CN105574131B - Social network friend making recommendation method and system based on dynamic community identification - Google Patents
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Abstract
The invention discloses a social network friend making recommendation method and system based on dynamic community identification, and belongs to the technical field of the Internet. The system function mainly consists of the following four parts: a community identification module, a community classification module, a friend recommendation module and a result display module. The social network friend making recommendation method based on the dynamic community identification comprises the following steps of: a first step, obtaining a user two-degree friend list from a user design relation database, carrying out community identification, and meanwhile calculating a community correlation index; a second step, classifying obtained communities based on a calculation result of the community correlation index; and a third step, recommending friends according to the community classification result, and displaying a friend recommendation result through the community category. The method is used for finding and recommending possible interested people of the user by the method of dynamic community identification and community attribute analysis.
Description
Technical field
The invention belongs to Internet technical field, and in particular to a kind of social networks recognized based on dynamic corporations is made friends and pushed away
Recommend method and system.
Background technology
User's similarity measurement that the friend recommendation method of current social networks and software is based primarily upon in social networks comes
Recommendation, representational product and function can knowable people, the People You May Know of Facebook including Tencent QQ
Deng.The common feature of these methods is：More potential good friend is more likely recommended for common friend number.
But, in actual applications, the effect of social networks friend recommendation is poor.Existing social networks friend recommendation side
Method is faced with a predicament：The people of recommendation recognizes, but is unwilling to add as a friend.This has two：(1) common friend
The more potential good friend of number, the uninterested part of user in the ripe corporations that often user participates in.For example, Yong Hujia
The classmate of the 80% of class of senior middle school is used as good friend, then the common friend number with remaining 20% is very high, but may this remain
Under 20% user be unwilling to add as a friend；Again for example, user has added the colleague of unit 80%, but remaining 20% leader
It is unwilling to add as a friend；(2) conversely, the less potential good friend of some common friend numbers, it may be possible to because corporations are formed.Example
Such as, the college class of new admission,, in understanding and familiar stage mutually, such potential good friend's common friend number is not for everybody
Height, is but on the contrary that user is interested.These reasons are caused based on the inaccurate of the friend recommendation of similarity measurement jointly
Really.
The content of the invention
For the defect for overcoming above-mentioned prior art to exist, it is an object of the invention to provide a kind of known based on dynamic corporations
Other social networks is made friends and recommends method and system.
The present invention is to be achieved through the following technical solutions：
The invention discloses a kind of social networks friend-making recommendation method recognized based on dynamic corporations, is comprised the following steps：
Step one, obtains two degree of buddy lists of user from user's design relation database, carries out corporations' identification, while meter
Calculate corporations' correlation metric；
Resulting corporations, based on corporations' correlation metric result of calculation, are classified by step 2；
Step 3, according to corporations' classification results, carries out friend recommendation, and shows friend recommendation result by corporations' classification.
Two degree of buddy lists of user are obtained from user's design relation database described in step one, corporations' identification is carried out, is had
Gymnastics conduct：
1) good friend's ID lists of targeted customer are obtained, is the once buddy list of the user；The every of targeted customer is obtained again
Good friend's ID lists of individual good friend, are two degree of buddy lists of the user；Merge once buddy list and two degree of buddy lists, if depositing
Repeating, then deleting the record in two degree of buddy lists, the list after merging is being whole good friend's ID lists；
2) in judging whole good friend's ID lists, friend relation is whether there is between any two ID, builds adjacency matrix；
3) based on adjacency matrix, corporations' identification is carried out to the network of whole good friend ID lists composition, exports each ID category
In corporations.
Calculating corporations correlation metric described in step one, refers to that the corporations belonged to for each ID calculate corporations' relation close
The relation of degree, the Connection Density for averagely setting up duration, user and corporations of corporations' relation and user and corporations averagely sets up duration.
Corporations' correlation metric computational methods are as follows：
Friend relation number * 2/ [ID numbers * (ID numbers -1 in corporations) in corporations] in corporations' relation density=corporations；
The friend relation number set up in duration sum/corporations for averagely setting up relation in duration=corporations of corporations' relation；
User and the Connection Density=user of corporations and ID numbers in the friend relation number/corporations of ID in corporations；
User averagely sets up duration=user and sets up duration sum/use with the friend relation of ID in corporations with the relation of corporations
The friend relation number of ID in family and corporations.
It is as follows that step 2 carries out classification concrete operations to corporations：
1) duration is averagely set up based on corporations' relation density and corporations' relation, during corporations are divided into ripe corporations, are grown up
Corporations and initial corporations；
Wherein, define corporations relations averagely set up duration more than 180 days for ripe corporations；When corporations' relation is averagely set up
Length less than or equal to 180 days but was corporations in growth more than 30 days；Corporations' relation averagely sets up duration
Initial corporations；
2) a length of corporations' classification when averagely being set up with the relation of corporations based on the Connection Density and user of user and corporations, will
Corporations are divided into user and have been enter into corporations, user and are going into corporations and the uncorrelated corporations of user；
Wherein, define user and have been enter into corporations for user more than 0.6 with the Connection Density of corporations；User and the company of corporations
Density is connect more than 0.1 but corporations is going into for user less than or equal to 0.6；User is less than or equal to the Connection Density of corporations
0.1 for the uncorrelated corporations of user.
Friend recommendation is carried out according to corporations' classification results described in step 3, concrete operations are：
When corporations uncorrelated for user of corporations, any operation is not triggered；
When corporations have been enter into corporations for user, any operation is not triggered；
When corporations are corporations or initial corporations in growing up, and when being going into corporations for user, according to incorporator and use
The common friend number at family number, sort from high to low, recommend common friend number highest before 10 to 100 potential good friends；
When corporations are ripe corporations, and when being going into corporations for user, according to the common friend number of user number,
Sort from high to low, recommend 1 to 3 potential good friends before common friend number highest；
After contacting with these potential good friends' foundation, then gradually show that other are dived from more to few order according to common friend number
In good friend.
Friend recommendation result is shown by corporations' classification described in step 3, concrete operations are：
Displaying formed corporations recommendation results when, user can disposably see first 10 to 100 it is potential good
Friend, and these potential good friends can be added as a friend；
And when showing the recommendation results of the corporations being going into, user can only see first 1 to 3 potential good friends, other
Potential good friend's gray scale displaying, does not show complete information, can not operate, with the increasing of good friend's quantity that user adds in this corporation
Gradually relax control more.
The invention also discloses a kind of social networks friend-making commending system recognized based on dynamic corporations, the system includes：
Corporations' identification module, for obtaining two degree of buddy lists of user from user social contact relational database, carries out corporations
Identification, and calculate corporations' correlation metric；
Corporations' sort module, for classifying to the corporations for identifying；
Friend recommendation module, for recommending resulting corporations' classification；
As a result display module, according to corporations' classification friend recommendation result is shown.
Corporations' sort module is based on calculating the calculated corporations' correlation metric of corporations identification modules to the society that identifies
Group is classified.
Friend recommendation module is recommended based on resulting corporations' category result.
Compared with prior art, the present invention has following beneficial technique effect：
The social networks friend-making recommendation method recognized based on dynamic corporations disclosed by the invention, first from from user's design pass
It is acquisition two degree of buddy lists of user in database, carries out corporations' identification, while calculates corporations' correlation metric；Secondly, it is based on
Resulting corporations are classified by corporations' correlation metric result of calculation；Again, according to corporations' classification results, good friend is carried out
Recommend, be that user recommends to be going into the member of corporations and initial corporations, recommendation results reflect the current social interests of user；
Again, this method can avoid recommending the old relation of out-of-date corporations to user, it is to avoid recommend it to be introduced into the big of corporations to user
Magnitude relation, it is to avoid bring privacy leakage；Finally, friend recommendation result is shown by corporations' classification.The inventive method is by dynamic
Corporations recognize and corporations' attributive analysis method finding and recommended user may people interested；Push away with existing good friend
Recommend algorithm " recommend can knowable people " is different, the inventive method " recommend may be interested people ", for the current society of user
Hand over interest to be recommended, recommend accuracy rate higher, and avoid excessive user and harass and privacy leakage.
The invention also discloses the system that method is recommended in above-mentioned friend-making can be realized, systemic-function is mainly by following four part
Composition：Corporations' identification module, corporations' sort module, friend recommendation module and result display module.First, corporations' identification module
Two degree of buddy lists of user are obtained from user social contact relational database, carries out corporations' identification, and carry out some corporations' keys referring to
Target is calculated.Based on result of calculation, corporations' sort module is classified to resulting corporations.Friend recommendation module is based on gained
To corporations' classification recommended.Finally, as a result display module shows friend recommendation result according to corporations' classification.
Description of the drawings
Fig. 1 is building-block of logic of the present invention based on the social networks friend-making commending system of dynamic corporations' identification；
Fig. 2 is buddy list explanatory diagram involved in the present invention；
Fig. 3 is social networks friend-making recommendation results displaying figure of the present invention based on dynamic corporations' identification.
Wherein, 101 is corporations' identification module；102 is corporations' sort module；103 is friend recommendation module；104 is result
Display module.
Specific embodiment
With reference to specific embodiment, the present invention is described in further detail, it is described be explanation of the invention and
It is not to limit.
The invention discloses a kind of social networks friend-making commending system recognized based on dynamic corporations, its functional structure is as schemed
Shown in 1, wherein, 101 is corporations' identification module；102 is corporations' sort module；103 is friend recommendation module；104 is result exhibition
Show module.
Systemic-function is mainly made up of following four part：Corporations' identification module, corporations' sort module, friend recommendation module with
And result display module.
Based on the social networks friend-making recommendation method that dynamic corporations recognize, comprise the following steps：
Step one, obtains two degree of buddy lists of user from user's design relation database, carries out corporations' identification, while meter
Calculate corporations' correlation metric；
Resulting corporations, based on corporations' correlation metric result of calculation, are classified by step 2；
Step 3, according to corporations' classification results, carries out friend recommendation, and shows friend recommendation result by corporations' classification.
Below concrete example explanation is carried out to the work of each module：
1st, corporations' identification module
The first step, obtains good friend's ID lists of targeted customer, the referred to as once good friend ID lists of the user, the A in such as Fig. 2
B C D.Good friend's ID lists of each good friend of user, referred to as the two of the user degree good friend ID lists, the C D E F G in Fig. 2；
Merge once good friend ID lists and two degree of good friend's ID lists, such as run into duplicate keys, delete the record in two degree of good friend's ID lists, such as
C D in Fig. 2 are referred to only as once good friend.List after merging is referred to as whole good friend's ID lists, the A B C D E in such as Fig. 2
F G。
Second step, judges to whether there is friend relation between any two ID in whole good friend's ID lists, builds adjacent square
Battle array.For example, the value of (i, j) position of matrix is represented for 1 and there is friend relation between i-th ID and j-th ID；0 representative is not deposited
In friend relation.
3rd step, based on adjacency matrix, the network of whole good friend ID lists composition carries out corporations identification (Community
Detection).The existing ripe technical scheme of corporations' identification, specifically may be referred to the methods such as common Modularity ([1]
Newman,M.E.J.2004."Fast algorithm for detecting community structure in
networks,"Physical Review E(69:6),p 066133.；[2]Newman M E J.Modularity and
community structure in networks[J].Proceedings of the National Academy of
Sciences,2006,103(23):8577-8582.).After the completion of corporations' identification, the corporations that each ID belongs to are exported.For example, count
It is probably that ID 1001,1002,1003 belongs to corporations 1 to calculate result；1004,1005,1006 belong to corporations 2.
4th step, be that each corporation calculates corporations' relation density, and corporations' relation averagely sets up duration, user and corporations
Connection Density, user averagely sets up duration with the relation of corporations.Wherein：
Friend relation number * 2/ (ID numbers * (ID numbers -1 in corporations) in corporations) in corporations' relation density=corporations
The friend relation number set up in duration sum/corporations for averagely setting up relation in duration=corporations of corporations' relation
User and the Connection Density=user of corporations and ID numbers in the friend relation number/corporations of ID in corporations
User averagely sets up duration=user and sets up duration sum/use with the friend relation of ID in corporations with the relation of corporations
The friend relation number of ID in family and corporations.
It is exemplified below, it is assumed that ID includes 1001,1002,1003,1004 totally 4 in corporations, and the relation for existing includes 1001-
1002 (20 days), 1001-1003 (20 days), 1002-1004 (50 days), 1003-1004 (30 days)；There is good friend in user and 1001
, there is friend relation (30 days) with 1003 in relation (20 days).
So can calculate：
Corporations' relation density=4*2/ (4*3)=0.67
Corporations' relation averagely sets up duration=(20+20+50+30)/4=30
User and the Connection Density=2/4=0.5 of corporations
User averagely sets up duration=(20+30)/2=25 with the relation of corporations.
2nd, corporations' sort module
1) this module is primarily based on a length of corporations' classification when averagely setting up of corporations' relation density of calculating, corporations' relation,
It is divided into corporations, the class of initial corporations three in ripe corporations, growth.
A kind of possible sorting technique is：
Corporations' relation averagely set up duration more than 180 days：Referred to as ripe corporations；
Corporations' relation averagely sets up duration less than or equal to 180 days but more than 30 days：Corporations in referred to as growing up.
Corporations' relation averagely set up duration less than or equal to 30 days：Referred to as initial corporations.
2) a length of corporations when this module is also averagely set up based on user with the relation of the Connection Density, user and corporations of corporations
Classification, is divided into user and has been enter into corporations, user and be going into corporations and the uncorrelated corporations of user.
A kind of possible method is：
User is more than 0.6 with the Connection Density of corporations：Referred to as user has been enter into corporations
User links density more than 0.1 but less than or equal to 0.6 with corporations：Referred to as user is going into corporations
User links density less than or equal to 0.1 with corporations：Referred to as uncorrelated corporations of user.
3rd, friend recommendation module
When corporations uncorrelated for user of corporations, any operation (secret protection) is not triggered；
When corporations have been enter into corporations for user, any operation (avoiding excessively recommending old relation) is not triggered；
When corporations or initial corporations and user are going into corporations during corporations are for growth, according to incorporator with user's
Common friend number number, sort from high to low, recommend the greater number of potential good friend of common friend number highest；
When corporations are going into corporations for ripe corporations and user, according to the common friend number of user number, from
High to Low sequence, recommends the small number of potential good friend of common friend number highest；After contacting with these potential good friends' foundation,
Gradually show other potential good friends from more to few order according to common friend number again.
4th, recommendation results display module
As a result above-mentioned calculated recommendation results are showed user by display module.Referring to Fig. 3, displaying is formed
Corporations recommendation results when, user can disposably see substantial amounts of potential good friend, and can add these potential good friends preferably
Friend；And when showing the recommendation results of the corporations being going into, user can only see the potential good friend of minority, other potential good
Friendly gray scale shows, does not show complete information, also cannot operate, with user in this corporation plus increasing for good friend's quantity and
Gradually relax control.
Displaying formed corporations recommendation results when, user can disposably see first 10 to 100 it is potential good
Friend, and these potential good friends can be added as a friend；
And when showing the recommendation results of the corporations being going into, user can only see first 1 to 3 potential good friends, other
Potential good friend's gray scale displaying, does not show complete information, can not operate, with the increasing of good friend's quantity that user adds in this corporation
Gradually relax control more.
Claims (4)
1. it is a kind of based on dynamic corporations recognize social networks friend-making recommendation method, it is characterised in that comprise the following steps：
Step one, obtains two degree of buddy lists of user from user's design relation database, carries out corporations' identification, while calculating society
Group's correlation metric；
Two degree of buddy lists of user are obtained in the design relation database from user, corporations' identification is carried out, concrete operations are：
1) good friend's ID lists of targeted customer are obtained, is the once buddy list of the user；Each for obtaining targeted customer again is good
Good friend's ID lists of friend, are two degree of buddy lists of the user；Merge once buddy list and two degree of buddy lists, if there is weight
It is multiple, then the record in two degree of buddy lists is deleted, the list after merging is whole good friend's ID lists；
2) in judging whole good friend's ID lists, friend relation is whether there is between any two ID, builds adjacency matrix；
3) based on adjacency matrix, corporations' identification is carried out to the network of whole good friend ID lists composition, exports what each ID belonged to
Corporations；
Described calculating corporations correlation metric, refers to that the corporations belonged to for each ID calculate corporations' relation density, corporations' relation
The Connection Density for averagely setting up duration, user and corporations and the relation of user and corporations averagely set up duration；Specifically, corporations
Correlation metric computational methods are as follows：
Friend relation number * 2/ [ID numbers * (ID numbers -1 in corporations) in corporations] in corporations' relation density=corporations；
The friend relation number set up in duration sum/corporations for averagely setting up relation in duration=corporations of corporations' relation；
User and the Connection Density=user of corporations and ID numbers in the friend relation number/corporations of ID in corporations；
The relation of user and corporations averagely set up duration=user and the friend relation of ID in corporations set up duration sum/user with
The friend relation number of ID in corporations；
Resulting corporations, based on corporations' correlation metric result of calculation, are classified by step 2；
Step 3, according to corporations' classification results, carries out friend recommendation, and shows friend recommendation result by corporations' classification.
2. it is according to claim 1 based on dynamic corporations recognize social networks friend-making recommendation method, it is characterised in that step
It is as follows that rapid two pairs of corporations carry out classification concrete operations：
1) duration is averagely set up based on corporations' relation density and corporations' relation, corporations during corporations are divided into ripe corporations, are grown up
With initial corporations；
Wherein, define corporations relations averagely set up duration more than 180 days for ripe corporations；It is little that corporations' relation averagely sets up duration
In equal to 180 days but more than 30 days for growth in corporations；Corporations' relation averagely sets up duration less than or equal to 30 days for initial
Corporations；
2) a length of corporations' classification when averagely being set up with the relation of corporations based on the Connection Density and user of user and corporations, by corporations
It is divided into user and has been enter into corporations, user and is going into corporations and the uncorrelated corporations of user；
Wherein, define user and have been enter into corporations for user more than 0.6 with the Connection Density of corporations；User is close with the connection of corporations
Degree more than 0.1 but is going into corporations less than or equal to 0.6 for user；User is less than or equal to 0.1 with the Connection Density of corporations
For the uncorrelated corporations of user.
3. it is according to claim 2 based on dynamic corporations recognize social networks friend-making recommendation method, it is characterised in that step
Friend recommendation is carried out according to corporations' classification results described in rapid three, concrete operations are：
When corporations uncorrelated for user of corporations, any operation is not triggered；
When corporations have been enter into corporations for user, any operation is not triggered；
When corporations are corporations or initial corporations in growing up, and when being going into corporations for user, according to incorporator with user's
Common friend number number, sort from high to low, recommend common friend number highest before 10 to 100 potential good friends；
When corporations are ripe corporations, and when being going into corporations for user, according to the common friend number of user number, from height
To low sequence, recommend the potential good friend of 1 to 3 before common friend number highest；
After contacting with these potential good friends' foundation, then gradually show that other are potential good from more to few order according to common friend number
Friend.
4. it is according to claim 3 based on dynamic corporations recognize social networks friend-making recommendation method, it is characterised in that step
Friend recommendation result is shown by corporations' classification described in rapid three, concrete operations are：
During the recommendation results of the corporations that displaying is being formed, user can disposably see first 10 to 100 potential good friends, and
These potential good friends can be added as a friend；
And when showing the recommendation results of the corporations being going into, user can only see first 1 to 3 potential good friends, other potential
Good friend's gray scale show, do not show complete information, can not operate, with user in this corporation plus increasing for good friend's quantity and
Gradually relax control.
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| CN106570082B (en) * | 2016-10-19 | 2019-11-05 | 浙江工业大学 | A kind of friends method for digging of combination network topology characteristic and user behavior characteristics |
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| CN103294733A (en) * | 2012-03-05 | 2013-09-11 | 北京千橡网景科技发展有限公司 | Method and system used for recommending friends to users |
| CN104601438A (en) * | 2014-04-28 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Friend recommendation method and device |
| CN104838410A (en) * | 2015-02-11 | 2015-08-12 | 深圳市仁盟互动网络科技有限公司 | Method and system for establishing friendship based on trust recommendation social networking services |
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| US9224150B2 (en) * | 2007-12-18 | 2015-12-29 | Napo Enterprises, Llc | Identifying highly valued recommendations of users in a media recommendation network |
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| CN103294733A (en) * | 2012-03-05 | 2013-09-11 | 北京千橡网景科技发展有限公司 | Method and system used for recommending friends to users |
| CN104601438A (en) * | 2014-04-28 | 2015-05-06 | 腾讯科技（深圳）有限公司 | Friend recommendation method and device |
| CN104838410A (en) * | 2015-02-11 | 2015-08-12 | 深圳市仁盟互动网络科技有限公司 | Method and system for establishing friendship based on trust recommendation social networking services |
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