CN105450510B

CN105450510B - Friend management method, device and server for social network-i i-platform

Info

Publication number: CN105450510B
Application number: CN201510959349.2A
Authority: CN
Inventors: 侯文迪; 陈志军; 龙飞
Original assignee: Xiaomi Inc
Current assignee: Xiaomi Inc
Priority date: 2015-12-18
Filing date: 2015-12-18
Publication date: 2019-03-01
Anticipated expiration: 2035-12-18
Also published as: CN105450510A

Abstract

The disclosure provides a kind of friend management method, device and server for social network-i i-platform, and a specific embodiment of the method includes: to obtain the good friend of social network user some or all of to issue social network information；The good friend of predefined type is found out from the good friend of the user based on the social network information；The list of the good friend of the predefined type is pushed, to the user to remind user to carry out interruption-free setting according to the list.The embodiment searches the good friend of predefined type without user manually, to save the time, improves the service efficiency of social networks.

Description

Friend management method, device and server for social network-i i-platform

Technical field

This disclosure relates to field of computer technology, in particular to a kind of friend management method for social network-i i-platform, Device and server.

Background technique

With the continuous development of network technology, some network-based instant messaging tools occur therewith, for example, wechat, The common instant messaging tools such as credulity.User can share some societies in the circle of friends of these instant messaging tools with friend It hands over the network information (dynamic of such as user or the information of forwarding), greatly improves user experience.But the portion of some users Point good friend often may send out advertising information or brush screen some, bring and bother to many users.

Currently, most of user passes through the good friend for searching those advertisement brush screens manually, then it is shielded or is deleted It removes, to waste the time, reduces the service efficiency of social networks.

Summary of the invention

To solve the above-mentioned problems, the disclosure provide it is a kind of for the friend management method of social network-i i-platform, device and Server.

According to the first aspect of the embodiments of the present disclosure, a kind of friend management method for social network-i i-platform is provided, is wrapped It includes:

The good friend for obtaining social network user some or all of issued social network information；

The good friend of predefined type is found out from the good friend of the user based on the social network information；

The list of the good friend of the predefined type is pushed, to the user to remind user to carry out exempting to beat according to the list Disturb setting.

Optionally, described to find out the good of predefined type from the good friend of the user based on the social network information Friend, comprising:

Obtain the information of the corresponding textual form of content of the social network information；

Based on the information of the textual form, determine whether corresponding social network information belongs to predetermined classification；

According to the judgement as a result, the good friend for counting the user issued belong to the other social network of the predetermined class The related data of network information；

The good friend for meeting the good friend of predetermined condition as predefined type is found out from the good friend of the user, wherein institute State good friend of the good friend for meeting predetermined condition for the corresponding related data more than predetermined threshold.

Optionally, the related data includes following one or more:

What the good friend of the user issued belongs to the quantity of the other social network information of the predetermined class；

What the good friend of the user issued belongs to the other social network information proportion of the predetermined class.

Optionally, the information of the corresponding textual form of content for obtaining the social network information, comprising:

Determine the expression forms of information of each social network information；

In the way of corresponding to the expression forms of information, the content for extracting each social network information is corresponding Textual form information；

Wherein, the expression forms of information includes text, image, video, link and sound.

Optionally, the information based on the textual form, determines whether corresponding social network information belongs to predetermined classification, Include:

Obtain the kind judging dictionary obtained by training；

Kind judging is carried out using information of the kind judging dictionary to the textual form, with the determination text shape Whether the corresponding social network information of the information of formula belongs to predetermined classification.

Optionally, training obtains the kind judging dictionary in the following way:

The corresponding text information of multiple sample social network informations is obtained as sample information, wherein the known sample Whether social network information belongs to the predetermined classification；

Keyword is extracted from the sample information；

Determine the keyword and the other relevance parameter of the predetermined class；

Extract the corresponding keyword of the relevance parameter for being more than or equal to predetermined threshold；

Kind judging dictionary is generated based on the keyword and the corresponding relevance parameter.

Optionally, the keyword and the other relevance parameter of the predetermined class are determined, comprising:

The keyword is calculated using the method for card side's verifying CHI to join with the other chi-square value of the predetermined class as correlation Number.

It is optionally, described that kind judging is carried out using information of the kind judging dictionary to the textual form, comprising:

Keyword is extracted from the information of the textual form as keyword to be determined；

The corresponding relevance parameter of the keyword to be determined is searched from the kind judging dictionary；

The ratio of referring to is obtained, it is described other to belong to the predetermined class in corresponding sample social network information with reference to ratio Ratio shared by information；

Based on the corresponding relevance parameter of the keyword to be determined and it is described refer to ratio, it is related to calculate first Degree and second degree of correlation, wherein first degree of correlation be the textual form the corresponding social network information of information with The other degree of correlation of predetermined class, second degree of correlation be the textual form the corresponding social network information of information with it is non-predetermined The degree of correlation of classification；

Judge the size of first degree of correlation and second degree of correlation；If first degree of correlation is big, the textual form The corresponding social network information of information belong to predetermined classification.

Optionally, it is described based on the corresponding relevance parameter of the keyword to be determined and it is described refer to ratio, Calculate first degree of correlation and second degree of correlation, comprising:

The product of the relevance parameter of the keyword to be determined is calculated as the first product, and described first is multiplied Product is multiplied with described with reference to ratio again, is as a result used as first degree of correlation；

1 is calculated with the product of the difference of the relevance parameter of the keyword to be determined as the second product, and by 1 and The difference with reference to ratio is multiplied with second product again, is as a result used as second degree of correlation.

According to the second aspect of an embodiment of the present disclosure, a kind of good friend's managing device for social network-i i-platform is provided, is wrapped It includes:

Module is obtained, the good friend for being configured as obtaining social network user some or all of issued social networks letter Breath；

Searching module is configured as good friend of the social network information got based on the acquisition module from the user In find out the good friend of predefined type；

Pushing module is configured as pushing the good friend for the predefined type that the searching module is found out to the user List, with remind user according to the list carry out interruption-free setting.

Optionally, the searching module includes:

Text information acquisition submodule is configured as obtaining the corresponding textual form of content of the social network information Information；

Decision sub-module is configured as the letter of the textual form got based on the text information acquisition submodule Breath, determines whether corresponding social network information belongs to predetermined classification；

Statistic submodule is configured as according to decision sub-module judgement as a result, counting good friend's hair of the user What cloth was crossed belongs to the related data of the other social network information of the predetermined class；

Submodule is searched, is configured as finding out the good friend for meeting predetermined condition from the good friend of the user as predetermined The good friend of type, wherein the good friend for meeting predetermined condition is the good friend that the corresponding related data is more than predetermined threshold.

Optionally, the related data includes following one or more:

Optionally, the text information acquisition submodule includes:

It determines submodule, is configured to determine that the expression forms of information of each social network information；

Extracting sub-module is configured as according to the side for corresponding to the expression forms of information that the determining submodule determines Formula extracts the information of the corresponding textual form of content of each social network information；

Optionally, the decision sub-module includes:

Dictionary acquisition submodule is configured as obtaining the kind judging dictionary obtained by training；

Kind judging submodule is configured as the kind judging dictionary got using the dictionary acquisition submodule Kind judging is carried out to the information of the textual form, is with the corresponding social network information of information of the determination textual form It is no to belong to predetermined classification.

Optionally, training obtains the kind judging dictionary in the following way:

Keyword is extracted from the sample information；

Optionally, the kind judging submodule includes:

Keyword extraction submodule is configured as extracting keyword from the information of the textual form as pass to be determined Keyword；

Parameter searches submodule, is configured as searching the keyword to be determined from the kind judging dictionary corresponding The relevance parameter；

With reference to ratio acquisition submodule, it is configured as obtaining with reference to ratio, it is described social for corresponding sample with reference to ratio Belong to ratio shared by the other information of the predetermined class in the network information；

Computational submodule is configured as based on the corresponding relevance parameter of keyword to be determined and the ginseng Ratio is examined, first degree of correlation and second degree of correlation are calculated, wherein first degree of correlation is the information pair of the textual form The social network information and the other degree of correlation of predetermined class answered, second degree of correlation are the corresponding society of information of the textual form Hand over the degree of correlation of the network information and non-predetermined classification；

Judging submodule is configured as judging the size of first degree of correlation and second degree of correlation；If described first is related Degree is big, then the corresponding social network information of the information of the textual form belongs to predetermined classification.

Optionally, the computational submodule includes:

First relatedness computation submodule is configured as calculating multiplying for the relevance parameter of the keyword to be determined Product is used as the first product, and first product is multiplied with described with reference to ratio again, is as a result used as first degree of correlation；

Second relatedness computation submodule is configured as calculating the relevance parameter of 1 with the keyword to be determined The product of difference is multiplied again with second product as the second product, and by 1 and the difference with reference to ratio, be as a result used as the Two degrees of correlation.

According to the third aspect of an embodiment of the present disclosure, a kind of server is provided, comprising:

Processor；

Memory for storage processor executable instruction；

Wherein, the processor is configured to:

The technical scheme provided by this disclosed embodiment can include the following benefits:

A kind of friend management method for social network-i i-platform provided by the above embodiment of the disclosure is obtained by being based on The good friend for the social network user got some or all of issued social network information, found out from the good friend of the user The good friend of predefined type, and push to the user list of the good friend of above-mentioned predefined type, with remind user according to the list into The setting of row interruption-free.It searches the good friend of predefined type manually without user, to save the time, improves making for social networks Use efficiency.

Another friend management method for being used for social network-i i-platform provided by the above embodiment of the disclosure, passes through judgement Whether the social network information of good friend's publication belongs to predetermined classification, and according to the judgement as a result, the good friend of counting user issues That crosses belongs to the related data of the other social network information of predetermined class, meets predetermined item to find out from the good friend of the user Good friend of the good friend of part as predefined type, and the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user Interruption-free setting is carried out according to the list.The good friend for searching predefined type manually without user mentions to help to save the time The service efficiency of high social networks.

Another friend management method for being used for social network-i i-platform provided by the above embodiment of the disclosure, passes through determination The expression forms of information of social network information, and in the way of corresponding to the expression forms of information, extract social networks letter The information of the corresponding textual form of the content of breath further finds out the good friend's work for meeting predetermined condition from the good friend of the user For the good friend of predefined type, and the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user according to the list Carry out interruption-free setting.It searches the good friend of predefined type manually without user, to help to save the time, improves social networks Service efficiency.

Another friend management method for being used for social network-i i-platform provided by the above embodiment of the disclosure, by using Kind judging is carried out by information of the obtained kind judging dictionary of training to above-mentioned textual form, to determine in the form of the text Whether the corresponding social network information of information belongs to predetermined classification, and according to the judgement as a result, the good friend of counting user issues That crosses belongs to the related data of the other social network information of predetermined class, meets predetermined item to find out from the good friend of the user Good friend of the good friend of part as predefined type, the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user's root Interruption-free setting is carried out according to the list.It searches the good friend of predefined type manually without user, to help to save the time, improves The service efficiency of social networks.

It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not The disclosure can be limited.

Detailed description of the invention

The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure Example, and together with specification for explaining the principles of this disclosure.

Fig. 1 is a kind of disclosure friend management method for social network-i i-platform shown according to an exemplary embodiment Flow chart；

Fig. 2 is the disclosure another good friend manager for being used for social network-i i-platform shown according to an exemplary embodiment The flow chart of method；

Fig. 3 is the disclosure another good friend manager for being used for social network-i i-platform shown according to an exemplary embodiment The flow chart of method；

Fig. 4 is the disclosure another good friend manager for being used for social network-i i-platform shown according to an exemplary embodiment The flow chart of method；

Fig. 5 is a kind of disclosure good friend's managing device for social network-i i-platform shown according to an exemplary embodiment Block diagram；

Fig. 6 is that the disclosure another good friend for social network-i i-platform shown according to an exemplary embodiment manages dress The block diagram set；

Fig. 7 is that the disclosure another good friend for social network-i i-platform shown according to an exemplary embodiment manages dress The block diagram set；

Fig. 8 is that the disclosure another good friend for social network-i i-platform shown according to an exemplary embodiment manages dress The block diagram set；

Fig. 9 is that the disclosure another good friend for social network-i i-platform shown according to an exemplary embodiment manages dress The block diagram set；

Figure 10 is that the disclosure another good friend for social network-i i-platform shown according to an exemplary embodiment manages The block diagram of device；

Figure 11 is that disclosure one kind shown according to an exemplary embodiment can be using the exemplary of the embodiment of the present disclosure System architecture diagram；

Figure 12 is a kind of disclosure good friend's management dress for social network-i i-platform shown according to an exemplary embodiment The structural schematic diagram set.

Specific embodiment

Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment Described in embodiment do not represent all implementations consistent with this disclosure.On the contrary, they be only with it is such as appended The example of the consistent device and method of some aspects be described in detail in claims, the disclosure.

It is only to be not intended to be limiting the disclosure merely for for the purpose of describing particular embodiments in the term that the disclosure uses. The "an" of the singular used in disclosure and the accompanying claims book, " described " and "the" are also intended to including majority Form, unless the context clearly indicates other meaning.It is also understood that term "and/or" used herein refers to and wraps It may be combined containing one or more associated any or all of project listed.

It will be appreciated that though various information, but this may be described using term first, second, third, etc. in the disclosure A little information should not necessarily be limited by these terms.These terms are only used to for same type of information being distinguished from each other out.For example, not departing from In the case where disclosure range, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as One information.Depending on context, word as used in this " if " can be construed to " ... when " or " when ... When " or " in response to determination ".

As shown in Figure 1, Fig. 1 is that a kind of good friend for social network-i i-platform shown according to an exemplary embodiment manages The flow chart of method, this method can be applied in server.Method includes the following steps:

In a step 101, the good friend for obtaining social network user some or all of issued social network information.

In the present embodiment, social networks can for IM (Instant Messenger, instant messaging) tool (such as wechat, QQ, Fetion etc.) or website (such as facebook, Renren Network, microblogging) etc..Social network information can be used for social networks The information etc. of dynamic or forwarding that family is issued by social networks, for example, social network information can pass through for wechat user The dynamic message dynamic message or microblog users that perhaps QQ user is issued by QQ space of circle of friends publication pass through microblogging Dynamic message of publication etc..

In the present embodiment, it in response to the generation of scheduled event, can obtain what the good friend of social network user issued Part or all of social network information, and subsequent operation is carried out, to push the list of the good friend of predefined type to above-mentioned user. In one implementation, scheduled event can be predetermined expired, and the expected time of arrival can be a scheduled time span (perhaps one month or two months etc. such as one week).For example, every other week or one month, pushing a predetermined class to user The list of the good friend of type.In one another kind of implementation, scheduled event can also be the request for receiving user's transmission Push the instruction of the list of predefined type good friend.It is sent for example, the user end to server installed in terminal can be used in user The instruction etc. of the list of request push predefined type good friend.It is appreciated that scheduled event can also be other events Generation, the disclosure do not limit the particular content of the generation of scheduled event.

In the present embodiment, in response to the generation of scheduled event, the good friend of some available designated user was once issued The whole social network informations crossed, the designated user that also good friend of some available designated user once issued can be clear The whole social network informations (i.e. the designated user has the permission for browsing above-mentioned social network information) look at, can also obtain certain The good friend of a designated user some period (as nearest one week perhaps nearest one month or it is 1 year nearest) issued Social network information, the good friend that can also obtain some designated user at random issued predetermined item number (e.g., 30 or 50 Deng) social network information.It is appreciated that the disclosure to not limiting in this respect.

In a step 102, the good of predefined type is found out from the good friend of above-mentioned user based on above-mentioned social network information Friend.

In general, people can share knowledge, enjoyment and the sense in life by publication social network information with good friend Realize, increase the interaction with good friend and exchange, it is also possible to by the social network information of browsing good friend, to understand The recent developments of friend.But some people issue some advertising informations using social network-i i-platform, or issue some other and it is to be easy The content (such as constellation text, chicken soup text, patch of starting a rumour show off patch etc.) of dislike, and frequent brush screen, allow many good friends to detest, influence The experience of good friend.

In the present embodiment, certain classification information is more or release information frequency is higher to issue by the good friend of predefined type Good friend.For example, the good friend of predefined type can be the more good friend of releasing advertisements classification information, or it is also possible to issue chicken The more good friend of soup text classification information perhaps can also be that publication is started a rumour and paste the more good friend of classification information or can also be The frequency that releases news is more than the good friend etc. of predetermined threshold.Because whether certain good friend of user belongs to the good friend of predefined type, with this The content or information publication situation of the information of good friend's publication are closely related, therefore, can be based on the social activity that the good friend issued The network information differentiates whether the good friend belongs to the good friend of predefined type.

In the present embodiment, the name of the good friend of predefined type is belonged in the good friend to obtain some social network user It is single, need the good friend for obtaining the social network user first some or all of to issue social network information, then, based on upper It states social network information to determine the type of each good friend, determines whether each good friend belongs to the good friend of predefined type, so The good friend (e.g., the more good friend etc. of releasing advertisements classification information) for belonging to predefined type is found out from the good friend of the user afterwards.

In step 103, Xiang Shangshu user push predefined type good friend list, with remind user according to the list into The setting of row interruption-free.

In the present embodiment, after belonging to the good friend of predefined type in the good friend for find out social network user, obtaining should Belong to the list of the good friend of predefined type in the good friend of user, and the list be pushed to above-mentioned user, with remind user according to The list carries out interruption-free setting.Wherein, interruption-free setting, which can be, shields the good friend, is also possible to delete the good friend, may be used also To be other set-up modes, it will be understood that do not limited in terms of the concrete mode that interruption-free is arranged in the disclosure.

The friend management method provided by the above embodiment for social network-i i-platform of the disclosure is got by being based on The good friend of social network user some or all of issued social network information, found out from the good friend of the user predetermined The good friend of type, and the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user to be exempted from according to the list Bother setting.It searches the good friend of predefined type manually without user, to save the time, improves the use effect of social networks Rate.

As shown in Fig. 2, Fig. 2 another good friend for social network-i i-platform shown according to an exemplary embodiment manages The flow chart of method, which, which is described in detail, finds out predefined type from the good friend of user based on social network information The process of good friend, this method can be applied in server, comprising the following steps:

In step 201, the good friend for obtaining social network user some or all of issued social network information.

In step 202, the information of the corresponding textual form of content of above-mentioned social network information is obtained.

In the present embodiment, above-mentioned social network information may be the information of textual form, be also possible to image format Information can also be the information etc. of link form.Since the information of textual form is easier to handle, get social network After the good friend of network user some or all of issued social network information, firstly, being corresponded to above-mentioned social network information Processing, the information of the corresponding textual form of content of above-mentioned social network information is obtained, to carry out subsequent analysis and place Reason.

For example, the information can be directly acquired if above-mentioned social network information is the information of textual form.Example again Such as, if above-mentioned social network information is the information of link form, corresponding network resource can be found out according to the link, and Therefrom extract the information of textual form.For another example if above-mentioned social network information is the information of image format, it can be to this Image carries out image recognition processing, extracts the text information in image, the information etc. as textual form.

In step 203, it is predetermined to determine whether corresponding social network information belongs to for the information based on above-mentioned textual form Classification.

In the present embodiment, the social network information can be divided into several classification by the content of social network information, And the same social network information also may belong to multiple classifications.The predetermined classification of the present embodiment can be according to any principle The classification separated can be the classification for being easy to allow other users to dislike.For example, the other social network information of predetermined class can be extensively The information for accusing classification, is also possible to the information of constellation text classification, can also be the information of chicken soup text classification, can also be patch of starting a rumour The information etc. of classification, it will be understood that can also have the information of other classifications, the disclosure to not limiting in this respect.

In the present embodiment, the information of each textual form can be analyzed and processed, therefore, it is determined that corresponding society Hand over whether the network information belongs to predetermined classification.In one implementation, it can be got using the method for cluster to above-mentioned The information of all textual forms carries out clustering, therefore, it is determined that the corresponding social network information of the information of each textual form is It is no to belong to predetermined classification.

In another implementation, each textual form can also be determined using preparatory trained discrimination model Whether the corresponding social network information of information belongs to predetermined classification.Wherein, a discrimination model corresponds to a kind of predetermined classification, can be with For the different discrimination model of different predetermined classification training.It is appreciated that other sides that arbitrarily may be implemented can also be used Method determines whether the corresponding social network information of information of textual form belongs to predetermined classification, and the disclosure to not limiting in this respect.

In step 204, according to above-mentioned judgement as a result, the good friend for counting above-mentioned user issued belong to predetermined classification Social network information related data.

In the present embodiment, above-mentioned related data may include following one or more: the good friend of above-mentioned user issued The quantity for belonging to the other social network information of predetermined class and above-mentioned user good friend issued belong to the other society of predetermined class Hand over network information proportion.

In a kind of implementation of the present embodiment, can count that the good friend of above-mentioned user issued belongs to predetermined classification Social network information quantity, as above-mentioned related data.For example, it is assumed that the other information of predetermined class is the other letter of commercial paper Breath can then count the quantity (such as item number) for the other information of commercial paper that each good friend of above-mentioned user issued, as above-mentioned Related data.

In another implementation of the present embodiment, belonging to of can also counting that the good friend of above-mentioned user issued is predetermined Ratio shared by the social network information of classification, as above-mentioned related data.For example, it is assumed that the other information of predetermined class is commercial paper Other information can also count the number for the other information of commercial paper that each good friend of above-mentioned user issued within a certain period of time Amount accounts for the ratio for the information sum that the period was issued, or the non-advertisement classification information issued with the period The ratio of quantity, as above-mentioned related data.

It is appreciated that related data can also be the data of other aspects, concrete form side of the disclosure to related data Face does not limit.

In step 205, the good friend for meeting predetermined condition is found out from the good friend of above-mentioned user as predefined type Good friend.

In the present embodiment, the good friend for meeting predetermined condition is good friend of the corresponding related data more than predetermined threshold.Example Such as, it is assumed that the other information of predetermined class is the other information of commercial paper, then the good friend for meeting predetermined condition can be releasing advertisements classification The good friend of the quantity a predetermined level is exceeded threshold value of information.The good friend for meeting predetermined condition can also be the advertisement classification letter issued Breath accounts for good friend of the ratio more than predetermined ratio threshold value for the information sum issued.The good friend for meeting predetermined condition that will be found out Good friend as predefined type.It is appreciated that predetermined condition can also be other conditions, the disclosure to not limiting in this respect.

In step 206, Xiang Shangshu user push predefined type good friend list, with remind user according to the list into The setting of row interruption-free.

It should be noted that no longer going to live in the household of one's in-laws on getting married in above-mentioned Fig. 2 embodiment for the step identical with Fig. 1 embodiment It states, related content can be found in Fig. 1 embodiment.

The friend management method provided by the above embodiment for social network-i i-platform of the disclosure, by determining good friend's hair Whether the social network information of cloth belongs to predetermined classification, and according to the judgement as a result, the category that the good friend of counting user issued In the related data of the other social network information of predetermined class, meet the good of predetermined condition to find out from the good friend of the user Good friend of the friend as predefined type, and the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user according to this List carries out interruption-free setting.It searches the good friend of predefined type manually without user, to help to save the time, improves social The service efficiency of network.

As shown in figure 3, Fig. 3 is that another good friend for social network-i i-platform shown according to an exemplary embodiment manages The information for the corresponding textual form of content for obtaining social network information is described in detail in the flow chart of reason method, the embodiment Process, this method can be used in server, comprising the following steps:

In step 301, the good friend for obtaining social network user some or all of issued social network information.

In step 302, the expression forms of information of each above-mentioned social network information is determined.

In step 303, in the way of corresponding to the above- mentioned information form of expression, each above-mentioned social networks letter is extracted The information of the corresponding textual form of the content of breath.

In the present embodiment, expression forms of information includes but is not limited to text, image, video, link and sound etc..? Before the information of the corresponding textual form of content for extracting each above-mentioned social network information, it is necessary first to determine each above-mentioned society The expression forms of information for handing over the network information, because for the different social network information of the form of expression, extracting corresponding text shape The mode of the information of formula is different.Then, according still further to the mode for corresponding to the above- mentioned information form of expression, each above-mentioned social activity is extracted The information of the corresponding textual form of the content of the network information.

In a kind of implementation of the present embodiment, if it is determined that the expression forms of information of the social network information gone out is text Word then directly extracts the word content of the social network information.

In another implementation of the present embodiment, if it is determined that the expression forms of information of social network information gone out is Image then can carry out image recognition to the image first, the text information for including in the image be extracted, as the social network The information of the corresponding textual form of the content of network information.

In another implementation of the present embodiment, if it is determined that the expression forms of information of social network information gone out is Image can also carry out feature extraction to the image, according to the feature of the image extracted, obtain and the image phase from network The verbal description of pass, the information of the corresponding textual form of content as the social network information.

In another implementation of the present embodiment, if it is determined that the expression forms of information of social network information gone out is Link can find out corresponding Internet resources (such as linking corresponding webpage) according to link and then obtain from above-mentioned network money Relevant text information is taken, the information of the corresponding textual form of the content as the social network information.

In another implementation of the present embodiment, if it is determined that the expression forms of information of social network information gone out is Video can obtain caption information from the data of the video, or carry out identification to video frame to obtain the phase in video Close text information, the information of the corresponding textual form of content as the social network information.

In another implementation of the present embodiment, if it is determined that the expression forms of information of social network information gone out is Sound can carry out speech recognition to the sound first, which believes as the social networks The information of the corresponding textual form of the content of breath.

In step 304, it is predetermined to determine whether corresponding social network information belongs to for the information based on above-mentioned textual form Classification.

In step 305, according to above-mentioned judgement as a result, the good friend for counting above-mentioned user issued belong to predetermined classification Social network information related data.

Within step 306, the good friend for meeting predetermined condition is found out from the good friend of above-mentioned user as predefined type Good friend.

In step 307, Xiang Shangshu user push predefined type good friend list, with remind user according to the list into The setting of row interruption-free.

It should be noted that for the step identical with Fig. 1 and Fig. 2 embodiment, in above-mentioned Fig. 3 embodiment no longer into Row repeats, and related content can be found in Fig. 1 and Fig. 2 embodiment.

The friend management method provided by the above embodiment for social network-i i-platform of the disclosure, by determining social network The expression forms of information of network information, and in the way of corresponding to the expression forms of information, extract the interior of social network information Hold the information of corresponding textual form, the good friend for meeting predetermined condition is further found out from the good friend of the user as predetermined The good friend of type, and the list of the good friend of above-mentioned predefined type is pushed to the user, to remind user to be exempted from according to the list Bother setting.It searches the good friend of predefined type manually without user, to help to save the time, improves the use of social networks Efficiency.

As shown in figure 4, Fig. 4 is that another good friend for social network-i i-platform shown according to an exemplary embodiment manages The information based on textual form is described in detail in the flow chart of reason method, the embodiment, determines that corresponding social network information is No to belong to the other process of predetermined class, this method can be used in server, comprising the following steps:

In step 401, the good friend for obtaining social network user some or all of issued social network information.

In step 402, the information of the corresponding textual form of content of above-mentioned social network information is obtained.

In step 403, the kind judging dictionary obtained by training is obtained.

In the present embodiment, better kind judging dictionary can be trained in advance, each kind judging dictionary can be sentenced A kind of fixed classification.Which classification needed to determine, just obtains the corresponding kind judging dictionary of the category.Kind judging dictionary can be In local server training and it is stored in local server, when needing kind judging dictionary, can be directly acquired from local；? It can be in other server training and be stored in local server, when needing kind judging dictionary, directly acquired from local； It can also be in other server training and be stored in other servers, when needing kind judging dictionary, network can be passed through It is obtained from other servers.

In the present embodiment, kind judging dictionary can be trained in the following way and be obtained: firstly, obtaining multiple sample societies Hand over the corresponding text information of the network information as sample information, wherein it is predetermined whether the known sample social network information belongs to Classification.Specifically, any number of social networks letter can be arbitrarily obtained from the social network information that multiple users issue Breath, as sample social network information.Then according to predetermined class, others' work classifies to these information.For example, it is assumed that predetermined Classification is advertisement classification, then these sample social network informations can be divided into the other information of commercial paper and non-commercial paper is other Information.These corresponding text informations of sample social network information are extracted again as sample information, and class is carried out to sample information It does not identify, e.g., is identified with 1 pair of other information of commercial paper, is identified with 0 pair of non-other information of commercial paper.

Then, keyword is extracted from these above-mentioned sample informations.Specifically, some tools in sample information can be extracted The word being of practical significance, as keyword, such as some nouns, verb, adjective etc..And the void of some not practical significances Word, such as some prepositions, conjunction, auxiliary word, modal particle be not usually by as keyword.The keyword extracted can form one Crucial phrase, it is ensured that duplicate word is not present in crucial phrase.

Then, each keyword and the other relevance parameter of above-mentioned predetermined class in crucial phrase are determined.Wherein, keyword It is that can arbitrarily indicate the parameter of keyword with the other correlation of predetermined class with the other relevance parameter of predetermined class, the disclosure is to phase The parameter of closing property specifically asks method not limit.It, can be using the side of card side's verifying CHI in a kind of implementation of the present embodiment Method calculates each keyword and the other chi-square value of predetermined class as relevance parameter.

For example, it is assumed that predetermined classification identifies non-advertisement classification with 0 with 1 come identified ad classification for advertisement classification.It closes In keyword group include " preferential " this keyword, in sample information, that is, belong to the other information of commercial paper include again " preferential " this The sample information of a keyword has A item, is not belonging to the other information of commercial paper but includes the sample information of " preferential " this keyword There is B item, only belonging to the other information of commercial paper not comprising the sample information of " preferential " this keyword has C item, that is, is not belonging to advertisement The sample information that the information of classification does not include " preferential " this keyword again has D item, and the total number of sample information is N item.With P (x ∣c₁) indicate keyword and the other chi-square value of predetermined class, keyword and the other relevance parameter of predetermined class are indicated with Ω, wherein x It indicates keyword " preferential ", c₁Indicate advertisement classification.Then

Ω=P (x ∣ c can be enabled₁).And since A+C and B+D and N are fixed value, and hence it is also possible to enableΩ can also be enabled to take and P (x ∣ c₁) relevant certain ginsengs Number, the disclosure do not limit the specific value aspect of Ω.

Finally, extract be more than or equal to predetermined threshold the corresponding keyword of relevance parameter, and based on above-mentioned keyword with And corresponding relevance parameter generates kind judging dictionary.Specifically, firstly, presetting the predetermined of a relevance parameter Threshold value finds out corresponding relevance parameter more than or equal to predetermined threshold then from the crucial phrase obtained in preceding step Keyword, and these keyword extractions are come out.Classification is generated based on above-mentioned keyword and corresponding relevance parameter to sentence Determine dictionary, wherein the category determines the information in dictionary comprising above-mentioned keyword and corresponding relevance parameter.

In step 404, determine that dictionary carries out kind judging to the information of above-mentioned textual form using the category, with determination Whether the corresponding social network information of the information of text form belongs to predetermined classification.

In the present embodiment, it can determine that dictionary carries out kind judging to the information of above-mentioned textual form using the category, So that it is determined that whether the corresponding social network information of the information of text form belongs to predetermined classification.Specifically, for every The information of the corresponding textual form of social network information is used as firstly, extracting keyword from the information of text form wait sentence Determine keyword.Then, all to be determined from the information for finding out text form in above-mentioned trained kind judging dictionary The corresponding relevance parameter of keyword.For example, it is assumed that predetermined classification is advertisement classification, mentioned from the information of above-mentioned textual form All keywords to be determined taken include A, B, C, D, E, c₁Indicate advertisement classification, c₂Indicate advertisement classification.From trained advertisement It is respectively P (A ∣ c that these keywords and the other relevance parameter of commercial paper are found out in kind judging dictionary₁)、P(B∣c₁)、P(C∣ c₁)、P(D∣c₁)、P(E∣c₁)。

Then, the ratio of referring to is obtained, is to belong to the other letter of predetermined class in corresponding sample social network information with reference to ratio The shared ratio of breath.In training kind judging dictionary, at the same calculate it is above-mentioned with reference to ratio, then by it is above-mentioned with reference to ratio with Corresponding dictionary stores with being associated.In conjunction with above-mentioned example, for example, it is assumed that thering is m item to belong to commercial paper in training dictionary Other sample social network information, other n items belong to the other sample social network information of non-commercial paper, are indicated with α with reference to ratio, Then α=m/m+n.It will be stored with being associated with reference to ratio α with the advertisement kind judging dictionary.Sentence when using above-mentioned advertisement classification When determining information progress kind judging of the dictionary to textual form, it can be obtained from stored data above-mentioned with reference to ratio.

Then, it is based on the corresponding above-mentioned relevance parameter of above-mentioned keyword to be determined and refers to ratio, calculate the first phase Guan Du and second degree of correlation, wherein first degree of correlation is the corresponding social network information of information of above-mentioned textual form and pre- Determine the degree of correlation of classification, information corresponding social network information and non-predetermined classification of second degree of correlation for above-mentioned textual form The degree of correlation.In the present embodiment, the relevance parameter of all keywords to be determined in the information of above-mentioned textual form can be calculated Product is multiplied again with above-mentioned with reference to ratio as the first product, and by the first product, as a result as first degree of correlation.Calculate 1 And the product of the difference of the above-mentioned relevance parameter of each keyword to be determined is as the second product in the information of above-mentioned textual form, And 1 and the above-mentioned difference with reference to ratio are multiplied with the second product again, as a result it is used as second degree of correlation.

In conjunction with above-mentioned example, for example, first degree of correlation can use P (c₁∣ X) it indicates, second degree of correlation can use P (c₂∣X) It indicates, wherein X indicates the corresponding social network information of the information of above-mentioned form, then

P(c₁∣ X)=P (A ∣ c₁)·P(B∣c₁)·P(C∣c₁)·P(D∣c₁)·P(E∣c₁)·α

P(c₂∣ X)=[1-P (A ∣ c₁)]·[1-P(B∣c₁)]·[1-P(C∣c₁)]·[1-P(D∣c₁)]·[1-P(E∣ c₁)]·(1-α)

Finally, judge the size of first degree of correlation and second degree of correlation, if first degree of correlation is big, text form The corresponding social network information of information belongs to predetermined classification.If second degree of correlation is big, the corresponding society of the information of text form The network information is handed over to belong to non-predetermined classification.In conjunction with above-mentioned example, for example, if P (c₁∣ X) > P (c₂∣ X), then illustrate text shape The corresponding social network information of the information of formula belongs to the other information of commercial paper.If P (c₁∣ X) < P (c₂∣ X), then illustrate the text The corresponding social network information of the information of form belongs to the other information of non-commercial paper.

In step 405, according to above-mentioned judgement as a result, the good friend for counting above-mentioned user issued belong to predetermined classification Social network information related data.

In a step 406, the good friend for meeting predetermined condition is found out from the good friend of above-mentioned user as predefined type Good friend.

In step 407, Xiang Shangshu user push predefined type good friend list, with remind user according to the list into The setting of row interruption-free.

It should be noted that for the step identical with Fig. 1 and Fig. 2 embodiment, in above-mentioned Fig. 4 embodiment no longer into Row repeats, and related content can be found in Fig. 1 and Fig. 2 embodiment.

The friend management method provided by the above embodiment for social network-i i-platform of the disclosure, by using by instructing The kind judging dictionary got carries out kind judging to the information of above-mentioned textual form, to determine the information pair in the form of the text Whether the social network information answered belongs to predetermined classification, and according to the judgement as a result, the category that the good friend of counting user issued In the related data of the other social network information of predetermined class, meet the good of predetermined condition to find out from the good friend of the user Good friend of the friend as predefined type, pushes the list of the good friend of above-mentioned predefined type, to the user to remind user according to the name It is single to carry out interruption-free setting.It searches the good friend of predefined type manually without user, to help to save the time, improves social network The service efficiency of network.

It should be noted that although describing the operation of the method for the present invention in the accompanying drawings with particular order, this is not required that Or hint must execute these operations in this particular order, or have to carry out operation shown in whole and be just able to achieve the phase The result of prestige.On the contrary, the step of describing in flow chart can change and execute sequence.For example, in the process 400 of Fig. 4, Ke Yixian Step 403 is executed, the kind judging dictionary obtained by training is obtained, then executes step 402 again, obtain above-mentioned social networks The information of the corresponding textual form of the content of information.Additionally or alternatively, it is convenient to omit certain steps merge multiple steps It is executed for a step, and/or a step is decomposed into execution of multiple steps.

Corresponding with the friend management method embodiment of social network-i i-platform is previously used for, the disclosure is additionally provided for society Hand over good friend's managing device of the network platform and its embodiment of applied server.

As shown in figure 5, Fig. 5 is that the disclosure is shown according to an exemplary embodiment a kind of for the good of social network-i i-platform Friendly managing device block diagram, the device include: to obtain module 501, searching module 502 and pushing module 503.

Wherein, module 501 is obtained, the good friend for being configured as obtaining social network user some or all of issued social activity The network information.

Searching module 502 is configured as the social network information got based on acquisition module 501 from the good of above-mentioned user The good friend of predefined type is found out in friend.

Pushing module 503 is configured as pushing the good friend's for the predefined type that searching module 502 is found out to above-mentioned user List, to remind user to carry out interruption-free setting according to the list.

As shown in fig. 6, Fig. 6 is that the disclosure is shown according to an exemplary embodiment another for social network-i i-platform Good friend's managing device block diagram, for the embodiment on the basis of aforementioned embodiment illustrated in fig. 5, searching module 502 may include: text Acquisition of information submodule 601, decision sub-module 602, statistic submodule 603 and lookup submodule 604.

Wherein, text information acquisition submodule 601 is configured as obtaining the corresponding text of content of above-mentioned social network information The information of this form.

Decision sub-module 602 is configured as the letter of the textual form got based on text information acquisition submodule 601 Breath, determines whether corresponding social network information belongs to predetermined classification.

Statistic submodule 603 is configured as according to the judgement of decision sub-module 602 as a result, counting the good friend of above-mentioned user That issued belongs to the related data of the other social network information of predetermined class.

Submodule 604 is searched, is configured as finding out the good friend's conduct for meeting predetermined condition from the good friend of above-mentioned user The good friend of predefined type, wherein the good friend for meeting predetermined condition is good friend of the corresponding related data more than predetermined threshold.

In some optional embodiments, above-mentioned related data includes following one or more: the good friend of user was issued The quantity for belonging to the other social network information of predetermined class.What the good friend of user issued belongs to the other social networks letter of predetermined class Cease proportion.

As shown in fig. 7, Fig. 7 is that the disclosure is shown according to an exemplary embodiment another for social network-i i-platform Good friend's managing device block diagram, for the embodiment on the basis of aforementioned embodiment illustrated in fig. 6, text information acquisition submodule 601 can To comprise determining that submodule 701 and extracting sub-module 702.

Wherein it is determined that submodule 701, is configured to determine that the expression forms of information of each social network information.

Extracting sub-module 702 is configured as according to the side for corresponding to the expression forms of information for determining that submodule 701 determines Formula extracts the information of the corresponding textual form of content of each social network information.

Wherein, the above- mentioned information form of expression includes text, image, video, link and sound.

As shown in figure 8, Fig. 8 is that the disclosure is shown according to an exemplary embodiment another for social network-i i-platform Good friend's managing device block diagram, for the embodiment on the basis of aforementioned embodiment illustrated in fig. 6, decision sub-module 602 may include: word Allusion quotation acquisition submodule 801 and kind judging submodule 802.

Wherein, dictionary acquisition submodule 801 is configured as obtaining the kind judging dictionary obtained by training.

Kind judging submodule 802 is configured as the kind judging dictionary pair got using dictionary acquisition submodule 801 The information of above-mentioned textual form carries out kind judging, to determine whether the corresponding social network information of information in the form of the text belongs to In predetermined classification.

In some optional embodiments, training obtains above-mentioned kind judging dictionary in the following way: obtaining multiple samples The corresponding text information of this social network information is as sample information, wherein whether known above-mentioned sample social network information belongs to In predetermined classification.Keyword is extracted from above-mentioned sample information.Determine the keyword and the other relevance parameter of predetermined class.It extracts More than or equal to the corresponding keyword of the relevance parameter of predetermined threshold.Based on the keyword and corresponding relevance parameter Generate kind judging dictionary.

In some optional embodiments, keyword and the other relevance parameter of predetermined class are determined, comprising: test using card side The method of card CHI calculates the keyword and the other chi-square value of predetermined class as relevance parameter.

As shown in figure 9, Fig. 9 is that the disclosure is shown according to an exemplary embodiment another for social network-i i-platform Good friend's managing device block diagram, on the basis of aforementioned embodiment illustrated in fig. 8, kind judging submodule 802 can wrap the embodiment Include: keyword extraction submodule 901, parameter searches submodule 902, with reference to ratio acquisition submodule 903, computational submodule 904 And judging submodule 905.

Wherein, keyword extraction submodule 901 is configured as extracting keyword conduct from the information of above-mentioned textual form Keyword to be determined.

Parameter searches submodule 902, is configured as searching keyword to be determined from above-mentioned kind judging dictionary corresponding Above-mentioned relevance parameter.

With reference to ratio acquisition submodule 903, it is configured as obtaining with reference to ratio, above-mentioned reference ratio is corresponding sample society It hands in the network information and belongs to ratio shared by the other information of predetermined class.

Computational submodule 904, be configured as based on the corresponding relevance parameter of above-mentioned keyword to be determined and with reference to than Example calculates first degree of correlation and second degree of correlation, wherein first degree of correlation is the corresponding social activity of information of above-mentioned textual form The network information and the other degree of correlation of predetermined class, second degree of correlation be above-mentioned textual form the corresponding social network information of information with The degree of correlation of non-predetermined classification.

Judging submodule 905 is configured as judging first degree of correlation and the second phase that computational submodule 904 is calculated The size of Guan Du.If first degree of correlation is big, the corresponding social network information of the information of above-mentioned textual form belongs to predetermined classification.

As shown in Figure 10, Figure 10 is that the disclosure is shown according to an exemplary embodiment another for social network-i i-platform Good friend's managing device block diagram, on the basis of aforementioned embodiment illustrated in fig. 9, computational submodule 904 may include: the embodiment First relatedness computation submodule 1001 and the second relatedness computation submodule 1002.

Wherein, the first relatedness computation submodule 1001 is configured as calculating the correlation ginseng of above-mentioned keyword to be determined Several products is multiplied with reference ratio again as the first product, and by the first product, is as a result used as first degree of correlation.

Second relatedness computation submodule 1002 is configured as calculating the difference of the relevance parameter of 1 and keyword to be determined Product is multiplied again with the second product as the second product, and by 1 and with reference to the difference of ratio, as a result as second degree of correlation.

It should be appreciated that above-mentioned apparatus can be preset in the server, can also be loaded by modes such as downloadings In server.Corresponding units in above-mentioned apparatus can cooperate flat for social networks to realize with the unit in server The scheme of good friend's management of platform.

For device embodiment, since it corresponds essentially to embodiment of the method, so related place is referring to method reality Apply the part explanation of example.The apparatus embodiments described above are merely exemplary, wherein described be used as separation unit The modular unit of explanation may or may not be physically separated, the component shown as modular unit can be or Person may not be physical module unit, it can and it is in one place, or may be distributed over multiple network module units On.Some or all of the modules therein can be selected to realize the purpose of disclosure scheme according to the actual needs.This field Those of ordinary skill can understand and implement without creative efforts.

Figure 11 shows the exemplary system architecture that can apply the embodiment of the present disclosure.

As shown in figure 11, system architecture 1100 may include terminal device 1101,1102, network 1103 and server 1104,1105.Network 1103 between terminal device 1101,1102 and server 1104 for providing the medium of communication link. Network 1103 may include various connection types, such as wired, wireless communication link or fiber optic cables etc..

User 1110 can be used terminal device 1101,1102 and be interacted by network 1103 with server 1104, to receive Or send message etc..Various client applications, such as various instant messaging works can be installed on terminal device 1101,1102 Tool, social network client, browser etc..

Terminal device 1101,1102 can be various electronic equipments, including but not limited to smart phone, tablet computer, a Personal digital assistant, pocket computer on knee and intelligent wearable equipment etc..

Server 1104,1105 can be to provide the server of various services.Server can to the data received into The processing such as row storage, analysis, and processing result is fed back into terminal device.Server can in response to user service request and Service is provided.For example, server can push the list etc. of the good friend of predefined type to terminal device.It is appreciated that a clothes Business device can provide one or more services, and same service can also be provided by multiple servers.

It should be understood that the number of terminal device, network and server in Figure 11 is only schematical.According to realization need It wants, can have any number of terminal device, network and server.

Correspondingly, the disclosure also provides a kind of server, which includes processor；It can be held for storage processor The memory of row instruction；Wherein, which is configured as:

Figure 12 is the device that a kind of good friend for social network-i i-platform shown according to an exemplary embodiment manages 1200 structural schematic diagram.For example, device 1200 can be mobile phone, computer, digital broadcast terminal, information receiving and transmitting set It is standby, game console, tablet device, Medical Devices, body-building equipment, personal digital assistant etc..

Referring to Fig.1 2, device 1200 may include following one or more components: processing component 1202, memory 1204, Power supply module 1206, multimedia component 1208, audio component 1210, the interface 1212 of input/output (I/O), sensor module 1214 and communication component 1216.

The integrated operation of the usual control device 1200 of processing component 1202, such as with display, telephone call, data communication, Camera operation and record operate associated operation.Processing element 1202 may include one or more processors 1220 to execute Instruction, to perform all or part of the steps of the methods described above.In addition, processing component 1202 may include one or more moulds Block, convenient for the interaction between processing component 1202 and other assemblies.For example, processing component 1202 may include multi-media module, To facilitate the interaction between multimedia component 1208 and processing component 1202.

Memory 1204 is configured as storing various types of data to support the operation in device 1200.These data Example includes the instruction of any application or method for operating on device 1200, contact data, telephone book data, Message, picture, video etc..Memory 1204 can by any kind of volatibility or non-volatile memory device or they Combination is realized, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), it is erasable can Program read-only memory (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic memory, flash memory Reservoir, disk or CD.

Power supply module 1206 provides electric power for the various assemblies of device 1200.Power supply module 1206 may include power management System, one or more power supplys and other with for device 1200 generate, manage, and distribute the associated component of electric power.

Multimedia component 1208 includes the screen of one output interface of offer between described device 1200 and user.? In some embodiments, screen may include liquid crystal display (LCD) and touch panel (TP).If screen includes touch panel, Screen may be implemented as touch screen, to receive input signal from the user.Touch panel includes that one or more touch passes Sensor is to sense the gesture on touch, slide, and touch panel.The touch sensor can not only sense touch or sliding is dynamic The boundary of work, but also detect duration and pressure associated with the touch or slide operation.In some embodiments, more Media component 1208 includes a front camera and/or rear camera.When device 1200 is in operation mode, as shot mould When formula or video mode, front camera and/or rear camera can receive external multi-medium data.Each preposition camera shooting Head and rear camera can be a fixed optical lens system or have focusing and optical zoom capabilities.

Audio component 1210 is configured as output and/or input audio signal.For example, audio component 1210 includes a wheat Gram wind (MIC), when device 1200 is in operation mode, when such as call mode, recording mode, and voice recognition mode, microphone quilt It is configured to receive external audio signal.The received audio signal can be further stored in memory 1204 or via communication Component 1216 is sent.In some embodiments, audio component 1210 further includes a loudspeaker, is used for output audio signal.

I/O interface 1212 provides interface, above-mentioned peripheral interface module between processing component 1202 and peripheral interface module It can be keyboard, click wheel, button etc..These buttons may include, but are not limited to: home button, volume button, start button and Locking press button.

Sensor module 1214 includes one or more sensors, and the state for providing various aspects for device 1200 is commented Estimate.For example, sensor module 1214 can detecte the state that opens/closes of device 1200, the relative positioning of component, such as institute The display and keypad that component is device 1200 are stated, sensor module 1214 can be with detection device 1200 or device 1,200 1 The position change of a component, the existence or non-existence that user contacts with device 1200,1200 orientation of device or acceleration/deceleration and dress Set 1200 temperature change.Sensor module 1214 may include proximity sensor, be configured in not any physics It is detected the presence of nearby objects when contact.Sensor module 1214 can also include optical sensor, as CMOS or ccd image are sensed Device, for being used in imaging applications.In some embodiments, which can also include acceleration sensing Device, gyro sensor, Magnetic Sensor, pressure sensor, microwave remote sensor or temperature sensor.

Communication component 1216 is configured to facilitate the communication of wired or wireless way between device 1200 and other equipment.Dress The wireless network based on communication standard, such as WiFi can be accessed by setting 1200,2G or 3G or their combination.It is exemplary at one In embodiment, communication component 1216 receives broadcast singal or broadcast correlation from external broadcasting management system via broadcast channel Information.In one exemplary embodiment, the communication component 1216 further includes near-field communication (NFC) module, to promote short distance Communication.For example, radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra wide band can be based in NFC module (UWB) technology, bluetooth (BT) technology and other technologies are realized.

In the exemplary embodiment, device 1200 can be by one or more application specific integrated circuit (ASIC), number Signal processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for executing the above method.

In the exemplary embodiment, a kind of non-transitorycomputer readable storage medium including instruction, example are additionally provided It such as include the memory 1204 of instruction, above-metioned instruction can be executed by the processor 1220 of device 1200 to complete the above method.Example Such as, the non-transitorycomputer readable storage medium can be ROM, random access memory (RAM), CD-ROM, tape, soft Disk and optical data storage devices etc..

Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure Its embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or Person's adaptive change follows the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure Or conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by following Claim is pointed out.

It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the accompanying claims.

Claims

1. a kind of friend management method for social network-i i-platform, which is characterized in that the described method includes:

The list of the good friend of the predefined type is pushed to the user, is set with reminding user to carry out interruption-free according to the list It sets；

Wherein, the good friend for finding out predefined type from the good friend of the user based on the social network information, comprising:

According to the judgement as a result, the other social networks of the predetermined class that belongs to that the good friend for counting the user issued is believed The related data of breath；

The good friend for meeting the good friend of predetermined condition as predefined type is found out from the good friend of the user, wherein described full The good friend of sufficient predetermined condition is the good friend that the corresponding related data is more than predetermined threshold；

Wherein, the information based on the textual form, determines whether corresponding social network information belongs to predetermined classification, packet It includes:

Obtain the kind judging dictionary obtained by training；

Kind judging is carried out using information of the kind judging dictionary to the textual form, with the determination textual form Whether the corresponding social network information of information belongs to predetermined classification；

Wherein, training obtains the kind judging dictionary in the following way:

The corresponding text information of multiple sample social network informations is obtained as sample information, wherein the known sample is social Whether the network information belongs to the predetermined classification；

Keyword is extracted from the sample information；

Kind judging dictionary is generated based on the keyword and the corresponding relevance parameter；

It is wherein, described that kind judging is carried out using information of the kind judging dictionary to the textual form, comprising:

The ratio of referring to is obtained, the reference ratio is to belong to the other information of the predetermined class in corresponding sample social network information Shared ratio；

Based on the corresponding relevance parameter of the keyword to be determined and it is described refer to ratio, calculate first degree of correlation with And second degree of correlation, wherein first degree of correlation is the corresponding social network information of information of the textual form and predetermined The degree of correlation of classification, information corresponding social network information and non-predetermined classification of second degree of correlation for the textual form The degree of correlation；

Judge the size of first degree of correlation and second degree of correlation；If first degree of correlation is big, the letter of the textual form It ceases corresponding social network information and belongs to predetermined classification.

2. the method according to claim 1, wherein the related data includes following one or more:

3. the method according to claim 1, wherein the content for obtaining the social network information is corresponding The information of textual form, comprising:

In the way of corresponding to the expression forms of information, the corresponding text of content of each social network information is extracted The information of this form；

4. the method according to claim 1, wherein the determination keyword and the other phase of the predetermined class Closing property parameter, comprising:

The keyword and the other chi-square value of the predetermined class are calculated as relevance parameter using the method for card side's verifying CHI.

5. the method according to claim 1, wherein described be based on the corresponding phase of the keyword to be determined Closing property parameter and it is described refer to ratio, calculate first degree of correlation and second degree of correlation, comprising:

The product of the relevance parameter of the keyword to be determined is calculated as the first product, and again by first product It is multiplied with described with reference to ratio, is as a result used as first degree of correlation；

Calculate 1 and the keyword to be determined the relevance parameter difference product as the second product, and by 1 with it is described It is multiplied again with second product with reference to the difference of ratio, is as a result used as second degree of correlation.

6. a kind of good friend's managing device for social network-i i-platform, which is characterized in that described device includes:

Module is obtained, the good friend for being configured as obtaining social network user some or all of issued social network information；

Searching module is configured as looking into from the good friend of the user based on the social network information that the acquisition module is got Find out the good friend of predefined type；

Pushing module is configured as pushing the name of the good friend for the predefined type that the searching module is found out to the user It is single, to remind user to carry out interruption-free setting according to the list；

Wherein, the searching module includes:

Text information acquisition submodule is configured as obtaining the letter of the corresponding textual form of content of the social network information Breath；

Decision sub-module is configured as the information of the textual form got based on the text information acquisition submodule, Determine whether corresponding social network information belongs to predetermined classification；

Statistic submodule is configured as according to decision sub-module judgement as a result, the good friend for counting the user issued The related data for belonging to the other social network information of the predetermined class；

Submodule is searched, is configured as finding out the good friend for meeting predetermined condition from the good friend of the user as predefined type Good friend, wherein the good friend for meeting predetermined condition be the corresponding related data be more than predetermined threshold good friend；

Wherein, the decision sub-module includes:

Kind judging submodule, the kind judging dictionary for being configured as getting using the dictionary acquisition submodule is to institute Whether the information for stating textual form carries out kind judging, belonged to the corresponding social network information of information of the determination textual form In predetermined classification；

Wherein, training obtains the kind judging dictionary in the following way:

Keyword is extracted from the sample information；

Wherein, the kind judging submodule includes:

Keyword extraction submodule is configured as extracting keyword from the information of the textual form as key to be determined Word；

Parameter searches submodule, and it is corresponding described to be configured as searching the keyword to be determined from the kind judging dictionary Relevance parameter；

With reference to ratio acquisition submodule, it is configured as obtaining with reference to ratio, the reference ratio is corresponding sample social networks Belong to ratio shared by the other information of the predetermined class in information；

Computational submodule, be configured as based on the corresponding relevance parameter of the keyword to be determined and it is described with reference to than Example calculates first degree of correlation and second degree of correlation, wherein first degree of correlation is that the information of the textual form is corresponding Social network information and the other degree of correlation of predetermined class, second degree of correlation are the corresponding social network of information of the textual form The degree of correlation of network information and non-predetermined classification；

Judging submodule is configured as judging first degree of correlation that the computational submodule is calculated and second degree of correlation Size；If first degree of correlation is big, the corresponding social network information of the information of the textual form belongs to predetermined classification.

7. device according to claim 6, which is characterized in that the related data includes following one or more:

8. device according to claim 6, which is characterized in that the text information acquisition submodule includes:

Extracting sub-module is configured as in the way of corresponding to the expression forms of information that the determining submodule determines, Extract the information of the corresponding textual form of content of each social network information；

9. device according to claim 6, which is characterized in that the computational submodule includes:

First relatedness computation submodule, the product for being configured as calculating the relevance parameter of the keyword to be determined are made For the first product, and first product is multiplied with described with reference to ratio again, as a result as first degree of correlation；

Second relatedness computation submodule is configured as calculating the difference of the relevance parameter of 1 and the keyword to be determined Product is multiplied again with second product as the second product, and by 1 and the difference with reference to ratio, as a result as the second phase Guan Du.

10. a kind of server characterized by comprising

Processor；

Memory for storage processor executable instruction；

Wherein, the processor is configured to:

Obtain the kind judging dictionary obtained by training；

Wherein, training obtains the kind judging dictionary in the following way:

Keyword is extracted from the sample information；