CN106202534A - A kind of content recommendation method based on community users behavior and system - Google Patents
A kind of content recommendation method based on community users behavior and system Download PDFInfo
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Abstract
The invention provides a kind of content recommendation method based on community users behavior, client is received the request of community users input, behavioural information is sent to server；Described described community users behavioural information in server is collected and analyzes；Described behavioural information for collecting carries out pretreatment again, and the described behavioural information of the specification in described server uploads and use hadoop to store；The described behavioural information of specification is classified；Set up different community users models, community users is classified；Drawn a portrait by one community users of community users model generation again；Obtain the recommended content for community users.The present invention can carry out personalized recommendation in mobile client homepage and computer homepage, and according to community users hobby, unification carries out personalized recommendation, it is achieved the accurate service of thousand people thousand；The present invention is by precisely recommending, and recommended content improves 2 to 8 times than traditional clicking rate based on fixed position.
Description
Technical field
The present invention relates to networking technology area, particularly relate to process and the storage side of dynamic realtime output any specification picture
Method.
Background technology
The mobile Internet epoch are changing our everyone life, under this feature of Rapid Variable Design, mobile
The all of data in the Internet change the most at any time.Especially in the personalized epoch, the demand for each user carries out individual
Propertyization distinguish and location, make everyone open computer or mobile phone, it is seen that information content be different, the most advantageously in
The content of oneself is distributed by content provider accurately, advantageously sends out in the content effectively helping corporate boss to realize brand
Ferment and the accurate travel of content.
Particularly, develop into today at the web advertisement, advertisement putting had higher requirement: advertiser according to
The marketing objectives of oneself requires advertisement putting lock onto target audient, propagates one to one, it is provided that multiple terminals is thrown in, according to effect
Charging, here it is the concept that accurate advertisement is thrown in.The development speed of accurate advertisement is very fast, has the stage of the most several development,
First stage is that region orientation is thrown in, and second stage is the hobby according to client.One people goes to see an article
Time randomness have, but also have certain point of interest, the content that we are said by article, advertiser and they
Target customers do a match contacts together.Accurate advertisement is exactly each attribute according to user, including the net that surfs the web
By computer-related technologies, all network behaviors such as page, community are interactive, predict that the possible consumption of user is correlated with purpose, and root
The technology throwing in advertisement is oriented according to these purposes.
At present, in industry, commonly used user's registration information, user's browse path are combined with collaborative filtering recommending technology, will
User's registration information and browse path, as main basis for estimation, set up new user in predicting model, by model to user
It is predicted recommending.But, the accurate advertisement jettison system of most company research and development or lay particular emphasis on certain ring of advertisement putting
Joint (such as effect of advertising monitoring link), or lay particular emphasis on accurate advertisement input (such as search engine advertisement) in certain field, not
Form the general platform of a system.
The website not having community's concept be difficult to record user the mutual-action behavior such as browse and the behavior to user is made precisely
Analysis, and as a community website, have different channels and community to meet access or the interactive demand of user, every day is used
The interactions such as family constantly accesses in community, discusses, posts, money order receipt to be signed and returned to the sender, scoring, define user's distinctive user behavior information, society
The classification of user can be summarized according to these mutual-action behaviors of user in website, district, draws a portrait to user, then can give and use
The content wanted is recommended at family.
Summary of the invention
The technical problem to be solved be to provide a kind of dynamic content recommendation method based on community users behavior and
System, can to the user behavior information in community, filter, analyze and recommended content classification storage, and
The resources such as the recommended content after described classification storage are precisely recommended, promotes Consumer's Experience.To this end, the present invention provide with
Lower technical scheme:
A kind of content recommendation method based on community users behavior, it is characterised in that comprise the following steps: step 1: client
End receives the request of community users input, and the behavioural information of community users is arranged on the cookie of client by described client
In；
Step 2: the described behavioural information in described client cookie is sent to server by client；
Step 3: described described community users behavioural information in server is collected and analyzes；Again for collecting
Described behavioural information carry out pretreatment, to incomplete or do not meet the described behavioural information of specification, carry out clearing up, normalizing
Change, conversion, integrated, the community users behavioural information of stipulations operation formation specification；
Step 4: the described behavioural information of the specification in described server uploads and use hadoop to store；
Step 5: the described behavioural information of specification is classified；Setting up different community users models, described community uses
Community users is entered by family model by the community users ascribed characteristics of population, behavior interest, geographical position, mutual-action behavior and purchasing behavior etc.
Row classification；Drawn a portrait by one community users of community users model generation again；
Step 6: according to described community users portrait and the label of recommended content, uses data recommendation computing formula to carry out
Matching primitives sorts, and obtains the recommended content for community users, and described recommended content and label thereof are to be stored in advance in
The content informations such as model content on server, merchant advertisement content, activity description.
Described classification includes following characteristics:
The community users ascribed characteristics of population includes: sex, division of life span, and division of life span includes: unmarried, in love, parent-offspring, education
Deng；
Community users behavior interest, including: emotion, wedding celebration, parent-offspring, education, automobile, cuisines, tourism, fashion, investment, room
Product, number, job hunting, health etc.；
Community users geographical position, including: country, province, city, region；
Community users mutual-action behavior, including: post, money order receipt to be signed and returned to the sender, mark, collect, consulting etc.；
Community users purchasing behavior, including: place an order, payment etc..
Using on the basis of technique scheme, the present invention also can use technical scheme further below:
In step 6, the label of described recommended content is specially and uses Lucene participle technique to described recommended content
Carry out participle, each word after participle is carried out word frequency statistics, word frequency is defined as more than the word setting threshold value described interior
The label held, is labeled described recommended content.
In step 6, described data recommendation computing formula is: the hot value of described content recommendation+(current time-recommended
The issuing time of content)/the set time.
Calculate weight order value according to described computing formula, obtain the corresponding weight order value of described recommended content, so
After further according to weight order value, recommended content carried out screening and sequencing, output meets being pushed away of the behavior characteristics of community users
Recommend content.
Described system includes data collection module, data filtering module, data analysis module, data memory module, data
Recommending module；
Described data collection module, is stored in cookie by javascript technology by the behavioural information of community users
In, described data module is additionally operable to, in community users accesses station behind address, send the behavioural information in cookie to service
Device, it is achieved the collection of community users behavioural information；
Described data filtering module has been used for the scanning to described behavioural information and has filtered, and removes the community's use not being inconsistent specification
Family behavioural information, and complete the arrangement to described behavioural information；
Described data memory module, for storing the community users behavioural information after filtering, uses hadoop technology
Store.
Described data recommendation module for carrying out the coupling of recommended content, brush choosing, sorting and recommend for community users.
Owing to using technical scheme, the invention have the benefit that the present invention in mobile client homepage and
Computer homepage can carry out personalized recommendation, " thousand people thousand faces ", according to community users hobby, unified to model, circle, plate
Block, activity, advertisement, commodity, service carry out personalized recommendation, it is achieved the accurate service of thousand people thousand；
The present invention is by precisely recommending, and recommended content improves 2 to 8 times than traditional clicking rate based on fixed position.
This technology achieves recommended content and persistently exposes with community users simultaneously, and impression doubles than traditional location exposure；
Present invention achieves personalized correlation recommendation, got through between model, businessman, commodity, plate by correlation recommendation
Access path, forms the ecosystem that community is unique；
The present invention can realize accurate advertisement and throw in, and promotes ad conversion rates, promotes operating income, it is achieved value added by marketing,
Service pushes, and by channels such as client, note, wechat, actively pushes accurate information to community users, convene for activity,
Under line, set-off, commercial promotions, service provide.
Accompanying drawing explanation
The method flow diagram that Fig. 1 provides for the present invention.
The system module figure that Fig. 2 provides for the present invention.
Detailed description of the invention
As it is shown in figure 1, a kind of content recommendation method based on community users behavior, comprise the following steps: step 1: client
End receives the request of community users input, and the behavioural information of community users is arranged on the cookie of client by described client
In；
Step 2: the described behavioural information in described client cookie is sent to server by client；
Step 3: described described community users behavioural information in server is collected and analyzes；Again for collecting
Described behavioural information carry out pretreatment, to incomplete or do not meet the described behavioural information of specification, carry out clearing up, normalizing
Change, conversion, integrated, the community users behavioural information of stipulations operation formation specification；
Step 4: the described behavioural information of the specification in described server uploads and use hadoop to store；
Step 5: the described behavioural information of specification is classified；Setting up different community users models, described community uses
Community users is entered by family model by the community users ascribed characteristics of population, behavior interest, geographical position, mutual-action behavior and purchasing behavior etc.
Row classification；Drawn a portrait by one community users of community users model generation again；
Step 6: according to described community users portrait and the label of recommended content, uses data recommendation computing formula to carry out
Matching primitives sorts, and obtains the recommended content for community users, and described recommended content and label thereof are to be stored in advance in
The content informations such as model content on server, merchant advertisement content, activity description.
The label of described recommended content is specially and uses Lucene participle technique that described recommended content is carried out participle,
Each word after participle is carried out word frequency statistics, word frequency is defined as more than the word setting threshold value the label of described content,
Described recommended content is labeled.
Described data recommendation computing formula is: the hot value of described content recommendation+(sending out of current time-recommended content
The cloth time)/the set time.
Described classification includes following characteristics:
The community users ascribed characteristics of population includes: sex, division of life span, and division of life span includes: unmarried, in love, parent-offspring, education
Deng；
Community users behavior interest, including: emotion, wedding celebration, parent-offspring, education, automobile, cuisines, tourism, fashion, investment, room
Product, number, job hunting, health etc.；
Community users geographical position, including: country, province, city, region；
Community users mutual-action behavior, including: post, money order receipt to be signed and returned to the sender, mark, collect, consulting etc.；
Community users purchasing behavior, including: place an order, payment etc..
Calculate weight order value according to described computing formula, obtain the corresponding weight order value of described recommended content, so
After further according to weight order value, recommended content carried out screening and sequencing, output meets being pushed away of the behavior characteristics of community users
Recommend content.
Weight order value is calculated, as shown in the chart according to described computing formula:
Table 1
Obtain the corresponding weight order value of described content, then carry out the screening of recommended content further according to weight order value
Sequence, output meets the recommended content of the behavior characteristics of community users.
As in figure 2 it is shown, the system of commending contents side based on community users behavior, described system includes data collection mould
Block, data filtering module, data analysis module, data memory module, data recommendation module；
Described data collection module includes server end and client, and described server end is for row storage, described client
Community users behavioural information, for generating the behavioural information of community users, is arranged in cookie by end by javascript,
In community users accesses station behind address, the value in cookie is sent to server, it is achieved adopting of community users behavioural information
Collection；
Described data filtering module includes server, has been used for the scanning to community users behavioural information and has filtered, has removed
It is not inconsistent the community users behavioural information of specification, and completes the arrangement to community users behavioural information；
Described data memory module includes server, for the community users behavioural information after filtering is stored,
Described data recommendation module is for the recommendation carrying out content for community users.
Specific embodiment is as follows: the comparison thrown in by industry region:
According to keyword throw in and compare:
The present invention is by precisely recommending, and recommended content improves 2 to 8 times than traditional clicking rate based on fixed position.
Pass through our clicking rate before and after using the present invention of the data to website to contrast, it can be seen that data are significantly increased
As it has been described above, after those of ordinary skill in the art reads file of the present invention, according to technical scheme and
Technology design makes other various corresponding conversion scheme without creative mental work, belongs to the model that the present invention is protected
Enclose.
Claims (5)
1. a content recommendation method based on community users behavior, it is characterised in that comprise the following steps: step 1: client
Receiving the request of community users input, the behavioural information of community users is arranged in the cookie of client by described client；
Step 2: the described behavioural information in described client cookie is sent to server by client；
Step 3: described described community users behavioural information in server is collected and analyzes；Again for the institute collected
State behavioural information and carry out pretreatment, to incomplete or do not meet the described behavioural information of specification, carry out clearing up, normalization,
Conversion, the community users behavioural information that integrated, stipulations operation forms specification；
Step 4: the described behavioural information of the specification in described server uploads and use hadoop to store；
Step 5: the described behavioural information of specification is classified；Set up different community users models, described community users mould
Community users is carried out point by type by the community users ascribed characteristics of population, behavior interest, geographical position, mutual-action behavior and purchasing behavior
Class；Drawn a portrait by one community users of community users model generation again；
Step 6: according to described community users portrait and the label of recommended content, uses data recommendation computing formula to mate
Calculating sequence, obtain the recommended content for community users, described recommended content and label thereof are to be stored in advance in service
Model content on device, merchant advertisement content, activity description content information.
A kind of content recommendation method based on community users behavior, it is characterised in that in step 6,
The label of described recommended content is specially and uses Lucene participle technique that described recommended content is carried out participle, after participle
Each word carry out word frequency statistics, word frequency is defined as the label of described content more than the word setting threshold value, to described quilt
Content recommendation is labeled.
A kind of content recommendation method based on community users behavior, it is characterised in that in step 6,
Described data recommendation computing formula is: the hot value of described content recommendation+(issuing time of current time-recommended content)/
Set time.
A kind of content recommendation method based on community users behavior, it is characterised in that according to described
Computing formula calculates weight order value, obtains the corresponding weight order value of described recommended content, then further according to weight order
Value carries out screening and sequencing to recommended content, and output meets the recommended content of the behavior characteristics of community users.
5. the system realizing claim 1 method, it is characterised in that described system includes data collection module, data mistake
Filter module, data analysis module, data memory module, data recommendation module；
Described data collection module, is stored in the behavioural information of community users in cookie by javascript technology, institute
State data module and be additionally operable in community users accesses station behind address, the behavioural information in cookie is sent to server, real
The collection of existing community users behavioural information；
Described community users behavioural information in server is collected and analyzes for described by described data filtering module, complete
The scanning of paired described behavioural information is filtered, and removes the community users behavioural information not being inconsistent specification, and completes to believe described behavior
The arrangement of breath；
Described data memory module, for storing the community users behavioural information after filtering, uses hadoop technology to carry out
Storage；
Described data recommendation module is used for using data recommendation computing formula to carry out matching primitives sequence, carries out for community users
The coupling of recommended content, brush select, sort and recommend.
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| CN107809485A (en) * | 2017-10-31 | 2018-03-16 | 广州云移信息科技有限公司 | Information recommendation method and terminal |
| CN108038237A (en) * | 2017-12-27 | 2018-05-15 | 广州市云润大数据服务有限公司 | A kind of information recommendation method and system |
| CN108269107A (en) * | 2016-12-30 | 2018-07-10 | 阿里巴巴集团控股有限公司 | User information processing method and processing device |
| CN108399249A (en) * | 2018-03-02 | 2018-08-14 | 上海精数信息科技有限公司 | Data normalization method, user's portrait providing method, equipment and storage medium |
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