CN102955805B - Recommendation data of website information processing method and system - Google Patents
Recommendation data of website information processing method and system Download PDFInfo
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- CN102955805B CN102955805B CN201110247866.9A CN201110247866A CN102955805B CN 102955805 B CN102955805 B CN 102955805B CN 201110247866 A CN201110247866 A CN 201110247866A CN 102955805 B CN102955805 B CN 102955805B
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Abstract
This application provides a kind of recommendation data of website information processing method, including: obtain the operation of nominator recommended website information, and in receiving the user record recommended, record nominator's information；The recommendation chain of site information is determined according to the nominator's information in user record；According to recommending the user profile of each user in chain, the recommending data of site information is added up.Present invention also provides a kind of recommendation data of website information realizing preceding method and process system.The recommendation data of website information processing method of the application and system, it is possible to the recommending data of site information is added up and ensured the accuracy of statistical data.
Description
Technical field
The application relates to computer network data technical field, particularly relates to a kind of recommendation data of website information processing method and system.
Background technology
Along with the development of technology, network social intercourse has been increasingly becoming a kind of new social mode, and network social intercourse develops into present various social network sites (SNS, SocialNetworkSites) from initial Email.Under normal circumstances, social network sites needs user to apply for the registration of on corresponding website, and fills in relevant personal information, thus obtaining individual's account.When user logs in social network sites, individual's account and relevant information just becomes website or other people identify the primary identity of user identity.
In order to promote the information in social network sites, social network sites always wants to user can recommend other users by the site information that oneself accesses, such as video, commodity etc..In order to improve the enthusiasm of user, social network sites also can arrange some awards and encourage to recommend.Such as current a lot of group buying websites, it is stipulated that if product successful referral is given other users by a certain user, the rebating of certain amount of money can be obtained.Such as certain group buying websites is at selling film ticket, and this link is told B by QQ, msn or other modes by A, and then B connects one film ticket of purchase according to this, then A, by this website rebating of acquisition, can be used for it and consumes on the web site.But, this kind of way of recommendation is only limitted to two person-to-person mutual recommendations, if product is through multistage recommendation, also can only get last two person-to-person recommendation informations.When website is when carrying out follow-up work, for instance, it is desirable to the recommending data of all site informations is added up when obtaining relevant information, require to look up substantial amounts of data, increase timing statistics, and, because information record is imperfect, statistical data also there will be error.
Summary of the invention
Technical problems to be solved in this application are to provide a kind of recommendation data of website information processing method and system, it is possible to the recommending data of site information is added up and ensured the accuracy of statistical data.
In order to solve the problems referred to above, this application discloses a kind of recommendation data of website information processing method, comprise the following steps:
Obtain the operation of nominator recommended website information, and in receiving the user record recommended, record nominator's information；
The recommendation chain of site information is determined according to the nominator's information in user record；
According to recommending the user profile of each user in chain, the recommending data of site information is added up.
Further, the described nominator's information that records in receiving the user record recommended includes:
When user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information；
When this site information of user's subsequent access, do not change its original nominator's information.
Further, described record nominator's information includes:
By the information record of current nominator in the user record of its recommended.
Further, the described recommendation chain determining site information includes:
Choose a user, determine forward nominator step by step, until nominator is the user actively accessing site information.
Further, described record nominator's information includes:
To recommend transmission information and current nominator's information record in the user record of its recommended before current nominator.
Further, the described recommendation chain determining site information includes:
Choosing a user, the recommendation transmission acquisition of information from the user record of this user recommends chain.
Further, the described operation obtaining nominator recommended website information includes:
Obtain nominator and replicate the operation of site information correspondence link, nominator's information is added in link, from described link, obtains this nominator's information；Or
Obtain nominator and click the operation of corresponding button in website, generate the recommendation message including nominator's information, from described recommendation message, obtain this nominator's information.
Further, described according to recommending the user profile of each user in chain, the recommending data of site information is carried out statistics and includes:
Obtain and recommend the feedback information of each user in chain, the user with identical feedback information is divided into a class；Or
Obtain and recommend the positional information of each user in chain, according to each user the feedback information of site information added up site information in each place by acceptance level.
Further, described acquisition recommends the feedback information of each user in chain, also includes after the user with identical feedback is divided into a class:
All types of user preference is obtained according to classification results；
Choose all types of user according to user preference and wish that it is shown by the site information obtained.
Further, described method also includes:
Obtain and recommend each user feedback information to site information in chain；
Determine according to field feedback and effectively recommend chain and effective nominator.
In order to solve the problems referred to above, disclosed herein as well is a kind of site information commending system, including:
Nominator's data obtaining module, for obtaining the operation of nominator recommended website information, and records nominator's information in receiving the user record recommended；
Chain is recommended to determine module, for determining the recommendation chain of site information according to the nominator's information in user record；
Statistical module, for according to recommending the user profile of each user in chain, adding up the recommending data of site information.
Further, described nominator's data obtaining module includes:
Nominator's information recording unit, for when user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information；When this site information of user's subsequent access, do not change its original nominator's information.
Further, described nominator's information recording unit includes:
Current nominator's information record subelement, is used for the information record of current nominator in the user record of its recommended.
Further, described recommendation chain determines that module includes:
First searches unit, determines forward nominator step by step for receiving, from last, the user recommended according to each user record, until nominator is the user actively accessing site information.
Further, described nominator's information recording unit includes:
Recommend transmission information recording unit, for recommending transmission information and current nominator's information record in the user record of its recommended before current nominator.
Further, described recommendation chain determines that module includes:
Second searches unit, recommends chain and nominator for receiving the recommendation transmission acquisition of information the user record recommended from last.
Further, described statistical module includes:
Taxon, recommends the feedback information of each user in chain for obtaining, the user with identical feedback is divided into a class；Or
Location information acquiring unit, recommends the positional information of each user in chain for obtaining, according to each user the feedback information of site information added up site information in each place by acceptance level.
Further, described system also includes:
Effective nominator determines module, for obtaining each user feedback information to site information in recommendation chain, and determines effectively recommendation chain and effective nominator according to field feedback.
Compared with prior art, the application includes advantages below:
The recommendation data of website information processing method of the application and system are passed through nominator's information record in receiving the user record recommended, can when follow-up data be added up, a selected user, as do not carried out the user recommended, and determine forward nominator step by step, just the recommendation chain of site information can be obtained, and carry out the statistics of recommending data according to the chain of recommending obtained, thus realizing a large amount of recommending datas of site information are processed, and, each user participating in recommending by recommending the mode of chain to find, thus ensureing the accuracy of statistical data, owing to the application carries out statistical analysis on the basis of recommendation chain, rather than the user profile of users all in website is carried out statistical analysis, alleviate the data volume of statistical analysis, save the workload of web station system data analysis, improve the efficiency of statistical analysis.
Secondly, it is divided into a class by recommending the user providing identical feedback information in chain, to determine user preference, its site information wishing to obtain can be provided the user with for user preference when subsequent operation, ensure the accuracy that site information provides, to reduce the number of times that user actively searches, thus reducing the burden of Website server.
Additionally, by to recommending chain to be analyzed, obtain the positional information of each user, and according to the feedback information of user judge site information in each place by acceptance level, such that it is able to be easy to determine the emphasis that some site information information is issued, save the process that website is investigated and analysed again, it is to avoid system resource waste.
Certainly, the arbitrary product implementing the application is not necessarily required to reach all the above advantage simultaneously.
Accompanying drawing explanation
Fig. 1 is the flow chart of the recommendation data of website information processing method embodiment one of the application；
Fig. 2 is the flow chart of the nominator's information recording method in the recommendation data of website information processing method shown in the application Fig. 1；
Fig. 3 is the flow chart of the recommendation data of website information processing method embodiment two of the application；
Fig. 4 is the structural representation of the site information commending system embodiment one of the application；
Fig. 5 is the structural representation of the site information commending system embodiment two of the application.
Detailed description of the invention
Understandable for enabling the above-mentioned purpose of the application, feature and advantage to become apparent from, below in conjunction with the drawings and specific embodiments, the application is described in further detail.
With reference to Fig. 1, it is shown that a kind of recommendation data of website information processing method embodiment one of the application, comprise the following steps:
Step 101, obtains the operation of nominator recommended website information, and records nominator's information in receiving the user record recommended.
Site information can be a certain commodity on website, a certain video or hot news etc.; when website wishes this site information is promoted; recommended links would generally be increased on site information; thus facilitating user after browsing or find this site information, this site information is recommended other users.
Can pass through to replicate the link of this site information when site information is recommended by nominator, and pass to other users by the mode such as instant messenger or mail.In order to record nominator's information; would generally (such as URL (URL in link; UniformResourceLocator) information of this nominator) is added; when needing record nominator's information in receiving the user record recommended, then directly can obtain from this link.Wherein, nominator's information can be user's user's number of registration (registration ID) on website, it can also be the machine code etc. of the IP address of the user terminal that user uses or user terminal, nominator's information can be directly recorded in the cookie receiving the user recommended, can also store in the server, when needed, then can directly read from user cookie or server.
It addition, website can also preset button, it is recommended that person is by after clicking corresponding button, and website just automatically generates recommendation message, and the information of nominator is added in recommendation message.This recommendation message can pass to other users by the mode of instant messaging, it is also possible to is directly published in social network sites, when other presentees click this recommendation message, and just record nominator's information in receiving the user record recommended.
The frequent amendment to data caused because repeating record in order to avoid nominator's information, occupying system resources and cause data redundancy, when carrying out nominator's information record, nominator's information when only record user accesses this site information first time, if and this user follow-up is because when this site information is conducted interviews by the recommendation accepting other nominators, then will not be recorded.
With reference to Fig. 2, concrete can be achieved in that
D1, when user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information.
Wherein, it is judged that with or without nominator, it is possible to by whether this user profile there being nominator's information determine, if there being nominator's information, then it is assumed that there is nominator, otherwise, then do not have.
D2, when this site information of user's subsequent access, does not change its original nominator's information.
During this site information of user's subsequent access, regardless of whether be recommend or oneself actively access through others, all without changing its original recommendation information.Such as, when user accesses some websites information first time, it does not have nominator, now, recording its nominator's information for sky, even if this this site information of user's subsequent access is the recommendation through others, its nominator's information remains as sky.
If site information is through multistage recommendation, then the information of every one-level nominator can be recorded successively.Wherein, by the information record of current nominator in the user record of its recommended, namely the information of each nominator is recorded only in the user record of its recommended, so can reduce the data volume of record, thus alleviating the burden of server.Additionally, the information of nominator can also adopt the mode record recommending chain, to recommend transmission information and current nominator's information record in the user record of its recommended before current nominator, namely receive, at each, the recommendation transmission information also recorded while record nominator's information in the user record recommended before site information.
Step 102, determines the recommendation chain of site information according to the nominator's information in each user record.
Determine that the recommendation chain of site information can be accomplished by: choose a user, determine forward nominator step by step, until nominator is the user actively accessing site information.Wherein, the user chosen can be that any one receives the user recommended, and in order to avoid repeating, identical recommendation chain only records once.Such as, a site information, A recommend B, B and recommend C, C and recommend D.If choosing user B, the recommendation chain then determined is A-> B, if and choose D, the recommendation chain then determined is A-> B-> C-> D, it is possible to find out that A-> B is the part repeated, it is possible to choose longer chain of recommending and cover short recommendation chain, thus avoiding the occurrence of repetition, it is ensured that the accuracy of statistical data.
Alternatively, it is also possible to choose a user, the recommendation transmission information from the user record of this user directly obtains recommendation chain.Certainly, if there is the recommendation chain repeated, it would however also be possible to employ aforesaid method, with long chain of recommending, short recommendation chain is covered.
Step 103, according to recommending the user profile of each user in chain, adds up the recommending data of site information.
User profile includes user's feedback information and customer position information etc. to site information.The recommending data of site information is carried out statistics include: according to recommending the feedback information in chain, user is classified.Such as, the user with identical feedback is divided into a class.Such as, in a recommendation chain A-> B-> C-> D-> E-> F-> G-> H, A, D, G have purchased this commodity, all the other people are only by recommending, then think that A, D, G are exactly the crowd of common interest, they are divided into a class, and all the other carry out the user that recommends, as, B, C, E, F, it is considered as the crowd of common interest, it is possible to be divided into a class.Further, it is also possible to add up each user at the different number of times recommending to occur in chain to determine its interest to a certain site information, number of times is divided into a class in set point.It is, of course, also possible to user is classified according to other modes, this also applies for this is not limiting as.Each user participating in recommending by recommending the mode of chain to find, so that the statistics of recommending data is more accurate.And owing to the embodiment of the present application is to carry out statistical analysis on the basis of recommendation chain, rather than the user profile of users all in website is carried out statistical analysis, alleviate the data volume of statistical analysis, save the workload of web station system data analysis, improve the efficiency of statistical analysis.
After user is classified, it is possible to analyze all types of user preference, thus providing the user with its site information wishing to obtain for user's classification, reduce the number of times that user actively searches, thus reducing taking and the burden of Website server site resource.
It addition, the recommending data of site information is carried out statistics can also be: obtain and recommend the positional information of each user in chain, according to each user the feedback information of site information added up site information in each place by acceptance level.Such as, user A recommends certain commodity to the friend of its U.S., but unmanned purchase, be on the contrary A a friend this commercial product recommending give one Europe friend, generation is sold fast, such that it is able to infer that these commodity are that Europe is relatively popular, then later can using Europe as by the main promoting region of these commodity.
Furthermore it is also possible to chain will be recommended to be shown in the accession page of each nominator, consequently facilitating user understands oneself position in recommending chain.Further, it is also possible to the user that site information provides expection feedback in recommending chain carries out labelling, and is shown in the accession page of each nominator by the chain of recommending after labelling, consequently facilitating user understands the user profile with it with common hobby.Such as, for the user A of social network sites, the user of common interest is had to include 3 parts with A: to buy the direct good friend of the A of these commodity；Recommend chain is bought the user of these type of commodity relevant with A, represent the good friend of good friend；And other bought the user of these commodity, represent other users.
The recommendation data of website information processing method of the application and system are passed through nominator's information record in receiving the user record recommended, can when follow-up data be added up, a selected user, as do not carried out the user recommended, and determine forward nominator step by step, just the recommendation chain of site information can be obtained, and carry out the statistics of recommending data according to the chain of recommending obtained, thus realizing a large amount of recommending datas of site information are processed, and, each user participating in recommending by recommending the mode of chain to find, thus ensureing the accuracy of statistical data.
Preferably, with reference to Fig. 3, it is shown that the recommendation data of website information processing method embodiment two of the application, further comprising the steps of after step 103:
Step 201, the field feedback in chain of recommending according to obtaining determines effectively recommendation chain and effective nominator.
If a certain site information has been made intended feedback through the recommendation of other users by a certain user, then thinks and is this time recommended as effective recommendation.Such as, site information is commodity, website wishes that user buys this commodity, so user buys these commodity and then thinks site information has been made intended feedback, if site information is video, website is wished that user clicks and is browsed this video, then user's click browses this video and then thinks site information has been made intended feedback.The recommendation chain that so composition is effectively recommended is for effectively recommending chain, and effectively recommending all nominators in chain is effective nominator.
Now, according to aforesaid two kinds of nominator's information recording method formulas, effectively recommend chain and effective nominator can be obtained by following two kinds of methods.If the information of each nominator is recorded only in the user record of its recommended, so can pass through the mode searched step by step, the user recommended is received from last, namely the user of expection feedback is provided, determine forward nominator step by step, until finding the user actively accessing site information, i.e. first nominator, the path of this search procedure is effectively recommends chain, and in search procedure, determined all nominators are effective nominator.If each receives the nominator recommending to determine in search procedure is effective nominator, described all effective nominators are according to the recommendation transmission information that have recorded in the user record that the path that recommendation order forms is recommendation chain above, so can directly receive the user record recommended directly obtains from last and recommend chain, recommend in transmission information, namely recommending all users comprised in chain is all effective nominators, adopt in this way, it is possible to reduce inquiry times, thus improving response speed.
Such as, one commodity, by A recommend B, B recommend C, C recommend give D, and only C finally have purchased these commodity, so, it is recommended that chain is: A-> B-> C-> D, effectively recommendation chain is: A-> B-> C, wherein, A and B is considered as effective nominator.
Further, after the step 201 of embodiment two, it is also possible to comprise the following steps:
Set allocation rule, carry out all effective nominators rewarding distribution.
Award can be preset to carry out according to website, for instance increases user integral or returns cash etc. mode.When all effective nominators are rewarded, preset allocation rule, for instance, each effective nominator's mean allocation or according to recommending contribution to reward step by step, the award ratio namely having the different people recommending contribution obtained is different.
The recommendation contribution of each effective nominator can be determined according to contribution according to aforesaid effective recommendation chain when rewarding step by step.According to the mode successively decreased step by step, namely can effectively recommending the every one-level nominator on chain according to recommending contribution, the award ratio of its acquisition is different.The method of salary distribution rewarded step by step is adopted to can ensure that the reasonability rewarding distribution.Such as, E have purchased a certain commodity, the recommendation chain finding its correspondence is A-> B-> C-> D-> E, then it was determined that the recommendation that E is because D have purchased this commodity, then it is believed that the recommendation contribution of D is maximum, for first order nominator, C takes second place, for second level nominator, by that analogy.In addition it is also possible to determine the allocation proportion contributing one or two bigger nominators, follow-up nominator adopts identical ratio to be allocated.This kind of mode is when effective nominator's quantity is more, it is possible to reduce amount of calculation, thus reducing taking system resource.Such as, allocation rule is: the ratio that first order nominator distribution is bigger, and such as 50%, the second level nominator take second place, such as 30%, and the remaining remaining ratio of nominator's mean allocation.
With reference to Fig. 4, it is shown that the site information commending system of the embodiment of the present application one, including nominator's data obtaining module 10, chain is recommended to determine module 20 and statistical module 30.
Nominator's data obtaining module 10, for obtaining the operation of nominator recommended website information, and records nominator's information in receiving the user record recommended.
Chain is recommended to determine module 20, for determining the recommendation chain of site information according to the nominator's information in each user record.
Statistical module 30, for according to recommending the user profile of each user in chain, adding up the recommending data of site information.
Preferably, it is recommended that person's data obtaining module 10 includes nominator's information recording unit, for when user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information；When this site information of user's subsequent access, do not change its original nominator's information.
Preferably, it is recommended that person's information recording unit includes current nominator's information record subelement, it is used for the information record of current nominator in the user record of its recommended.Recommend chain to determine that module 20 includes the first lookup unit, determine forward nominator step by step for receiving, from last, the user recommended according to each user record, until nominator is the user actively accessing site information.
Preferably, it is recommended that person's information recording unit includes recommending transmission information record subelement, for recommending transmission information and current nominator's information record in the user record of its recommended before current nominator.Recommend chain to determine that module 20 includes the second lookup unit, recommend chain and nominator for receiving from last the recommendation transmission information the user record recommended obtains.
Preferably, statistical module 30 includes taxon, recommends the feedback information of each user in chain for obtaining, the user with identical feedback is divided into a class；Or location information acquiring unit, recommend the positional information of each user in chain for obtaining, according to each user the feedback information of site information added up site information in each place by acceptance level.
With reference to Fig. 5, it is preferable that system also includes effective nominator and determines module 50, for obtaining each user feedback information to site information in recommendation chain, and determine effectively recommendation chain and effective nominator according to field feedback.
Preferably, system also includes allocation rule and determines module, is used for setting allocation rule, carries out all effective nominators rewarding distribution.
Each embodiment in this specification all adopts the mode gone forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar part mutually referring to.For system embodiment, due to itself and embodiment of the method basic simlarity, so what describe is fairly simple, relevant part illustrates referring to the part of embodiment of the method.
Above recommendation data of website information processing method provided herein and system are described in detail, principle and the embodiment of the application are set forth by specific case used herein, and the explanation of above example is only intended to help and understands the present processes and core concept thereof；Simultaneously for one of ordinary skill in the art, according to the thought of the application, all will change in specific embodiments and applications, in sum, this specification content should not be construed as the restriction to the application.
Claims (16)
1. a recommendation data of website information processing method, it is characterised in that comprise the following steps:
Obtain the operation of nominator recommended website information, and in receiving the user record recommended, record nominator's information；
The recommendation chain of site information is determined according to the nominator's information in user record；Wherein, in described recommendation chain, first user is the user actively accessing site information, and in described recommendation chain, previous user creates recommendation behavior for a rear user；
According to recommending the user profile of each user in chain, the recommending data of site information is added up；
Wherein, the described nominator's information that records in receiving the user record recommended includes:
When user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information；
When this site information of user's subsequent access, do not change its original nominator's information.
2. the method for claim 1, it is characterised in that described record nominator's information includes:
By the information record of current nominator in the user record of its recommended.
3. method as claimed in claim 2, it is characterised in that the described recommendation chain determining site information includes:
Choose a user, determine forward nominator step by step, until nominator is the user actively accessing site information.
4. the method for claim 1, it is characterised in that described record nominator's information includes:
To recommend transmission information and current nominator's information record in the user record of its recommended before current nominator.
5. method as claimed in claim 4, it is characterised in that the described recommendation chain determining site information includes:
Choosing a user, the recommendation transmission acquisition of information from the user record of this user recommends chain.
6. the method for claim 1, it is characterised in that the operation of described acquisition nominator recommended website information includes:
Obtain nominator and replicate the operation of site information correspondence link, nominator's information is added in link, from described link, obtains this nominator's information；Or
Obtain nominator and click the operation of corresponding button in website, generate the recommendation message including nominator's information, from described recommendation message, obtain this nominator's information.
7. the method as described in any one of claim 1 to 6, it is characterised in that described according to recommending the user profile of each user in chain, carries out statistics to the recommending data of site information and includes:
Obtain and recommend the feedback information of each user in chain, the user with identical feedback information is divided into a class；Or
Obtain and recommend the positional information of each user in chain, according to each user the feedback information of site information added up site information in each place by acceptance level.
8. method as claimed in claim 7, it is characterised in that the feedback information of each user in chain is recommended in described acquisition, also includes after the user with identical feedback is divided into a class:
All types of user preference is obtained according to classification results；
Choose all types of user according to user preference and wish that it is shown by the site information obtained.
9. the method for claim 1, it is characterised in that described method also includes:
Obtain and recommend each user feedback information to site information in chain；
Determine according to field feedback and effectively recommend chain and effective nominator.
10. a site information commending system, it is characterised in that including:
Nominator's data obtaining module, for obtaining the operation of nominator recommended website information, and records nominator's information in receiving the user record recommended；
Chain is recommended to determine module, for determining the recommendation chain of site information according to the nominator's information in user record；Wherein, in described recommendation chain, first user is the user actively accessing site information, and in described recommendation chain, previous user creates recommendation behavior for a rear user；
Statistical module, for according to recommending the user profile of each user in chain, adding up the recommending data of site information；
Wherein, described nominator's data obtaining module includes:
Nominator's information recording unit, for when user accesses some websites information first time, it is judged that with or without nominator, if not having, then nominator's information of this user is recorded as sky, if having, then record nominator's information；When this site information of user's subsequent access, do not change its original nominator's information.
11. system as claimed in claim 10, it is characterised in that described nominator's information recording unit includes:
Current nominator's information record subelement, is used for the information record of current nominator in the user record of its recommended.
12. system as claimed in claim 11, it is characterised in that described recommendation chain determines that module includes:
First searches unit, determines forward nominator step by step for receiving, from last, the user recommended according to each user record, until nominator is the user actively accessing site information.
13. system as claimed in claim 10, it is characterised in that described nominator's information recording unit includes:
Recommend transmission information recording unit, for recommending transmission information and current nominator's information record in the user record of its recommended before current nominator.
14. system as claimed in claim 13, it is characterised in that described recommendation chain determines that module includes:
Second searches unit, recommends chain and nominator for receiving the recommendation transmission acquisition of information the user record recommended from last.
15. the system as described in any one of claim 10 to 14, it is characterised in that described statistical module includes:
Taxon, recommends the feedback information of each user in chain for obtaining, the user with identical feedback is divided into a class；Or
Location information acquiring unit, recommends the positional information of each user in chain for obtaining, according to each user the feedback information of site information added up site information in each place by acceptance level.
16. system as claimed in claim 10, it is characterised in that described system also includes:
Effective nominator determines module, for obtaining each user feedback information to site information in recommendation chain, and determines effectively recommendation chain and effective nominator according to field feedback.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110247866.9A CN102955805B (en) | 2011-08-24 | 2011-08-24 | Recommendation data of website information processing method and system |
| HK13104559.0A HK1177525A1 (en) | 2011-08-24 | 2013-04-16 | Method and system for processing website information recommendation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110247866.9A CN102955805B (en) | 2011-08-24 | 2011-08-24 | Recommendation data of website information processing method and system |
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| Publication Number | Publication Date |
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| CN102955805A CN102955805A (en) | 2013-03-06 |
| CN102955805B true CN102955805B (en) | 2016-06-29 |
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| CN201110247866.9A Active CN102955805B (en) | 2011-08-24 | 2011-08-24 | Recommendation data of website information processing method and system |
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| WO2016113958A1 (en) * | 2015-01-13 | 2016-07-21 | ソニー株式会社 | Information processing apparatus, information processing method, and program |
| CN105487861B (en) * | 2015-11-26 | 2018-11-27 | 珠海多玩信息技术有限公司 | The method and device of securing component calling amount |
| CN106846132A (en) * | 2017-04-12 | 2017-06-13 | 杭州纳戒科技有限公司 | Merchandise news method for pushing, device and system |
| CN112291578A (en) * | 2020-04-30 | 2021-01-29 | 许周 | Artificial intelligence-based e-commerce information pushing method and system and cloud platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1608268A (en) * | 2001-12-28 | 2005-04-20 | 皇家飞利浦电子股份有限公司 | Method of enabling e-commerce |
| CN101103368A (en) * | 2004-11-30 | 2008-01-09 | 阿诺·马索尼 | Open system for dynamically generating a network of contacts |
| CN101464983A (en) * | 2007-12-18 | 2009-06-24 | 汤溪蔚 | Electronic commercial matters application method and system |
| CN101944135A (en) * | 2010-10-22 | 2011-01-12 | 道有道(北京)科技有限公司 | Information processing system and method |
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| US20090197681A1 (en) * | 2008-01-31 | 2009-08-06 | Microsoft Corporation | System and method for targeted recommendations using social gaming networks |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1608268A (en) * | 2001-12-28 | 2005-04-20 | 皇家飞利浦电子股份有限公司 | Method of enabling e-commerce |
| CN101103368A (en) * | 2004-11-30 | 2008-01-09 | 阿诺·马索尼 | Open system for dynamically generating a network of contacts |
| CN101464983A (en) * | 2007-12-18 | 2009-06-24 | 汤溪蔚 | Electronic commercial matters application method and system |
| CN101944135A (en) * | 2010-10-22 | 2011-01-12 | 道有道(北京)科技有限公司 | Information processing system and method |
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| HK1177525A1 (en) | 2013-08-23 |
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