is calculated according to the weight factor and the preset browsing parameter；
Using the content forward in the weight ranking as the alternating content for pushing model content next time.
6. content recommendation method according to claim 1, which is characterized in that according to it is described by classification user data,
After determining recommended priority to the content-data with label, further includes:
Judgement is it has been recommended that whether content has browsed；
If it is determined that then obtaining relevant content information according to the high label of user's frequency of use it has been recommended that content has browsed；
The relevant content information of acquisition is generated as model in the community, again as the content-data with label
It is secondary to recommend to user；
It traverses and recycles aforesaid operations until user terminates to browse.
7. a kind of content recommendation device for community's class application program characterized by comprising
User's categorization module obtains the user data by classification for carrying out user's classification processing to the user in community；
Content, classification module is obtained interior with label for carrying out classifying content processing to the content in community according to label
Hold data；
Priority configuration module, it is true to the content-data with label for the user data according to the process classification
Determine recommended priority.
8. content recommendation device according to claim 7, which is characterized in that further include: habit recommending module is used for
The user behavior for obtaining user in community is accustomed to data；
It is accustomed to data according to the user behavior, matches after the content-data with label to described by classification
User data in target user recommend.
9. content recommendation device according to claim 7, which is characterized in that further include: feedback recommendation module is used for
Record the preset browsing parameter it has been recommended that in content；
Configure the weight factor in the preset browsing parameter；
Weight ranking is calculated according to the weight factor and the preset browsing parameter；
Using the content forward in the weight ranking as the alternating content for pushing model content next time.
10. content recommendation device according to claim 7, which is characterized in that further include: supplement recommending module is used for
Judgement is it has been recommended that whether content has browsed；
If it is determined that then obtaining relevant content information according to the high label of user's frequency of use it has been recommended that content has browsed；
The relevant content information of acquisition is generated as model in the community, again as the content-data with label
It is secondary to recommend to user；
It traverses and recycles aforesaid operations until user terminates to browse.
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| CN201910335296.5A CN110297966A (en) | 2019-04-24 | 2019-04-24 | Content recommendation method and device for community's class application program |
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| CN201910335296.5A CN110297966A (en) | 2019-04-24 | 2019-04-24 | Content recommendation method and device for community's class application program |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112650934A (en) * | 2021-01-18 | 2021-04-13 | 北京小川在线网络技术有限公司 | Content push-up method based on high participation of user and electronic equipment thereof |
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