CN104462308A - Method and system for recommending friends in social network - Google Patents
Method and system for recommending friends in social network Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000003542 behavioural effect Effects 0.000 claims description 85
- 230000006855 networking Effects 0.000 claims description 18
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000002354 daily effect Effects 0.000 description 32
- 230000008859 change Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 230000013011 mating Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 235000006694 eating habits Nutrition 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses a social network friend recommendation method and system. The method comprises the following steps: the method comprises the steps of collecting behavior characteristic data of a user, sending the behavior characteristic data to a social network, analyzing the behavior characteristic data to obtain personal information of the user, comparing the personal information of the user with personal information of other social network users, and finding out a social network user matched with the personal information of the user. By the technical scheme, the authenticity of the personal information of the user in the social network can be improved, and the accuracy of friend matching is improved.
Description
Technical field
The present invention relates to Internet technical field, particularly relate to the method and system of commending friends in social networks.
Background technology
Along with the develop rapidly of Internet technology, occurred the diversified social networks such as microblogging, school net, Facebook, usual user needs to fill in personal information when registering these social networks, comprises the information such as residence, school, personal character, hobby.The friend recommendation function of existing social networks is all that the personal information of input when registering based on user carries out degree of correlation coupling mostly, to recommend other users of identical hobby, same city or identical school.But a lot of user is very random when filling in these personal information, or be disinclined to fill in, or it is long deliberately to keep away its short exhibition, causes the user profile that obtains untrue comprehensive, causing the friend recommendation of social networks inaccurate; On the other hand, the personal information of user may change along with the change of time, and such as residence change, hobby change etc., affect the accuracy of friend recommendation further.
Summary of the invention
The object of the invention is to the method and system proposing social networks friend recommendation, improve the authenticity of userspersonal information in social networks, improve the accuracy of good friend's coupling.
For reaching this object, the present invention by the following technical solutions:
A method for commending friends in social networks, comprising:
Gather the behavioural characteristic data of user;
Send described behavioural characteristic data to social networks;
Analyze the personal information that described behavioural characteristic data draw this user;
The personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
Preferably, described characteristic comprises: the place classification information that user comes in and goes out, movable information and daily daily life information, and wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate information;
The described behavioural characteristic data of described analysis draw the personal information of this user, comprising:
Analyze the hobby type that described place classification information draws this user;
Analyze the type of sports that described movable information draws this user preference;
Analyze the daily work and rest rule that described daily daily life information draws this user.
Preferably, the behavioural characteristic data of described collection user, comprising:
The behavioural characteristic data of user are gathered according to the first setting-up time cycle;
The described behavioural characteristic data of described transmission, to social networks, comprising:
The described personal information of this user is uploaded to social networks according to the second setting-up time cycle;
The described second setting-up time cycle is more than or equal to the described first setting-up time cycle.
Preferably, before the described behavioural characteristic data to social networks of described transmission, comprising:
Behavioural characteristic data upload social networks is authorized.
Preferably, the described behavioural characteristic data of described transmission, to social networks, comprising:
Upload described behavioural characteristic data to preset service device, forward described behavioural characteristic data by described preset service device.
Preferably, the described behavioural characteristic data of described analysis also comprise after drawing the personal information of this user:
The type marking described personal information is True Data.
Preferably, the personal information of this user of described comparison and other social network user, find out the social network user mated with the personal information of this user, comprising:
The personal information of this user of comparison and other social network user, draws the matching degree of the personal information of other social network user and the personal information of this user;
Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user;
Described commending friends is sent to described user.
Preferably, the personal information of this user of described comparison and other social network user, find out the social network user mated with the personal information of this user, comprising:
Receive good friend's matching condition of setting;
The personal information of this user of comparison and other social network user, draws the matching degree of other social network user and this user under described good friend's matching condition;
Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user;
Described commending friends is sent to described user.
The present invention also provides the system of commending friends in a kind of social networks on the other hand, comprise: Intelligent worn device, the social networking service device communicated with described Intelligent worn device, described Intelligent worn device comprises data acquisition unit and data upload unit, and described social networking service device comprises analytic unit and matching unit;
Described data acquisition unit, for gathering the behavioural characteristic data of user;
Described data upload unit, for sending described behavioural characteristic data to described social networking service device;
Described analytic unit, draws the personal information of this user for analyzing described behavioural characteristic data;
Described matching unit, for the personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
Preferably, described characteristic comprises: the place classification information that user comes in and goes out, movable information and daily daily life information, and wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate;
Described analytic unit, draws the hobby type of this user specifically for analyzing described place classification information; Analyze the type of sports that described movable information draws this user; Analyze the daily work and rest rule that described daily daily life information draws this user.
Preferably, described data acquisition unit, specifically for gathering the behavioural characteristic data of user according to the first setting-up time cycle;
Described data upload unit, specifically for uploading the described personal information of this user to described social networking service device according to the second setting-up time cycle;
Wherein, the described second setting-up time cycle is more than or equal to the described first setting-up time cycle.
Preferably, described Intelligent worn device also comprises granted unit, before sending described behavioural characteristic data to social networks, authorizes behavioural characteristic data upload social networks.
Preferably, described data upload unit, specifically for uploading described behavioural characteristic data to preset service device, described social networking service device is to behavioural characteristic data described in described preset service device acquisition request.
Preferably, described analytic unit, also for after analyzing described behavioural characteristic data and drawing the personal information of this user, the type marking described personal information is True Data.
Preferably, described matching unit, specifically for the personal information of this user of comparison and other social network user, draws the matching degree of the personal information of other social network user and the personal information of this user; Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user; Described commending friends is sent to described user.
Preferably, described matching unit, specifically for receiving good friend's matching condition of setting; The personal information of this user of comparison and other social network user, draws the matching degree of other social network user and this user under described good friend's matching condition; Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user; Described commending friends is sent to described user.
Implement the embodiment of the present invention, there is following beneficial effect:
The embodiment of the present invention is by gathering the behavioural characteristic data of user, send described behavioural characteristic data to social networks, analyze the personal information that described behavioural characteristic data draw this user, the personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.The solution of the present invention due to gather user behavior feature data types come from individual long-term action, be convenient to the correct understanding of user oneself on the one hand, also eliminate the trouble that user manually upgrades personal information on the one hand, the more important thing is the authenticity that improve userspersonal information in social networks; When opening friend recommendation function or the active searching friend of social networks, the personal information higher based on validity can match the good friend more conformed to, and matching way is more accurate, improves the accuracy of friend recommendation.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing described below is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of the method for commending friends in the social networks of first embodiment of the invention.
Fig. 2 is the structural representation of the system of commending friends in the social networks of third embodiment of the invention.
Embodiment
Carry out clear, complete description below in conjunction with accompanying drawing of the present invention to the technical scheme in the embodiment of the present invention, obviously, described embodiment is only a part of embodiment of the present invention, instead of whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under the prerequisite of not making creative work, all belongs to the scope of protection of the invention.
First embodiment
Fig. 1 is the method flow diagram of commending friends in the social networks of first embodiment of the invention, and details are as follows for the first embodiment:
Step S101, gathers the behavioural characteristic data of user.
In a first embodiment, described characteristic can comprise: the place classification information that user comes in and goes out, movable information and daily daily life information, wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate and feed calorie.
Dress the behavioural characteristic data of the equipment such as wrist-watch, bracelet Real-time Collection user by intelligence in the present embodiment.This kind of intelligent wearable device need meet hardware condition: possess multiple sensors, by each sensor collection different user data.Such as possess the communications module such as processor, positioning module, action sensor, heart rate sensor, bluetooth.Described positioning module, positioning module can obtain the place classification that user often comes in and goes out, such as library, arena, park, countryside, various concrete training seminar, health club, market etc.; Action sensor can obtain the motion recording of user, comprises the body-building information such as run duration, exercise intensity and motion frequency; The daily schedule that heart rate sensor can obtain, comprise between WA in morning and the information such as the sleep quality in night; The communications module such as bluetooth are then for uploading the behavioural characteristic data collected.Because the user behavior feature data types gathered by Intelligent worn device comes from individual long-term action, be convenient to the correct understanding of user oneself on the one hand, reduce the difficulty that user fills in personal information, also improve the authenticity of userspersonal information in social networks simultaneously.
It should be noted that, the sensor of other types can be added according to actual conditions, such as, can obtain the sensor of the calorie situation of the daily feed of user, or select other Intelligent worn device, as intelligent glasses, intelligent ring etc., with the behavioural characteristic data of Real-time Collection user.
In first embodiment, gather the behavioural characteristic data of user according to the first setting-up time cycle by Intelligent worn device.Such as per half an hour or one hour gather a user behavior characteristic, and the user behavior characteristic of collection is more, more easily reflects the behavioural characteristic of user.
Step S102, sends described behavioural characteristic data to social networks.
In first embodiment, because described behavioural characteristic data may relate to the individual private data of user, so before transmission data, need to authorize behavioural characteristic data upload social networks, when only having by sending authority checking, just send described behavioural characteristic data to social networks, if not, refusal uploads described behavioural characteristic data.
In the present embodiment, upload the described personal information of this user to social networks according to the second setting-up time cycle, and the described second setting-up time cycle is more than or equal to the described first setting-up time cycle.Such as every day sends once described behavioural characteristic data, by automatically sending behavioural characteristic data, the current personal information of user can be adjusted at any time and update to social networks, namely eliminating the difficulty that user manually inputs personal information, turn avoid the trouble that user revises personal information.
As the present embodiment one preferred implementation, when sending described behavioural characteristic data, can unify to upload described behavioural characteristic data to preset service device, social networking service device can to behavioural characteristic data described in described preset service device acquisition request.When the corresponding multiple social networks of this preset service device, the communication flows that Intelligent worn device sends data can be saved, save Internet resources.
Step S103, analyzes the personal information that described behavioural characteristic data draw this user.
In the present embodiment, analyze the place classification that user often comes in and goes out, the place classifications such as such as library, arena, park, countryside, various concrete training seminar, health club, market, can show whether this user likes to read, often go to court and like what motion, speciality training seminar kind etc., to a certain degree can reflect the hobby type of user, such as motion, amusement, travelling, cuisines, reading and/or shopping etc.; The type of sports of this user preference can be drawn by analyzing the movable information of user, practicing in such as morning exercises, evening, aerobic exercise (motion that Yoga, Tai Ji etc. are releived) and/or intense physical exercises (row the boat, motion that surfing, apparatus work etc. are more violent); The daily work and rest rule of this user is drawn, the custom such as such as, to go to bed early and get up early time, often stay up late by analyzing the daily daily life information of user; These data inherently can receive publicity in friend-making process.
Preferably, mark by analyzing the personal information drawn in the present embodiment, the type marking this kind of personal information is True Data, to distinguish and the personal information by the manual typing of user, is conducive to the open of social network information and transparent management.
Drawn by the daily behavior custom comprehensively analyzing user, behavioural characteristic and the hobby of user can be reacted more really, also be convenient to the feature that user holds oneself more accurately to a certain extent, user also can be avoided manually to input the inconvenience of personal information on the other hand.
Step S104, the personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
In a first embodiment, when opening friend recommendation function or the active searching friend of social networks, by the personal information of this user of comparison and other social network user, draw the matching degree of the personal information of other social network user and the personal information of this user, to match the friend recommendation that more conforms to oneself to described user, matching way is more accurate, improves the accuracy of friend recommendation.
As a preferred implementation, social networks can mate for user automatically according to the personal information of user recommends the good friend that hobby is similar or habits and customs are similar, social networks backstage, by the personal information of this user of comparison and other social network user, draws the matching degree of the personal information of other social network user and the personal information of this user; And then to find out the social network user setting quantity before described matching degree sorts from high to low be the commending friends of this user.Such as sort from high to low by described matching degree, finding out the social network user coming 10 is above the commending friends of this user; Described commending friends is sent to described user.
As another preferred implementation, user can sets itself good friend coupling adjustment, such as mate according to hobby, or mating according to eating habit, or mating etc. according to distance, after receiving good friend's matching condition of user's setting, by the personal information of this user of comparison and other social network user, draw the matching degree of other social network user and this user under described good friend's matching condition; Finding out the social network user setting quantity before described matching degree sorts from high to low is the commending friends of this user, such as, sort from high to low by described matching degree, and finding out the social network user coming 10 is above the commending friends of this user; Described commending friends is sent to described user.User can also set the combination of multiple match and regulate, such as, search for the people within the scope of periphery certain distance with phase hobby, invites motion together or movable temporarily, provides a kind of faster social activity more easily and to form a team mode.
By above-mentioned first embodiment, by gathering the behavioural characteristic data of user, send described behavioural characteristic data to social networks, analyze the personal information that described behavioural characteristic data draw this user, be convenient to the correct understanding of user oneself on the one hand, also eliminate the trouble that user manually inputs personal information on the one hand, the more important thing is the authenticity that improve userspersonal information in social networks; When opening friend recommendation function or the active searching friend of social networks, the personal information higher based on validity can match the good friend more conformed to, and matching way is more accurate, improves the accuracy of friend recommendation.
Second embodiment
In the social networks that second embodiment provides, the system of commending friends, belongs to same design with above-mentioned embodiment of the method, the detail content of not detailed description in the embodiment of system, can with reference to said method embodiment.
Fig. 2 shows the system architecture schematic diagram of commending friends in the social networks of second embodiment of the invention, is described in detail below.
Described system comprises: Intelligent worn device 10, the social networking service device 20 communicated with described Intelligent worn device 10, described Intelligent worn device 10 comprises data acquisition unit 101 and data upload unit 102, and described social networking service device 20 comprises analytic unit 201 and matching unit 202. each several part, and details are as follows:
Described data acquisition unit 101, for gathering the behavioural characteristic data of user.
In second embodiment, described characteristic comprises: the place classification information that user comes in and goes out, movable information and daily daily life information etc.Wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate etc.Corresponding, described collecting unit 101 can comprise positioning module, action sensor module, heart rate sensor module etc.
Preferably, data acquisition unit 101 described in the present embodiment gathers the behavioural characteristic data of user according to the first setting-up time cycle. and such as per half an hour or one hour gather a user behavior characteristic, the user behavior characteristic gathered is more, more easily reflects the behavioural characteristic of user.
Described data upload unit 102, for sending described behavioural characteristic data to social networking service device 20.
In second embodiment, in order to ensure privacy and the security of userspersonal information, need through authority checking before sending user behavior characteristic, therefore described Intelligent worn device 10 also comprises granted unit, before sending described behavioural characteristic data to social networks, behavioural characteristic data upload social networks is authorized.If by authorizing, allow to upload described behavioural characteristic data, otherwise, forbid uploading described behavioural characteristic data.
As a preferred implementation, can upload described behavioural characteristic data to preset service device, described social networking service device 20 can to behavioural characteristic data described in described preset service device acquisition request.When the corresponding multiple social networking service device 20 of this preset service device, which can reduce the data upload load of Intelligent worn device 10, is conducive to saving network flow.
Preferably, in the present embodiment, data upload unit 102 uploads the described personal information of this user to social networks according to the second setting-up time cycle, and set the described second setting-up time cycle and be more than or equal to the described first setting-up time cycle, such as every day sends once described behavioural characteristic data.
Described analytic unit 201, draws the personal information of this user for analyzing described behavioural characteristic data.
In second embodiment, analytic unit 201 can be specifically for: analyze the hobby type that described place classification information draws this user.Such as: the place classification of often coming in and going out according to user, as place classifications such as library, arena, park, countryside, various concrete training seminar, health club, markets, analyze this user whether like read, often take a trip to court or, cuisines, shopping etc. hobby.Analytic unit 201 also can draw the type of sports of this user preference specifically for analyzing described movable information, such as, like morning exercises or motion in evening, or likes having the oxygen of releiving to move or violent intense physical exercises etc.; Analytic unit 201 also draws the daily work and rest rule of this user by analyzing described daily daily life information, such as rule of life, go to bed early and get up early or often stay up late.Drawn by the daily behavior custom comprehensively analyzing user, behavioural characteristic and the hobby of user can be reacted more really, also be convenient to the feature that user holds oneself more accurately to a certain extent, user also can be avoided manually to upgrade the inconvenience of personal information on the other hand.
Preferably, in the present embodiment, analytic unit 201 is also for after analyzing described behavioural characteristic data and drawing the personal information of this user, and the type marking described personal information is True Data.To make the userspersonal information in social networks more open and transparent, decrease the generation of network fraud behavior.
Described matching unit 202, for the personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
In second embodiment, social networking service device can be regularly user's commending friends according to the personal information of user automatically, by the personal information of this user of comparison and other social network user, draw the matching degree of the personal information of other social network user and the personal information of this user; Finding out the social network user setting quantity before described matching degree sorts from high to low is the commending friends of this user, such as find out matching degree sort from high to low front 10 social network user recommend this user.Certainly, user also can sets itself good friend coupling adjustment, such as mate according to hobby, or mating according to eating habit, or mating etc. according to distance, after social networking service device receives good friend's matching condition of user's setting, by the personal information of this user of comparison and other social network user, draw the matching degree of other social network user and this user under described good friend's matching condition; Finding out the social network user setting quantity before described matching degree sorts from high to low is the commending friends of this user, such as find out matching degree sort from high to low front 10 social network user recommend this user.User can also set the combination of multiple match and regulate, such as, search for the people within the scope of periphery certain distance with phase hobby, invites motion together or movable temporarily, provides a kind of faster social activity more easily and to form a team mode.
Above-mentioned second embodiment, based on the behavioural characteristic data gathering user, send described behavioural characteristic data to social networks, analyze the personal information that described behavioural characteristic data draw this user, be convenient to the correct understanding of user oneself on the one hand, also improve the authenticity of userspersonal information in social networks simultaneously; When opening friend recommendation function or the active searching friend of social networks, the personal information higher based on validity can match the good friend more conformed to, and matching way is more accurate, improves the accuracy of friend recommendation.
Above disclosedly be only present pre-ferred embodiments, certainly the right of the present invention can not be limited with this, therefore, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., still belong to the scope that the present invention is contained.
Claims (16)
1. the method for commending friends in social networks, is characterized in that, comprising:
Gather the behavioural characteristic data of user;
Send described behavioural characteristic data to social networks;
Analyze the personal information that described behavioural characteristic data draw this user;
The personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
2. the method for commending friends in social networks as claimed in claim 1, it is characterized in that, described characteristic comprises: the place classification information that user comes in and goes out, movable information and daily daily life information, wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate information;
The described behavioural characteristic data of described analysis draw the personal information of this user, comprising:
Analyze the hobby type that described place classification information draws this user;
Analyze the type of sports that described movable information draws this user preference;
Analyze the daily work and rest rule that described daily daily life information draws this user.
3. the method for commending friends in social networks as claimed in claim 1, it is characterized in that, the behavioural characteristic data of described collection user, comprising:
The behavioural characteristic data of user are gathered according to the first setting-up time cycle;
The described behavioural characteristic data of described transmission, to social networks, comprising:
The described personal information of this user is uploaded to social networks according to the second setting-up time cycle;
The described second setting-up time cycle is more than or equal to the described first setting-up time cycle.
4. the method for commending friends in social networks as claimed in claim 1, is characterized in that, before the described behavioural characteristic data to social networks of described transmission, comprising:
Behavioural characteristic data upload social networks is authorized.
5. the method for commending friends in social networks as claimed in claim 4, it is characterized in that, the described behavioural characteristic data of described transmission, to social networks, comprising:
Upload described behavioural characteristic data to preset service device, forward described behavioural characteristic data by described preset service device.
6. the method for commending friends in social networks as claimed in claim 1, it is characterized in that, the described behavioural characteristic data of described analysis also comprise after drawing the personal information of this user:
The type marking described personal information is True Data.
7. the method for commending friends in social networks as claimed in claim 1, is characterized in that the personal information of this user of described comparison and other social network user is found out the social network user mated with the personal information of this user, being comprised:
The personal information of this user of comparison and other social network user, draws the matching degree of the personal information of other social network user and the personal information of this user;
Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user;
Described commending friends is sent to described user.
8. the method for commending friends in social networks as claimed in claim 1, is characterized in that the personal information of this user of described comparison and other social network user is found out the social network user mated with the personal information of this user, being comprised:
Receive good friend's matching condition of setting;
The personal information of this user of comparison and other social network user, draws the matching degree of other social network user and this user under described good friend's matching condition;
Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user;
Described commending friends is sent to described user.
9. the system of commending friends in a social networks, it is characterized in that, comprise: Intelligent worn device, the social networking service device communicated with described Intelligent worn device, described Intelligent worn device comprises data acquisition unit and data upload unit, and described social networking service device comprises analytic unit and matching unit;
Described data acquisition unit, for gathering the behavioural characteristic data of user;
Described data upload unit, for sending described behavioural characteristic data to described social networking service device;
Described analytic unit, draws the personal information of this user for analyzing described behavioural characteristic data;
Described matching unit, for the personal information of this user of comparison and other social network user, finds out the social network user mated with the personal information of this user.
10. the system of commending friends in social networks as claimed in claim 9, it is characterized in that, described characteristic comprises: the place classification information that user comes in and goes out, movable information and daily daily life information, wherein, described movable information comprises run duration, exercise intensity and motion frequency, and described daily daily life information comprises heart rate;
Described analytic unit, draws the hobby type of this user specifically for analyzing described place classification information; Analyze the type of sports that described movable information draws this user; Analyze the daily work and rest rule that described daily daily life information draws this user.
11. systems of commending friends in social networks as claimed in claim 9, is characterized in that, described data acquisition unit, specifically for gathering the behavioural characteristic data of user according to the first setting-up time cycle;
Described data upload unit, specifically for uploading the described personal information of this user to described social networking service device according to the second setting-up time cycle;
Wherein, the described second setting-up time cycle is more than or equal to the described first setting-up time cycle.
12. systems of commending friends in social networks as claimed in claim 9, it is characterized in that, described Intelligent worn device also comprises granted unit, before sending described behavioural characteristic data to social networks, authorizes behavioural characteristic data upload social networks.
13. systems of commending friends in social networks as claimed in claim 12, it is characterized in that, described data upload unit, specifically for uploading described behavioural characteristic data to preset service device, described social networking service device is to behavioural characteristic data described in described preset service device acquisition request.
14. systems of commending friends in social networks as claimed in claim 9, is characterized in that, described analytic unit, and also for after drawing the personal information of this user in the described behavioural characteristic data of analysis, the type marking described personal information is True Data.
15. systems of commending friends in social networks as claimed in claim 9, it is characterized in that, described matching unit, specifically for the personal information of this user of comparison and other social network user, draws the matching degree of the personal information of other social network user and the personal information of this user; Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user; Described commending friends is sent to described user.
16. systems of commending friends in social networks as claimed in claim 9, is characterized in that, described matching unit, specifically for receiving good friend's matching condition of setting; The personal information of this user of comparison and other social network user, draws the matching degree of other social network user and this user under described good friend's matching condition; Sort from high to low by described matching degree, the social network user finding out the predetermined number come above is the commending friends of this user; Described commending friends is sent to described user.
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