CN102130934A - Method and system for recommending friends in social network site (SNS) community - Google Patents
Method and system for recommending friends in social network site (SNS) community Download PDFInfo
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Abstract
The invention provides a method and system for recommending friends in a social network site (SNS) community. The method comprises the following steps: acquiring behavior information of a user; generating a friend recommending list according to the behavior information combined with community information; and recommending the friend recommending list to the user. By using the method and system for recommending the friends in the SNS community, the probability for the user to find the friends can be greatly improved; and simultaneously, the method and the system can be initiatively provided to the user when the user logs in the community, which facilitates the user to select people possibly known with each other or desired to be known by the user, thereby enhancing the viscosity of the community so as to pull the vitality of friend relation in the community.
Description
[technical field]
The present invention relates to Internet technical field, relate in particular to the method and system of commending friends in a kind of SNS community.
[background technology]
Social network (Social Networking Service is called for short " SNS ") is a technology application architecture under the Web2.0 system.SNS carries out human resources and shares by the foundation of direct social friends between the friend, finish or solve concrete application problem in setting up the process of social relationships.SNS separates theoretical running based on six degree, in the human connection network, get to know any strange friend that is:, and is at most middle as long as just can achieve the goal by six friends.Separate theory according to six degree, each individual social circle all constantly amplifies, and becomes a catenet at last.
Along with the continuous growth of SNS community users quantity on the network, the user seeks the people who oneself is familiar with at one's side in the boundless and indistinct sea of faces difficult all the more.Traditional mode be the user by keyword search, for example the people's that is familiar with name is wanted in input, the personal information of filling in according to user in the SNS community is searched the user who is complementary with keyword and is recommended.This method depends on the personal information that the user fills in, and the personal information that the user fills in often can not react real situation, so accuracy is not high.In addition, as the user and when not knowing its partial information of wanting the people that is familiar with, then can't search.
[summary of the invention]
Based on this, be necessary to provide a kind of method that can improve commending friends in the SNS community of recommending accuracy
The method of commending friends may further comprise the steps: the behavioural information of obtaining the user in a kind of SNS community; According to described behavioural information,, generate the friend recommendation tabulation in conjunction with community information; The user is recommended in described friend recommendation tabulation.
This user's behavioural information can be user's an instant messaging buddy list, and described community information is the user's registration information of community, and the step of described generation friend recommendation tabulation specifically can be: the instant messaging buddy list that reads the user; Obtain the user who has registered in community in the described instant messaging buddy list, generate the friend recommendation tabulation.
This user's behavioural information can be the buddy list of user in community, and described community information is the community relations chain tabulation of community users, the step of described generation friend recommendation tabulation specifically: read the buddy list of user in community; Obtain the community relations chain tabulation of the user in the described buddy list, be the friend recommendation tabulation.
This user's behavioural information can be the IP address list that the user logins community, and described community information is the user's registration information of community, the step of described generation friend recommendation tabulation specifically: the IP address list that reads user's login community; Obtain the highest IP address of weight in the described IP address list; The IP address that obtains last login in the community is registered user's tabulation of the highest IP address of described weight, is the friend recommendation tabulation.
This user's behavioural information is user's an instant messaging group-list, and described community information is the user's registration information of community, the step of described generation friend recommendation tabulation specifically: the instant messaging group-list that reads the user; Obtain the group's user list in the described group-list; Obtain in described group's user list and tabulate in the community users of community's registration, be the friend recommendation tabulation.
And with the step that the user is recommended in friend recommendation tabulation specifically can be: after being removed, the good friend of community of the user in the described friend recommendation tabulation recommends the user.
The described step that the user is recommended in friend recommendation tabulation also comprises: described friend recommendation tabulation is sorted according to good friend's weight.
In addition, also be necessary to provide a kind of system that can improve commending friends in the SNS community of recommending accuracy.
The system of commending friends in a kind of SNS community comprises: user behavior information acquisition module is used to obtain user's behavioural information; The community information acquisition module is used to obtain community information; Friend recommendation tabulation generation module according to described user's behavioural information, and in conjunction with community information, generates the friend recommendation tabulation; Recommending module is recommended the user with the friend recommendation tabulation that generates.
This user's behavioural information is user's an instant messaging buddy list, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining user's instant messaging buddy list, obtain the user who has registered in community in the described instant messaging buddy list, generate the friend recommendation tabulation.
This user's behavioural information is the buddy list of user in community, described community information is the community relations chain tabulation of community users, described friend recommendation tabulation generation module is further used for obtaining the buddy list of user in community, obtains the community relations chain tabulation of the user in the described buddy list and tabulates as friend recommendation.
This user's behavioural information is the IP address list that the user logins community, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining the IP address list that the user logins community, obtain the highest IP address of weight in the described IP address list, the IP address that obtains last login in the community is that registered user's tabulation of the highest IP address of described weight is tabulated as friend recommendation.
This user's behavioural information is user's an instant messaging group-list, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining user's instant messaging group-list, obtain the group's user list in the described group-list, obtaining has tabulated in the community users of community's registration in described group's user list tabulates as friend recommendation.
And recommending module also can be used for recommending the user after the good friend of community with the user in the tabulation of described friend recommendation removes.
In addition, this system also can comprise: order module is used for described friend recommendation tabulation is sorted according to good friend's weight.
The method and system of commending friends in the above-mentioned SNS community, behavioural information by obtaining the user and in conjunction with community information, the tabulation of generation friend recommendation, user in this friend recommendation tabulation then is people relevant with the user and that may be familiar with, thereby by setting up the accuracy that relation between them has improved commending friends, improved the probability that the user finds friend greatly; Simultaneously, these method and system can initiatively offer the user when the user logins community, make things convenient for the user to select them may be familiar with or wish the people of understanding, have strengthened the viscosity of community, thereby have spurred the liveness of the good friend of community relation.
[description of drawings]
Fig. 1 is the flow chart of the method for commending friends in the SNS of the present invention community;
Fig. 2 is the flow chart of the method for commending friends in the SNS community in the execution mode;
Fig. 3 is the method flow diagram of commending friends in the SNS community among first embodiment;
Fig. 4 is the method flow diagram of commending friends in the SNS community among second embodiment;
Fig. 5 is the method flow diagram of commending friends in the SNS community among the 3rd embodiment;
Fig. 6 is the method flow diagram of commending friends in the SNS community among the 4th embodiment;
Fig. 7 is the structural representation of the system of commending friends in the SNS of the present invention community;
Fig. 8 is the structural representation of the system of commending friends in the SNS community in the execution mode.
[embodiment]
Fig. 1 shows the method flow of commending friends in the SNS community among the present invention, and this method flow detailed process is as follows:
In step S101, obtain user's behavioural information.In one embodiment, user's behavioural information comprises following one or more: buddy list in community of user's instant messaging buddy list, user, the IP address list that the user logins community, instant messaging group-list of user etc.
In step S102,,, generate the friend recommendation tabulation in conjunction with community information according to described behavioural information.In one embodiment, in conjunction with community information comprises that the registered user, user of community are in the pass of community tethers tabulation etc.
In step S103, the user is recommended in the friend recommendation tabulation.Good friend in the friend recommendation tabulation that is generated is with user-dependent, so the accuracy height of commending friends.
Fig. 2 shows in the execution mode method flow of commending friends in the SNS community, and this method flow detailed process is as follows:
In step S201, obtain user's behavioural information.
In step S202,,, generate the friend recommendation tabulation in conjunction with community information according to described behavioural information.
In step S203, remove the good friend of community of the user in the described friend recommendation tabulation.Owing to may comprise user's the good friend of community in the tabulation of the friend recommendation that generated, repeat to recommend the user for fear of this part user's the good friend of community, therefore need to remove the good friend of community of the user in the friend recommendation tabulation.
In step S204, the good friend's of community that removed the user friend recommendation tabulation is sorted according to good friend's weight.Good friend's weight here can be the number of times that occurs in friend recommendation tabulation, at information completely degree of community's registration or the like.
In step S205, the user is recommended in the tabulation of the friend recommendation after the ordering.Sort owing to friend recommendation being tabulated, can make the high ordering of weight forward, make the user obtain good friend maximally related the most fast by most convenient with it according to good friend's weight.
Fig. 3 shows the method flow of commending friends in the SNS community among first embodiment, and this method flow detailed process is as follows:
In step S301, read user's instant messaging buddy list.Among this embodiment, user's immediate communication tool is combined with SNS community, when the user logins SNS community, can obtain user's instant messaging buddy list.
In step S302, obtain the user who has registered in community in the described instant messaging buddy list, generate the friend recommendation tabulation.
In step S303, remove the good friend of community of the user in the described friend recommendation tabulation.For the good friend of user on immediate communication tool, wherein the good friend who has registered in community may be user's the good friend of community, therefore needs to remove this part user's the good friend of community, avoids repeating to recommend these good friends to give the user.
In step S304, the good friend's of community that removed the user described friend recommendation tabulation is sorted according to good friend's weight.This good friend's weight can be with the chat frequency of user on immediate communication tool, at the integrity degree of community's log-on message, in the personal information of community and user's the degree of correlation etc.For example, for user A, its instant messaging good friend comprises user B, user C and user D, wherein user B and user C have carried out the good friend of community that registration and user B and user C are not user A in community, detect the chat frequency ratio user C of user B and user A and the chat frequency height of user A, therefore user B is more forward than the ordering of user C in the friend recommendation tabulation.
In step S305, the user is recommended in the described friend recommendation tabulation after the ordering.
Fig. 4 shows among second embodiment the method flow of commending friends in the SNS community, and this method flow detailed process is as follows:
In step S401, read the buddy list of user in community.
In step S402, obtain the community relations chain tabulation of the user in the described buddy list.Write down the good friend of user in community good friend in this community relations chain tabulation.
In step S403, remove the good friend of community of the user in the described community relations chain tabulation.
In step S404, the good friend's of community that removed the user described community relations chain tabulation is sorted according to good friend's weight.This good friend's weight can be the number of times that occurs in community relations chain tabulation, at the integrity degree of community's fill data and in the information of community and user's the degree of correlation etc.
In step S405, the user is recommended in the described community relations chain tabulation after the ordering.
Fig. 5 shows the method flow of commending friends in the 3rd the SNS community among the embodiment, and this method flow detailed process is as follows:
In step S501, read the IP address list that the user logins community.Among this embodiment, can read the IP address list of the up-to-date login of user community, for example get the IP address list of nearest 10 login communities.
In step S502, obtain the highest IP address of weight in the described IP address list.Here the highest IP address of so-called weight is meant the IP address that occurrence number is maximum in the IP address list that obtains, and when occurrence number is all identical, then is the IP address of logining recently.
In step S503, the IP address that obtains last login in the community is registered user's tabulation of the highest IP address of described weight.Among the registered user of community, Deng Lu IP address is the highest IP address of this weight at last, illustrates that then this registered user may be the closer people of distance users, therefore is likely that the user wants the people who is familiar with very much.
In step S504, remove the good friend of community of the user in described registered user's tabulation.Avoid repeating recommending.
In step S505, described registered user's tabulation of the good friend of community that removed the user is sorted according to good friend's weight.The integrity degree of this weight personal information that can be the registered user fill in community and degree of correlation of personal information and user etc.
In step S506, the user is recommended in the described registered user's tabulation after the ordering.
Fig. 6 shows the method flow of commending friends in the 4th the SNS community among the embodiment, and this method flow detailed process is as follows:
In step S601, read user's instant messaging group-list.Among this embodiment, immediate communication tool is combined with SNS community, and read the group-list that the user is added in immediate communication tool.
In step S602, obtain the group's user list in the described group-list.This group user list has write down all users among the group of user place, owing to be among the same group, therefore is correlated with mutually.
In step S603, obtain in group user list and tabulate in the community users of community's registration.
In step S604, remove the good friend of community of the user in the described community users tabulation.
In step S605, the good friend's of community that removed the user described community users tabulation is sorted according to good friend's weight.The integrity degree of the personal information that this weight can be the number of times that occurs in the tabulation of described community users, fill in community and community's personal information and user's the degree of correlation etc.
In step S606, the user is recommended in the described community users tabulation after the ordering.
Fig. 7 shows among the present invention the system of commending friends in the SNS community, and this system comprises user behavior information acquisition module 10, community information acquisition module 20, friend recommendation tabulation generation module 30 and recommending module 40.Wherein:
User behavior information acquisition module 10 is used to obtain user's behavioural information.As mentioned above, user's behavioural information comprises following one or more: buddy list in community of user's instant messaging buddy list, user, the IP address list that the user logins community, user's instant messaging group-list.
Community information acquisition module 20 is used to obtain community information.Community information comprises that the registered user, user of community are in the pass of community tethers tabulation etc.
Friend recommendation tabulation generation module 30 is used for the behavioural information according to the user, and in conjunction with community information, generates the friend recommendation tabulation.
Recommending module 40 is used for the user is recommended in the friend recommendation tabulation that generates.Can be when the user logins SNS community the user be recommended in the friend recommendation tabulation, make the user login SNS community and can see the relative people that is familiar with of maybe may wanting.
Fig. 8 shows in the execution mode system of commending friends in the SNS community, this system is except comprising above-mentioned user behavior information acquisition module 10, community information acquisition module 20, friend recommendation tabulation generation module 30 and recommending module 40, also comprise order module 50, wherein: order module 50 is used for the friend recommendation tabulation that generates is sorted according to good friend's weight.In one embodiment, recommending module 40 also is used for recommending the user after the good friend of community with the user of friend recommendation tabulation removes, and avoids repeating recommending.
In one embodiment, 30 of generation modules of friend recommendation tabulation are further used for obtaining user's instant messaging buddy list, obtain the user who has registered in community in the described instant messaging tabulation, generate the friend recommendation tabulation.Before recommending, 50 of order module according to chat frequency on immediate communication tool of instant messaging good friend and user, instant messaging good friend the integrity degree of community's fill data and personal information and user's degree of correlation equal weight to friend recommendation tabulation sort.
In one embodiment, friend recommendation tabulation generation module 30 is further used for obtaining the buddy list of user in community, obtains the community relations chain tabulation of the user in the described buddy list, and this community relations chain tabulation is the friend recommendation tabulation.This friend recommendation list records the good friend of user the good friend of community.The number of times that 50 of order module can occur in the friend recommendation tabulation according to the good friend, good friend are tabulated to friend recommendation at the integrity degree of community's fill data and personal information and user's degree of correlation equal weight and are sorted.
In one embodiment, friend recommendation tabulation generation module 30 is further used for obtaining the IP address list that the user logins community, obtain the IP address that weight is the highest in the IP address list, obtain of the registered user tabulation of the IP address of last login in the community, then be the friend recommendation tabulation for the highest IP address of this weight.Wherein, the IP address that weight is the highest can be the IP address that occurrence number is maximum in the IP address list, when the number of times that occurs when the IP address is identical, then can go to the IP address of login recently.Among this embodiment, can read the IP address list that the user logins community recently, the IP address list of for example nearest 10 login communities.Order module 50 can according to the good friend the integrity degree of community's fill data and personal information and user's degree of correlation equal weight to friend recommendation tabulation sort.
In one embodiment, friend recommendation tabulation generation module 30 is further used for obtaining user's instant messaging group-list, obtain the group's user list in the group-list, obtain in group user list and tabulate in the community users of community's registration, then tabulate as friend recommendation.Among this embodiment, immediate communication tool is combined with SNS community, and read the group-list that the user is added in immediate communication tool, obtain all users of user place group, the user that these users registered in community then gives the user as friend recommendation.The integrity degree of the personal information that 50 of order module are filled in according to the number of times that occur in described community users tabulation, in community and community's personal information and user's degree of correlation equal weight are tabulated to friend recommendation and are sorted.
The above embodiment has only expressed several execution mode of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection range of patent of the present invention should be as the criterion with claims.
Claims (14)
1. the method for commending friends in the SNS community may further comprise the steps:
Obtain user's behavioural information;
According to described behavioural information,, generate the friend recommendation tabulation in conjunction with community information;
The user is recommended in described friend recommendation tabulation.
2. the method for commending friends in the SNS according to claim 1 community, it is characterized in that, described user's behavioural information is user's an instant messaging buddy list, and described community information is the user's registration information of community, the step of described generation friend recommendation tabulation specifically:
Read user's instant messaging buddy list;
Obtain the user who has registered in community in the described instant messaging buddy list, generate the friend recommendation tabulation.
3. the method for commending friends in the SNS according to claim 1 community, it is characterized in that, described user's behavioural information is the buddy list of user in community, and described community information is the community relations chain tabulation of community users, the step of described generation friend recommendation tabulation specifically:
Read the buddy list of user in community;
Obtain the community relations chain tabulation of the user in the described buddy list, be the friend recommendation tabulation.
4. the method for commending friends in the SNS according to claim 1 community, it is characterized in that, described user's behavioural information is the IP address list that the user logins community, and described community information is the user's registration information of community, the step of described generation friend recommendation tabulation specifically:
Read the IP address list of user's login community;
Obtain the highest IP address of weight in the described IP address list;
The IP address that obtains last login in the community is registered user's tabulation of the highest IP address of described weight, is the friend recommendation tabulation.
5. the method for commending friends in the SNS according to claim 1 community, it is characterized in that, described user's behavioural information is user's an instant messaging group-list, and described community information is the user's registration information of community, the step of described generation friend recommendation tabulation specifically:
Read user's instant messaging group-list;
Obtain the group's user list in the described group-list;
Obtain in described group's user list and tabulate in the community users of community's registration, be the friend recommendation tabulation.
6. according to the method for commending friends in any described SNS community in the claim 1 to 5, it is characterized in that, the step that the user is recommended in the friend recommendation tabulation specifically:
Recommend the user after the good friend of the community removal with the user in the described friend recommendation tabulation.
7. according to the method for commending friends in any described SNS community in the claim 1 to 5, it is characterized in that the described step that the user is recommended in the friend recommendation tabulation also comprises:
Described friend recommendation tabulation is sorted according to good friend's weight.
8. the system of commending friends in the SNS community is characterized in that described system comprises:
User behavior information acquisition module is used to obtain user's behavioural information;
The community information acquisition module is used to obtain community information;
Friend recommendation tabulation generation module according to described user's behavioural information, and in conjunction with community information, generates the friend recommendation tabulation;
Recommending module is recommended the user with the friend recommendation tabulation that generates.
9. the system of commending friends in the SNS according to claim 8 community, it is characterized in that, described user's behavioural information is user's an instant messaging buddy list, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining user's instant messaging buddy list, obtain the user who has registered in community in the described instant messaging buddy list, generate the friend recommendation tabulation.
10. the system of commending friends in the SNS according to claim 8 community, it is characterized in that, described user's behavioural information is the buddy list of user in community, described community information is the community relations chain tabulation of community users, described friend recommendation tabulation generation module is further used for obtaining the buddy list of user in community, obtains the community relations chain tabulation of the user in the described buddy list and tabulates as friend recommendation.
11. the system of commending friends in the SNS according to claim 8 community, it is characterized in that, described user's behavioural information is the IP address list that the user logins community, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining the IP address list that the user logins community, obtain the highest IP address of weight in the described IP address list, the IP address that obtains last login in the community is that registered user's tabulation of the highest IP address of described weight is tabulated as friend recommendation.
12. the system of commending friends in the SNS according to claim 8 community, it is characterized in that, described user's behavioural information is user's an instant messaging group-list, described community information is the user's registration information of community, described friend recommendation tabulation generation module is further used for obtaining user's instant messaging group-list, obtain the group's user list in the described group-list, obtaining has tabulated in the community users of community's registration in described group's user list tabulates as friend recommendation.
13. the system of commending friends in any described SNS community in 12 is characterized in that according to Claim 8, described recommending module also is used for recommending the user after the good friend of community with the user of described friend recommendation tabulation removes.
14. the system of commending friends in any described SNS community in 12 according to Claim 8 is characterized in that described system also comprises:
Order module is used for described friend recommendation tabulation is sorted according to good friend's weight.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101020288A CN102130934A (en) | 2010-01-20 | 2010-01-20 | Method and system for recommending friends in social network site (SNS) community |
| SG2012052627A SG182566A1 (en) | 2010-01-20 | 2010-12-24 | Method and system for recommending friends in social networking service (sns) community |
| PCT/CN2010/080246 WO2011088723A1 (en) | 2010-01-20 | 2010-12-24 | Method and system for recommending friends in social networking service (sns) community |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101020288A CN102130934A (en) | 2010-01-20 | 2010-01-20 | Method and system for recommending friends in social network site (SNS) community |
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| CN102130934A true CN102130934A (en) | 2011-07-20 |
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| CN2010101020288A Pending CN102130934A (en) | 2010-01-20 | 2010-01-20 | Method and system for recommending friends in social network site (SNS) community |
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| Country | Link |
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| CN (1) | CN102130934A (en) |
| SG (1) | SG182566A1 (en) |
| WO (1) | WO2011088723A1 (en) |
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| CN102664828A (en) * | 2012-04-26 | 2012-09-12 | 复旦大学 | System and method for friend recommendation in social network service (SNS) network |
| CN102811179A (en) * | 2012-03-29 | 2012-12-05 | 北京淘友天下科技发展有限公司 | Information provision method and system for social network |
| CN102937995A (en) * | 2012-11-23 | 2013-02-20 | 北京小米科技有限责任公司 | Mutual information processing method and device |
| CN103020055A (en) * | 2011-09-20 | 2013-04-03 | 腾讯科技（深圳）有限公司 | Processing method and device for user recommendation |
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| CN103123627A (en) * | 2011-11-21 | 2013-05-29 | 腾讯科技（深圳）有限公司 | Method and device for user relationship chain expanding in social networking service (SNS) community |
| CN103188353A (en) * | 2011-12-31 | 2013-07-03 | 北京神州泰岳软件股份有限公司 | Method providing contact persons for user in internetwork communication tool and system providing contact persons for user in internetwork communication tool |
| WO2013123830A1 (en) * | 2012-02-24 | 2013-08-29 | 腾讯科技（深圳）有限公司 | Recommendation method and system for microblog users and computer storage medium |
| WO2013139239A1 (en) * | 2012-03-21 | 2013-09-26 | Tencent Technology (Shenzhen) Company Limited | Method for recommending users in social network and the system thereof |
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| SG182566A1 (en) | 2012-08-30 |
| WO2011088723A1 (en) | 2011-07-28 |
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