CN108537358A

CN108537358A - Processing method, device, storage medium and the electronic device of community data

Info

Publication number: CN108537358A
Application number: CN201810160163.4A
Authority: CN
Inventors: 石杨; 王常伦; 陈征
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2018-02-26
Filing date: 2018-02-26
Publication date: 2018-09-14
Anticipated expiration: 2038-02-26
Also published as: CN108537358B

Abstract

The invention discloses a kind of processing method of community data, device, storage medium and electronic devices.Wherein, this method includes：Determine the type for accessing the first user of each of the first user set of community, wherein the type of each first user is a target type under a target dimension；The second user with the type matching of the first user is searched from the set of second user for having not visited community；Obtain first user the target dimension the first index；Obtain the second user the target dimension the second index；Processing is optimized according to the data of the first index and the second exponent pair community, wherein the Activity Index that the community after optimization processing is used to make the first user under target dimension improves.The present invention solves the technical issues of can not optimizing community content to precipitate user according to user behavior.

Description

Processing method, device, storage medium and the electronic device of community data

Technical field

The present invention relates to data processing fields, in particular to a kind of processing method of community data, device, storage Medium and electronic device.

Background technology

Internet and the nearly fast development in 10 years of mobile Internet, netizen's permeability are continuously increased, and demographic dividend is gradually It disappears, internet gradually switchs to stock market by developing market.Internet product makes user be produced in selection the education of user Become more fastidious in terms of product and acquisition content information.Quality product and premium content become that drug on the market from supply falls short of demand.It passes The big flow of the portal type of media of system is gradually disintegrated and is shunted.

Due to above two aspect, internet product obtains user and the difficulty of precipitation user continues to increase.With interior Holding the emergence of marketing, the migration efficiency of internet product more attracts and precipitates user with good content under the new situation, with Assemble high-quality flow.For example, product is combined with community platform, precipitated by good community content more users come Use product.

However, there is presently no the method for the quality for weighing community, causes not optimizing community content now and Precipitate more users.

For above-mentioned problem, currently no effective solution has been proposed.

Invention content

An embodiment of the present invention provides a kind of processing method of community data, device, storage medium and electronic devices, so that Few the technical issues of solving not optimizing to precipitate user community content according to user behavior.

One side according to the ... of the embodiment of the present invention provides a kind of processing method of community data, including：It determines and accesses Cross the type of the first user of each of the first user set of community, wherein the type of each first user is one A target type under target dimension；It searches from the set of second user for having not visited the community and is used with described first The second user of the type matching at family；Obtain first index and the second user of first user in the target dimension In the second index of the target dimension, wherein first index is used to indicate first user and is accessing the community First time period before and the Behavioral change in the second time period after the access community, second index is for referring to Show Behavioral change of the second user in the first time period and the second time period；According to first index and The data of community described in second exponent pair optimize processing, wherein the community after optimization processing is described for making Activity Index of first user under the target dimension improves.

Another aspect according to the ... of the embodiment of the present invention additionally provides a kind of processing unit of community data, including：It determines single Member, the type for the first user of each of determining the first user set for accessing community, wherein each described first uses The type at family is a target type under a target dimension；Searching unit, for from having not visited the second of the community The second user with the type matching of first user is searched in the set of user；First acquisition unit, it is described for obtaining First index of first user in the target dimension, wherein first index is used to indicate first user and is accessing The Behavioral change in the second time period after first time period and the access community before the community；Second obtains list Member, for obtain the second user the target dimension the second index, wherein second index is used to indicate described Behavioral change of the second user in the first time period and the second time period；Processing unit, for according to described the The data of community described in one index and second exponent pair optimize processing, wherein the community after optimization processing uses It is improved in making Activity Index of first user under the target dimension.

Optionally, the processing unit includes：4th determining module, for being referred to using first index and described second Several ratio determines behavioral implications degree of the community to first user under the target dimension；Update module is used In the content for updating community's displaying according to the behavioral implications degree.

Another aspect according to the ... of the embodiment of the present invention additionally provides a kind of storage medium, is stored in the storage medium Computer program, wherein the computer program is arranged to execute above-mentioned method when operation.

Another aspect according to the ... of the embodiment of the present invention additionally provides electronic device, including memory and processor, feature It is, computer program is stored in the memory, and the processor is arranged to execute by the computer program The method stated.

Determine community to user's row by weighing the first index and the second index of user behavior variation under target dimension For influence, the data of community are optimized accordingly, so as to which solve in the prior art can not be according to user behavior pair Community content optimizes the technical issues of to precipitate user, has reached the skill optimized to community content according to user behavior Art effect.

Description of the drawings

Attached drawing described herein is used to provide further understanding of the present invention, and is constituted part of this application, this hair Bright illustrative embodiments and their description are not constituted improper limitations of the present invention for explaining the present invention.In the accompanying drawings：

Fig. 1 is a kind of schematic diagram of hardware structure according to the ... of the embodiment of the present invention；

Fig. 2 is the flow chart of the processing method of community data according to the ... of the embodiment of the present invention；

Fig. 3 is a plurality of types of schematic diagrames according to the ... of the embodiment of the present invention；

Fig. 4 is the flow chart according to the ... of the embodiment of the present invention for obtaining the first index and the second index；

Fig. 5 is the schematic diagram according to the ... of the embodiment of the present invention for obtaining the first index and the second index；

Fig. 6 is the block diagram of TGI indexes according to the ... of the embodiment of the present invention；

Fig. 7 is the schematic diagram of the processing unit of community data according to the ... of the embodiment of the present invention；

Fig. 8 is the schematic diagram of terminal according to the ... of the embodiment of the present invention.

Specific implementation mode

In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people The every other embodiment that member is obtained without making creative work should all belong to the model that the present invention protects It encloses.

It should be noted that term " first " in description and claims of this specification and above-mentioned attached drawing, " Two " etc. be for distinguishing similar object, without being used to describe specific sequence or precedence.It should be appreciated that using in this way Data can be interchanged in the appropriate case, so as to the embodiment of the present invention described herein can in addition to illustrating herein or Sequence other than those of description is implemented.In addition, term " comprising " and " having " and their any deformation, it is intended that cover It includes to be not necessarily limited to for example, containing the process of series of steps or unit, method, system, product or equipment to cover non-exclusive Those of clearly list step or unit, but may include not listing clearly or for these processes, method, product Or the other steps or unit that equipment is intrinsic.

Explanation of nouns：

TGI：Target Group Index, target group's index

DAU:Daily Active User, enliven number day

ARPU：Average Revenue Per User, per user's average income

One side according to the ... of the embodiment of the present invention provides a kind of processing method of community data.In the present embodiment, The hardware environment that the processing method of above-mentioned community data can be applied to terminal 102 as shown in Figure 1 and server 104 is constituted In.As shown in Figure 1, terminal 102 is attached by network and server 104, above-mentioned network includes but not limited to：Wide area network, Metropolitan Area Network (MAN) or LAN, terminal 102 can be mobile phone terminals, can also be PC terminals, notebook terminal or tablet computer terminal.

The processing method of the community data of the present embodiment can execute on the terminal 102, can also be held in server 104 Row.Terminal 102 can provide the interface 106 of community, and the data for collecting user are reported to server 104 and store, by taking Business device 104 handles these data.Alternatively, being handled the data of the user of collection by terminal 102.

The data processing of the present embodiment can two aspect community is analyzed, so as to according to analysis result to community Optimize processing.Include in terms of the two：The analysis of community's aggregate users quality and community analyze the promotion of user quality. The analysis of community's aggregate users quality is used for illustrating which feature the user of login community has, and the user with these features exists It is horizontal in what in some dimension in all users.To the title analysis of user quality for illustrating, user accesses for community Data variation before the community and after the access community in some dimension.According to community aggregate users quality analysis and Community can weigh community at least one of the promotion analysis of user quality and act on product push, and then be carried out to community Optimization.The optimization includes the selection to the content of community, is improved and is upgraded with the content to community.For example, for trip Play product can push the contents such as strategy, playing method, teaching and race related to user according to the first index and the second index, So that the content of push more meets the demand of user.For video playing product, can be sieved according to the first index and the second index It selects the top set comment of related communities, discuss, exhibition method, way of recommendation of video resource etc. can also be set.For map Product can be introduced according to the map rejuvenation frequency of the first index and the second index replacement related communities, surrounding area, navigation carries Show lamp etc..The content of above-mentioned optimization includes being not limited to column, is also not necessarily limited to the content of each column and the display form of community etc.. The process of above-mentioned optimization can be automatically adjusted according to the first index and the correspondence of the second index and corresponding data.

Fig. 2 is the flow chart of the processing method of community data according to the ... of the embodiment of the present invention.As shown in Fig. 2, community's number According to processing method include：

S202 determines the type for accessing the first user of each of the first user set of community, wherein each first The type of user is a target type under a target dimension；

First user is the user for accessing community, and the first user set includes multiple first users.First user's Type can be obtained by cluster.Target dimension can be determined by user clustering, and target dimension is divided into multiple types. Each first user corresponds to one kind in centering type.Wherein, target dimension may include one or more dimension.

Optionally it is determined that the type for accessing the first user of each of the first user set of community includes：Obtain mesh Mark the multiple types under dimension；The value range of target component in target dimension is determined according to multiple types；According to value range Classify with each first user in the first user of targeted parameter value pair set of each first user；Determine the first user's Type is the target type in multiple types.

Each target dimension may include multiple types, can be according to the target component in target dimension under target dimension To determine type.For example, under the dimension of user activity, including 3 type of high liveness, middle liveness and low liveness. For example, may include advanced tiers and common grade this 2 type in the dimension of user gradation.Liveness and user gradation Combination can also be used as a type, for example, high liveness and advanced tiers, low liveness and advanced tiers, middle liveness and Common grade.Wherein, high liveness, middle liveness and low liveness can join as a kind of target under liveness dimension Number, common grade and advanced tiers can also be used as a kind of target component under grade dimension.Liveness and grade can It is divided using a certain range of value.The type of the first user can be determined using the criteria for classifying.For example, user's When integral is more than or equal to numerical value A, advanced tiers are determined that the user is；When user integral is less than numerical value A, the user is determined For common grade.

Target dimension may include the first dimension and the second dimension, and the multiple types obtained under target dimension include：Using First user set is clustered into multiple types by the mode of user clustering in the first dimension and the second dimension, wherein per type Type includes the parameter value range of the first dimension and the parameter value range of the second dimension.

Each dimension includes multiple types, the type progress permutation and combination in the first dimension and the second dimension is obtained a variety of Type.For example, the first dimension is user activity, the second dimension is the paid cases of user.Wherein, liveness includes 3 types Type, respectively high liveness, middle liveness and low liveness；Paid cases include type in 3, respectively high payment, middle payment With low payment.Obtain 9 type shown in Fig. 3 under liveness and paid cases the two dimensions, respectively low payment it is high it is active, The low work of active, low payment in active, low payment in active, middle payment in high active, high high active, the low payment of payment of middle payment Jump, the low active and high payment of middle payment are low active.Divide liveness high, normal, basic and payment it is high, normal, basic when, can basis Each type of value range is divided.For example, liveness value at [0, m], is determined as low liveness type；Liveness Value is determined as middle liveness type at (m, n)；Liveness value is determined as high liveness type when more than n.Similarly, Value of paying is determined as low payment type at [0, g]；Value of paying is determined as middle payment type at (g, h)；Payment takes Value is determined as high payment type when more than h.

After clustering out multiple types in the way of user clustering, during the first user to accessing community gathers Each first user classifies.It is determined described in first user according to each first the activity of the user value and payment value Type.The first the activity of the user value is needed when determining type and value of paying all meets the arbitrary of type in above-mentioned 9 When the value range of one type, the type of first user is determined.

For example, the first the activity of the user is in the value range of [0, m], when paying in the value range of (g, h), really The type of fixed first user is the type paid in low liveness.In this manner to each of the set of the first user First user classifies, and determines the type of each first user.

S204 is searched from the set of second user for having not visited community and is used with the second of the type matching of the first user Family；

Optionally, similar users are carried out in second user to the first user of each type in the way of look-alike Diffusion, find out same type and have not visited the user of micro- community.That is, from the set of second user for having not visited community The difference for searching the targeted parameter value of second user and the targeted parameter value of the first user is in the second user in preset range； By the type that the type configuration of the second user found is corresponding first user.

It is similar that difference, which is in the first user of preset range and second user,.Is had determined that in above-mentioned steps The type of one user, then identical as the type of first user as the type of the similar second user of the first user.

Step S206, first index of acquisition first user in the target dimension, wherein first index is used In instruction first user in the first time period before accessing the community and the second time after the access community Behavioral change in section；

Step S208, obtain the second user the target dimension the second index, wherein second index is used In Behavioral change of the instruction second user in the first time period and the second time period；

Optionally, obtain the first user target dimension the first index and second user target dimension the second index Including：First parameter of first user in the first time period after accessing community and the first user are being accessed into community The ratio of the second parameter in the second time period before is as the first index；By second user in first time period The ratio of first parameter and second parameter of the second user in second time period is as the second index.

First parameter and the second parameter are the parameters under target dimension, for example, all liveness under the dimension of liveness, Alternatively, the week payment mean value under dimension of paying.First parameter and the second parameter are all the parameters under identical dimensional, different It is that the first parameter is the parameter after the first user access community, the second parameter accesses the parameter before community for the first user. The behavioral implications of the first user of community pair can be obtained using the parameter before and after access community.For example, the first parameter is to access society Payment value after area, the second parameter are the payment value before accessing community, just be can determine that according to the second parameter and the first parameter The variation of first user payment value before and after accessing community, so that it is determined that the influence of the payment behavior of the first user of community pair.For The problem that avoids first user's single data more inaccurate can count in a period of time the first user before accessing community The variation of parameter under target dimension afterwards, this can be several days for a period of time, one week, one month, season etc. it is different when Between granularity.

Second user is that have same type with the first user.First parameter of second user and the first of the first user Parameter is the parameter under identical dimensional in same time period, and the second parameter of second user is phase with the second parameter of the first user With the parameter under identical dimensional in the period.Such as：The first parameter of first user is that all liveness in first week October are equal Value, then the first parameter of second user is also all liveness mean values in first week October.Namely it is that all liveness mean values refer to All activity mean values in same time period.For example, the second parameter of the first user is the mean value of paying the moon in January, second user The second parameter be also January the moon payment mean value.Difference lies in the first parameter of the first user is that the first user accesses community Data later, the second parameter access the data before community for the first user.The first parameter and the second parameter of second user It is the data for the same time not accessing community.

The first parameter and the second parameter of first user is used for obtaining the first index, the first parameter of second user and second Parameter is used for obtaining the second index.First index is the ratio of the first parameter and the second parameter of the first user, embodies first User accesses difference of the community later in parameter, to obtain the behavioral implications power of the first user of community pair.Second index is The ratio of the first parameter and the second parameter of second user, embodies second user in first time period and second time period Behavioral change.

S210 optimizes processing according to the data of the first index and the second exponent pair community.Wherein, after optimization processing The Activity Index that the community is used to make first user under the target dimension improves.

For example, detection update push content before the first index and the second index, then to community push content into Row update, in the content and then the first index of secondary detection of update push and the second index, if detected after update push content To the first index and the second index ratio higher than the ratio of the first index and the second index that update detects before, explanation Newer push content makes user be more willing to browse the community, alternatively, user is more willing to use production corresponding with the community Product.If the ratio of the first index and the second index detected after update push content is less than update detects before first The ratio of index and the second index illustrates that newer push content does not improve the wish that user browses the community, needs again Update the content of push.The content of update push in this example, can also be the displaying side of the arrangement, the comment that update column The content etc. being arranged in formula, column.

The first parameter and the second parameter of second user be mainly with reference to effect, in order to eliminate community operation activity, when Between the factors such as period influence so that data processing is more accurate.Using the ratio of the first index and the second index to community Data optimize.Optimizing processing according to the data of the first index and the second exponent pair community includes：Refer to using first The ratio of number and the second index determines behavioral implications degree of the first user of community pair under target dimension；According to behavioral implications journey The content of Du Geng new communities displaying.

The influence that the behavior of behavioral implications degree, that is, the first user of community pair generates number.For example, in the dimension of liveness On degree, the first user logs in weekly the corresponding product in a community or community, the first user before the first user accesses community As soon as the first user logs in the corresponding product in time community or community daily after accessing community, this illustrates, the first user of community pair Login behavior have an impact.For example, if user A logs in the corresponding product in 2 communities or community daily after accessing community, User B logs in the corresponding product in 1 community or community daily after accessing community, then, community is to the behavioral implications of user A Degree is higher than the behavioral implications to user B.For example, for the first time after update community content, user A is logged in daily after accessing community The corresponding product in 2 communities or community, after second updates community content, user A logs in 3 communities daily after accessing community Or the corresponding product in community, then, more new communities are higher than to for the first time more the degree of the behavioral implications of user A for the second time New communities.

In the dimension of liveness, active promotion degree index can be calculated：M=M1/M2*100

First the first users of index M 1=access (first time period) all liveness mean values (the first parameter) behind community/this One user accesses (second time period) all liveness mean values (the second parameter) before community

Second index M 2=second users first time period all liveness mean values/second user in second time period All liveness mean values

In the dimension of payment, payment promotion degree index can be calculated：N=N1/N2*100

First the first users of index N1=access community after (first time period) week payment mean value (the first parameter)/this first User accesses (second time period) week payment mean value (the second parameter) before community

Second index N2=second users pay the mean value/second user in second time period in the week of first time period Week payment mean value

Compare the first user and access the front and back Parameters variation under target dimension in community, while comparing second user identical Parameters variation in period under target dimension, so that it is determined that influence of the community to user behavior, that is, community is to target The influence of parameter under dimension, accordingly come optimize community displaying content.For example, update column, the content for updating column, refresh page Face layout etc..

The present embodiment determines community by weighing the first index and the second index of user behavior variation under target dimension Influence to user behavior accordingly optimizes the data of community, so as to solve in the prior art can not according to The technical issues of family behavior optimizes to precipitate user community content has reached and has been carried out to community content according to user behavior The technique effect of optimization, it is usage experience to improve community using the product by the user.

The present embodiment is illustrated by taking micro- community as an example below in conjunction with Fig. 4.

S401 selects the user for accessing community one day, obtains the set of the first user.

S402, by user clustering at 9 quadrants.As shown in figure 3,9 quadrants are type in 9.

S403 diffuses out the corresponding user of 9 quadrants by look-alike methods in the user for having not visited community, Obtain the set of second user.

S404 calculates separately out the liveness of the first user and second user before and after accessing community and payment degree index Variation.

S405 calculates actively promotion degree index M and payment promotion degree index N.

As shown in figure 5, the user in specified quadrant before accessing micro- community defines before accessing micro- community the The type of one user obtains the first the activity of the user mean value M of the type₁₁With payment mean value N₁₁, after accessing micro- community User i.e. first user in specified quadrant, obtains liveness mean value M of first user after accessing community₁₂And payment Mean value N₁₂, to obtain M₁=M₁₂/M₁₁,N₁=N₁₂/N₁₁。

The similar users clustered out in first 2 weeks and preceding 1 week corresponding period define with before accessing micro- community The type of the corresponding second user of user obtains the liveness mean value M of the second user of the type₂₁With payment mean value N₂₁, after User i.e. second user in 1 week and latter 2 weeks specified quadrant, obtains the second user latter 1 week and rear 2 weeks liveness Mean value M₂₂With payment mean value N₂₂, to obtain M₂=M₂₂/M₂₁,N₁=N₂₂/N₂₁。

Wherein, M₁₁、M₁₂、M₁₂、M₂₂、N₁₁、N₁₂、N₂₁、N₂₂Value it is as shown in table 1.

Table 1

Index	All liveness	Week payment mean value
			Before accessing micro- community	M₁₁=3.80	N₁₁=36.0
After accessing micro- community	M₁₂=4.00	N₁₂=38.1
			Cluster is with reference to user (preceding)	M₂₁=3.90	N₂₁=36.4
Cluster is with reference to user (rear)	M₂₂=3.80	N₂₂=37.1

Calculated actively promotion degree index M and payment promotion degree index N are as shown in table 2：

Table 2

M₁	M₂	M
			1.05	0.97	108
N₁	N₂	N
			1.06	1.02	104

As shown in Table 2, M and N is numerical value more than 100, when the value is bigger, shows promotion of micro- community to user Effect is bigger.Index is promoted from liveness and payment degree is promoted in terms of index, which can promote the behavior of user, Liveness promotes exponent specification user and accesses more active after micro- community, that is, more uses the corresponding production in the micro- community Product；The post-paid that payment degree promotes the micro- community of exponent specification user access is more, that is to say, that user is more when using product Use payment function.

Micro- community of the present embodiment can be community associated with product, which can be built-in the society in product Area.For example, the community in game application, the community in map application, the community etc. in instant messaging application, the master of these communities It is exactly the content of more concerns and displaying this product to want feature.For example, community's displaying game in game application is relevant interior Hold, the community in map application shows that the relevant content of road conditions, the community in instant messaging application show socially relevant content.

The Behavioral change that user in a period of time inherent liveness dimension and payment dimension is illustrated in above-described embodiment, needs It is noted that the present embodiment can analyze the Behavioral change of user after accessing community in multiple dimensions, it can also be more Analysis user accesses the Behavioral change after community in a period, to judge that community becomes the trend that user behavior influences Change, and then community is updated and is adjusted.

For the content of more targeted update and optimization community, it can first judge often to access the crowd of community big Belong in disk crowd (all users with the relevant product in community, including all users for accessing community and having not visited community) In what quality, which can be weighed by the activity of the user and two dimensions of payment, that is, access society in determination Before each of the first user set in the area type of the first user, method further includes：Obtain the first user is integrated into mesh Mark dimension third index, wherein third index be used to indicate the first user with target signature the first user set In shared ratio；Obtain reference set target dimension the 4th index, wherein reference set include the first user set and Second user set, the 4th index are used to indicate the ratio shared in reference set of the reference with target signature；According to Three indexes and the 4th index obtain target group's index, wherein target group's index is used to indicate first with target signature Surging degree of the user with respect to the reference with target signature in reference set in target dimension.

By taking game community as an example, game community attracts the access of game user with its unique content and specific function.Example Such as, the micro- community's set individual center, newest information, heroic teaching, fine work strategy, game video, game circle of friends etc. of playing are more Kind function.The quality of community's aggregate users is the importance for judging community's value, can precipitate and assemble the society of high quality user Area can become the important channel that high-quality user is runed in game.Active and two dimension definition game of payment are chosen in multiple indexs The quality of user.Wherein active aspect chooses any active ues and retains within 3rd, retains within 7th and retain within 14th as active dimension The index of degree judges, selects payment permeability, payment ARPU and any active ues ARPU as in terms of user charges in terms of payment Judgement.

Society is carried out using the method for similar target group's index (Target Group Index, TGI) in the present embodiment The analytical judgment of area's aggregate users quality.Target group's index is a kind of " tendency sex index ", refers to a certain subgroup, a certain index Ratio, the ratio between with the same index ratio of total group, multiplied by with the value of 100 gained of criterion numeral.Target group's indicial response be Surging degree of the target group in index dimension to be analyzed in the field analyzed.Surging program can be a target group Dominant degree of the opposite reference group in target dimension, reference group is a radix, and target group are reference groups A part.For example, the groups of people in a class have participated in mathematics promotion class by selecting, this part, which passes through to select, to be entered Mathematics, which promotes the achievement of the student of class to a certain extent mathematically, can be better than other students for not entering mathematics promotion class.With The index can describe the feature of target group well.Effective data foundation is provided for policy development, value judgement.

The computational methods of target group's index are：

The TGI indexes=[group with same characteristic features in group's proportion/totality with a certain feature in target group Body proportion] * criterion numerals 100.TGI indexes can be used for characterizing the difference condition of different characteristic user's concerned issue, wherein TGI indexes are equal to 100 and indicate average level, are higher than 100, represent such user and are higher than whole water to the degree of concern of certain class problem It is flat.Wherein, there is group's proportion, that is, third index of a certain feature in target group, there is the group of same characteristic features in totality Body proportion i.e. the 4th index.Target group's index=(third index/the 4th index) * 100.

Illustrate that the analysis of TGI indexes calculates with the data instance of table 3：

Table 3

The block diagram of Fig. 6 can intuitively reflect TGI indexes.I.e. the first user of micro- community users of table 3 gathers, deep bid User, that is, reference set.In the present embodiment, payment ARPU and to enliven ARPU two indices different from ratio above, borrow centre is joined The concept of number (industry payment ARPU and industry enliven ARPU), it is assumed that industry pays ARPU as A, and it is B that industry, which enlivens ARPU, then micro- Community pay ARPU delivery rate be 26.5/A, deep bid pay ARPU delivery rate be 20.4/A, then TGI=(26.5/A)/ (20.4/A)=130, it is 238 to enliven ARPU similarly and can calculate.When TGI indexes are more than 100, the data are bigger, the communities Ze Wei User group in this index feature it is more apparent.From upper table 3 it can be seen that the active and payment TGI indexes of micro- community users are equal More than 100, payment permeability and to enliven ARPU more notable illustrates that micro- community assembles good core in reference set and uses Family, ability of payment are apparently higher than reference set.

In this way, it can be determined that assemble the quality condition of the first user in the set of the first user in community, and Can time dimension carry out TGI indexes daily or week calculating, in real time follow up contents community user mass change Situation, to adjust the content or the contents such as migration efficiency of community's displaying according to the mass change of user.

It should be noted that for each method embodiment above-mentioned, for simple description, therefore it is all expressed as a series of Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the described action sequence because According to the present invention, certain steps can be performed in other orders or simultaneously.Secondly, those skilled in the art should also know It knows, embodiment described in this description belongs to preferred embodiment, and involved action and module are not necessarily of the invention It is necessary.

Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation The method of example can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but it is very much In the case of the former be more preferably embodiment.Based on this understanding, technical scheme of the present invention is substantially in other words to existing The part that technology contributes can be expressed in the form of software products, which is stored in a storage In medium (such as ROM/RAM, magnetic disc, CD), including some instructions are used so that a station terminal equipment (can be mobile phone, calculate Machine, server or network equipment etc.) execute method described in each embodiment of the present invention.

Another aspect according to the ... of the embodiment of the present invention additionally provides a kind of processing side for implementing above-mentioned community data The processing unit of the community data of method, as shown in fig. 7, the device includes：

Determination unit 70 accessed the type of the first user of each of the first user set of community for determination, In, the type of each first user is a target type under a target dimension；

First user is to access community, and the first user set includes multiple first users.The type of first user It can be obtained by cluster.Target dimension can be determined by user clustering, and target dimension is divided into multiple types.Each First user corresponds to one kind in centering type.Wherein, target dimension may include one or more dimension.

Optionally, the determination unit includes：Acquisition module, for obtaining the multiple types under the target dimension；It takes It is worth module, the value range for determining target component in the target dimension according to the multiple types；Sort module is used for Each described the in being gathered first user according to the targeted parameter value of the value range and each first user One user classifies；Third determining module, for determining that the type of first user is described in the multiple types Target type.

The target dimension includes the first dimension and the second dimension, and the acquisition module includes：Submodule is clustered, for adopting With the mode of user clustering in first dimension and second dimension by first user set be clustered into it is described more Type, wherein each type includes the parameter value model of the parameter value range and second dimension of first dimension It encloses.

Searching unit 72, for being searched and first user from the set of second user for having not visited the community Type matching second user；

Optionally, similar users are carried out in second user to the first user of each type in the way of look-alike Diffusion, find out same type and have not visited the user of micro- community.That is, the searching unit includes：Searching module, for from Have not visited the targeted parameter value that the second user is searched in the set of the second user of the community and first user The difference of targeted parameter value be in the second user in preset range；Configuration module, for being used find described second The type of corresponding first user of the type configuration at family.

First acquisition unit 74, for obtain first user the target dimension the first index, wherein it is described First index be used to indicate first user before accessing the community first time period and after accessing the community Second time period in Behavioral change；

Second acquisition unit 76, for obtain the second user the target dimension the second index, wherein it is described Second index is used to indicate Behavioral change of the second user in the first time period and the second time period.

Optionally, the first acquisition unit includes：First determining module is used for first user described in access The first parameter in the first time period after community and first user before accessing the community described the The ratio of the second parameter in two periods is as first index；The second acquisition unit includes：Second determining module, For by first parameter of the second user in the first time period with the second user in the second time period The ratio of the second interior parameter is as second index.

Processing unit 78, for being optimized according to the data of community described in first index and second exponent pair Processing, wherein the community after optimization processing is for making Activity Index of first user under the target dimension carry It is high.

The first parameter and the second parameter of second user be mainly with reference to effect, in order to eliminate community operation activity, when Between the factors such as period influence so that data processing is more accurate.Using the ratio of the first index and the second index to community Data optimize.I.e. optionally, the processing unit includes：4th determining module, for utilizing first index and institute The ratio for stating the second index determines behavioral implications degree of the community to first user under the target dimension；Update Module, the content for updating community's displaying according to the behavioral implications degree.

Compare the first user and access the front and back Parameters variation under target dimension in community, while comparing second user identical Parameters variation in period under target dimension, so that it is determined that going influence of the community to user behavior, that is, community to mesh Mark dimension under parameter influence, accordingly come optimize community displaying content.For example, update column, the content for updating column, update Page layout etc..

For the content of more targeted update and optimization community, it can first judge often to access the crowd of community big Belong in disk crowd (all users with the relevant product in community, including all users for accessing community and having not visited community) In what quality, which can be weighed by the activity of the user and two dimensions of payment, that is, described device is also wrapped It includes：First index unit, for determine accessed community the first user set each of the first user type before, Obtain the third index for being integrated into the target dimension of first user, wherein the third index, which is used to indicate, to be had First user of target signature ratio shared in the set of first user；Second index unit, for obtaining reference It is integrated into the 4th index of the target dimension, wherein the reference set includes first user set and described second User gathers, and the 4th index is used to indicate the ratio shared in the reference set of the reference with the target signature Example；Third index unit, for obtaining target group's index according to the third index and the 4th index, wherein described Target group's index is used to indicate in the relatively described reference set of the first user with the target signature with the target The surging degree with reference in the target dimension of feature.

Society is carried out using the method for similar target group's index (Target Group Index, TGI) in the present embodiment The analytical judgment of area's aggregate users quality.Target group's index is a kind of " tendency sex index ", refers to a certain subgroup, a certain index Ratio, the ratio between with the same index ratio of total group, multiplied by with the value of 100 gained of criterion numeral.Target group's indicial response be Surging degree of the target group in index dimension to be analyzed in the field analyzed.Target can be described well with the index The feature of group.Effective data foundation is provided for policy development, value judgement.

The computational methods of target group's index are：

Another aspect according to the ... of the embodiment of the present invention additionally provides a kind of processing side for implementing above-mentioned community data The electronic device of method is stored with calculating as shown in figure 8, the electronic device includes, including memory and processor in the memory Machine program, the processor are arranged to execute the step in any of the above-described embodiment of the method by computer program.

Optionally, Fig. 8 is a kind of structure diagram of electronic device according to the ... of the embodiment of the present invention.As shown in figure 8, the electronics Device may include：One or more (one is only shown in figure) processors 801, at least one communication bus 802, user interface 803, at least one transmitting device 804 and memory 805.Wherein, communication bus 802 is for realizing the connection between these components Communication.Wherein, user interface 803 may include display 806 and keyboard 807.Transmitting device 804 may include optionally standard Wireline interface and wireless interface.

Optionally, in the present embodiment, above-mentioned electronic device can be located in multiple network equipments of computer network At least one network equipment.

Optionally, in the present embodiment, above-mentioned processor can be set to execute following steps by computer program：

S1 determines the type for accessing the first user of each of the first user set of community, wherein each described the The type of one user is a target type under a target dimension；

S2 is searched and the type matching of first user from the set of second user for having not visited the community Second user；

S3 obtains first user and is tieed up in the target in the first index of the target dimension and the second user Second index of degree, wherein when first index is used to indicate first of first user before accessing the community Between section and access the Behavioral change in second time period after the community, second index is used to indicate described second and uses Behavioral change of the family in the first time period and the second time period；

S4 optimizes processing, wherein excellent according to the data of community described in first index and second exponent pair Change the Activity Index that treated the community is used to make first user under the target dimension to improve.

Optionally, it will appreciated by the skilled person that structure shown in Fig. 8 is only to illustrate, electronic device also may be used To be smart mobile phone (such as Android phone, iOS mobile phones), tablet computer, palm PC and mobile internet device The terminal devices such as (Mobile Internet Devices, MID), PAD.Fig. 8 it does not cause the structure of above-mentioned electronic device It limits.For example, electronic device may also include more than shown in Fig. 8 or less component (such as network interface, display device Deng), or with the configuration different from shown in Fig. 8.

Wherein, memory 805 can be used for storing software program and module, such as the community data in the embodiment of the present invention Corresponding program instruction/the module for the treatment of method and apparatus, processor 801 are stored in the software journey in memory 805 by operation Sequence and module realize the processing method of above-mentioned community data to perform various functions application and data processing.It deposits Reservoir 805 may include high speed random access memory, can also include nonvolatile memory, as one or more magnetic storage fills It sets, flash memory or other non-volatile solid state memories.In some instances, memory 805 can further comprise relative to place The remotely located memory of device 801 is managed, these remote memories can pass through network connection to terminal.The example packet of above-mentioned network Include but be not limited to internet, intranet, LAN, mobile radio communication and combinations thereof.

Above-mentioned transmitting device 804 is used to receive via a network or transmission data.Above-mentioned network specific example It may include cable network and wireless network.In an example, transmitting device 804 includes a network adapter (Network Interface Controller, NIC), can be connected with other network equipments with router by cable so as to interconnection Net or LAN are communicated.In an example, transmitting device 804 is radio frequency (Radio Frequency, RF) module, For wirelessly being communicated with internet.

Wherein, specifically, memory 805 is used to store information, the Yi Jiying of deliberate action condition and default access user Use program.

The embodiments of the present invention also provide a kind of storage medium, computer program is stored in the storage medium, wherein The computer program is arranged to execute the step in any of the above-described embodiment of the method when operation.

Optionally, in the present embodiment, above-mentioned storage medium can be set to store by executing based on following steps Calculation machine program：

Optionally, storage medium is also configured to store the computer program for executing following steps：By described first First parameter of the user in the first time period after accessing the community is accessing the society with first user The ratio of the second parameter in the second time period before area is as first index；By the second user described The ratio of the first parameter and second parameter of the second user in the second time period in first time period is as institute State the second index.

Optionally, storage medium is also configured to store the computer program for executing following steps：Obtain the mesh Mark the multiple types under dimension；The value range of target component in the target dimension is determined according to the multiple types；According to Each described first uses during the targeted parameter value of the value range and each first user gather first user Classify at family；Determine that the type of first user is the target type in the multiple types.

Optionally, storage medium is also configured to store the computer program for executing following steps：It is poly- using user First user set is clustered into the multiple types by the mode of class in first dimension and second dimension, In, each type includes the parameter value range of the parameter value range and second dimension of first dimension.

Optionally, storage medium is also configured to store the computer program for executing following steps：From having not visited The targeted parameter value that the second user is searched in the set of the second user of the community and the target of first user are joined The difference of numerical value is in the second user in preset range；The type configuration of the second user found is described accordingly The type of first user.

Optionally, storage medium is also configured to store the computer program for executing following steps：Obtain described The third index for being integrated into the target dimension of one user, wherein the third index is used to indicate with target signature First user ratio shared in the set of first user；Acquisition reference set refers to the 4th of the target dimension Number, wherein the reference set includes first user set and the second user set, and the 4th index is for referring to Show the ratio shared in the reference set of the reference with the target signature；According to the third index and the described 4th Index obtains target group's index, wherein target group's index is used to indicate the first user with the target signature The surging degree with reference in the target dimension with the target signature in the relatively described reference set.

Optionally, storage medium is also configured to store for executing step included in the method in above-described embodiment Computer program, this is repeated no more in the present embodiment.

Optionally, in the present embodiment, one of ordinary skill in the art will appreciate that in the various methods of above-described embodiment All or part of step be that can be completed come command terminal device-dependent hardware by program, which can be stored in In one computer readable storage medium, storage medium may include：Flash disk, read-only memory (Read-Only Memory, ROM), random access device (Random Access Memory, RAM), disk or CD etc..

If the integrated unit in above-described embodiment is realized in the form of SFU software functional unit and as independent product Sale in use, can be stored in the storage medium that above computer can be read.Based on this understanding, skill of the invention Substantially all or part of the part that contributes to existing technology or the technical solution can be with soft in other words for art scheme The form of part product embodies, which is stored in a storage medium, including some instructions are used so that one Platform or multiple stage computers equipment (can be personal computer, server or network equipment etc.) execute each embodiment institute of the present invention State all or part of step of method.

In the above embodiment of the present invention, all emphasizes particularly on different fields to the description of each embodiment, do not have in some embodiment The part of detailed description may refer to the associated description of other embodiment.

In several embodiments provided herein, it should be understood that disclosed client, it can be by others side Formula is realized.Wherein, the apparatus embodiments described above are merely exemplary, for example, the unit division, only one Kind of division of logic function, formula that in actual implementation, there may be another division manner, such as multiple units or component can combine or It is desirably integrated into another system, or some features can be ignored or not executed.Another point, it is shown or discussed it is mutual it Between coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some interfaces, unit or module It connects, can be electrical or other forms.

The unit illustrated as separating component may or may not be physically separated, aobvious as unit The component shown may or may not be physical unit, you can be located at a place, or may be distributed over multiple In network element.Some or all of unit therein can be selected according to the actual needs to realize the mesh of this embodiment scheme 's.

In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also It is that each unit physically exists alone, it can also be during two or more units be integrated in one unit.Above-mentioned integrated list The form that hardware had both may be used in member is realized, can also be realized in the form of SFU software functional unit.

The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered It is considered as protection scope of the present invention.

Claims

1. a kind of processing method of community data, which is characterized in that including：

Determine the type for accessing the first user of each of the first user set of community, wherein the class of first user Type is a target type under target dimension；

From the set of second user for having not visited the community, searches and used with the second of the type matching of first user Family；

Obtain first user the target dimension the first index, wherein first index is used to indicate described Behavior of one user in the first time period before accessing the community and the second time period after the access community becomes Change；

Obtain the second user the target dimension the second index, wherein second index is used to indicate described Behavioral change of two users in the first time period and the second time period；

Processing is optimized according to the data of community described in first index and second exponent pair.

2. according to the method described in claim 1, it is characterized in that, obtaining first user the first of the target dimension Index and the second user include in the second index of the target dimension：

First parameter of first user in the first time period after accessing the community is used with described first The ratio of second parameter of the family in the second time period before accessing the community is as first index；

By first parameter of the second user in the first time period and the second user in the second time period The ratio of the second interior parameter is as second index.

3. according to the method described in claim 1, accessing each of the first user set of community it is characterized in that, determining The type of first user includes：

Obtain the multiple types under the target dimension；

The value range of target component in the target dimension is determined according to the multiple types；

Each institute in being gathered first user according to the targeted parameter value of the value range and each first user The first user is stated to classify；

Determine that the type of first user is the target type in the multiple types.

4. according to the method described in claim 3, it is characterized in that, the target dimension include the first dimension and the second dimension, The multiple types obtained under the target dimension include：

First user set is clustered into first dimension and second dimension by the way of user clustering The multiple types, wherein each type includes the parameter of the parameter value range and second dimension of first dimension Value range.

5. according to the method described in claim 3, it is characterized in that, from the set of second user for having not visited the community It searches and includes with the second user of the type matching of first user：

The targeted parameter value of the second user and described the are searched from the set of second user for having not visited the community The difference of the targeted parameter value of one user is in the second user in preset range；

By the type of corresponding first user of the type configuration of the second user found.

6. according to the method described in claim 1, it is characterized in that, every in determining the first user for accessing community set Before the type of a first user, the method further includes：

Obtain the third index for being integrated into the target dimension of first user, wherein the third index is used to indicate The ratio shared in the set of first user of the first user with target signature；

Fourth index of the acquisition reference set in the target dimension, wherein the reference set includes that first user collects It closes and the second user set, the 4th index is used to indicate the reference with the target signature in the reference set In shared ratio；

Target group's index is obtained according to the third index and the 4th index, wherein target group's index is used for Indicate that the reference with the target signature exists in the relatively described reference set of the first user with the target signature Surging degree in the target dimension.

7. according to the method described in claim 1, it is characterized in that, according to described in first index and second exponent pair The data of community optimize processing：

Determine the community to first user in the target using the ratio of first index and second index Behavioral implications degree under dimension；

The content of community's displaying is updated according to the behavioral implications degree.

8. a kind of processing unit of community data, which is characterized in that including：

Determination unit, the type for the first user of each of determining the first user set for accessing community, wherein each The type of first user is a target type under a target dimension；

Searching unit, for searching the type with first user from the set of second user for having not visited the community Matched second user；

First acquisition unit, for obtain first user the target dimension the first index, wherein it is described first refer to Number be used to indicate first user before accessing the community first time period and access after the community second Behavioral change in period；

Second acquisition unit, for obtain the second user the target dimension the second index, wherein it is described second refer to Number is used to indicate Behavioral change of the second user in the first time period and the second time period；

Processing unit, for optimizing processing according to the data of community described in first index and second exponent pair, Wherein, the Activity Index that the community after optimization processing is used to make first user under the target dimension improves.

9. device according to claim 8, which is characterized in that

The first acquisition unit includes：First determining module is used for first user after accessing the community The first parameter in the first time period is with first user in the second time period before accessing the community The second parameter ratio as first index；

The second acquisition unit includes：Second determining module is used for the second user in the first time period The ratio of first parameter and second parameter of the second user in the second time period is as second index.

10. device according to claim 8, which is characterized in that the determination unit includes：

Acquisition module, for obtaining the multiple types under the target dimension；

Value module, the value range for determining target component in the target dimension according to the multiple types；

Sort module is used for according to the targeted parameter value of the value range and each first user to first user Each first user classifies in set；

Third determining module, for determining that the type of first user is the target type in the multiple types.

11. device according to claim 10, which is characterized in that the target dimension includes the first dimension and the second dimension Degree, the acquisition module include：

Submodule is clustered, is used for described first by the way of user clustering in first dimension and second dimension User's set is clustered into the multiple types, wherein each type includes the parameter value range of first dimension and described The parameter value range of second dimension.

12. device according to claim 10, which is characterized in that the searching unit includes：

Searching module, the target for searching the second user from the set of second user for having not visited the community are joined The difference of numerical value and the targeted parameter value of first user is in the second user in preset range；

Configuration module, for by the type of corresponding first user of the type configuration of the second user found.

13. device according to claim 8, which is characterized in that described device further includes：

First index unit, for determine accessed community the first user set each of the first user type it Before, obtain the third index for being integrated into the target dimension of first user, wherein the third index is used to indicate tool The ratio for having the first user of target signature shared in the set of first user；

Second index unit, for obtain reference set the target dimension the 4th index, wherein the reference set packet It is used to indicate the ginseng with the target signature containing first user set and the second user set, the 4th index Examine shared ratio in the reference set；

Third index unit, for obtaining target group's index according to the third index and the 4th index, wherein described Target group's index is used to indicate in the relatively described reference set of the first user with the target signature with the target The surging degree with reference in the target dimension of feature.

14. a kind of storage medium, which is characterized in that be stored with computer program in the storage medium, wherein the computer Program is arranged to execute the method described in any one of claim 1 to 7 when operation.

15. a kind of electronic device, including memory and processor, which is characterized in that be stored with computer journey in the memory Sequence, the processor are arranged to execute the side described in any one of claim 1 to 7 by the computer program Method.