CN108038739A

CN108038739A - A kind of method and system that extending user is determined according to the statistics degree of association

Info

Publication number: CN108038739A
Application number: CN201711446826.0A
Authority: CN
Inventors: 罗维
Original assignee: Beijing Qihoo Technology Co Ltd
Current assignee: Beijing Qihoo Technology Co Ltd
Priority date: 2017-12-27
Filing date: 2017-12-27
Publication date: 2018-05-15

Abstract

The invention discloses a kind of method and system that extending user is determined according to the statistics degree of association, by being scheduled to the customer demand of input, obtain the negative sample collection for positive sample collection and user completely unrelated with the basic user composition for including basic user, the user characteristics training pattern of user is concentrated by positive sample collection and negative sample, obtain computation rule, calculate the degree of association score value that user is concentrated with positive sample for each user of the whole network one by one according to computation rule, be expanded user according to degree of association score value.Client can obtain the audient's demographic data to match with oneself actual demand, and precision is high, can fully meet the different demands of client.

Description

A kind of method and system that extending user is determined according to the statistics degree of association

Technical field

Determined the present invention relates to Internet technical field, and more particularly, to one kind according to the statistics degree of association The method and system of extending user.

Background technology

In Internet advertising field, for the businessman for launching advertisement, advertisement is launched to any crowd on a large scale, is deposited It is too high in cost, it is difficult to which that the problem of bearing, how from substantial amounts of netizen, pointedly selects suitable crowd, further according to not Each determined property with crowd goes out to need the advertisement crowd launched, is Internet advertising market development urgent problem.

At present, it is in Internet advertising field to provide more valuable crowd to advertiser using crowd's orientation method One important step, crowd's orientation method are by the analysis to user characteristic data, are found out and seed crowd behaviour feature The joint act feature of similar potential target crowd, using machine learning model, predicts target audience's demographic data, side is wide Accuse the main target group for finding and oneself being look for.The scale of wherein involved seed demographic data is at most in millions of amounts Level, and the scale of non-seed demographic data, when machine learning model is trained, can cause in several hundred million magnitudes, both ratios great disparity Memory adds model training and the memory overhead and time overhead of prediction using larger waste is above had.

Meanwhile, it is necessary to encoded to plaintext feature in some Machine learning tools, then can just do model training and Prediction, for example currently have 10,000,000 different characteristics, it is necessary to be encoded to them with 1 to 1,000 ten thousand, possible feature " accessed Sports.sina.com.cn " is encoded as 11, and feature " searching for tourism " is encoded as 999.

In traditional scheme, using unit feature coding, i.e., using single machine, the file of storage feature is traveled through, Encode successively at the same time.There are following 2 points deficiencies for the program：

If 1) tag file is especially big, than the feature if any tens times, then scheme operation is slower；

2) if tag file is originally to be stored in HDFS, while requires the tag file after coding to be also stored in HDFS On, then need first to download lower data from HDFS in this way, while the tag file after coding is uploaded on HDFS, and These can give exploitation and maintenance to bring extra work.

The content of the invention

To solve the above-mentioned problems, there is provided a kind of method and system that extending user is determined according to the statistics degree of association.

According to an aspect of the invention, there is provided a kind of method that extending user is determined according to the statistics degree of association, The described method includes：

Obtain the statistics associated with the network behavior of all users in data network, and to the statistics into Row feature extraction is with the user characteristics of definite all users；

The extended requests that fellow users extension is carried out to basic user are received, the extended requests are parsed to determine The setting quantity of extending user and the positive sample collection including multiple basic users；

Determine to include multiple training users' according to all users in the data network and the multiple basic user Negative sample collection, wherein the ratio of the basic user and the quantity of training user is less than or equal to predetermined threshold；

The user characteristics of the multiple training users concentrated to the negative sample carries out signature analysis, to determine to be used for each The computation rule that user's degree of being associated calculates；

The degree of association score value of each user in all users is calculated based on the computation rule, according to the degree of association point The descending order of value is ranked up all users to generate user list；And

The user of the highest setting quantity of degree of association score value in the user list for eliminating the multiple basic user is true It is set to extending user.

Preferably, the method further includes：By degree of association score value in the user list for not removing the multiple basic user The user of highest setting quantity is determined as extending user.

Preferably, according to the statistics of the network behavior off-line data of all users of data network, all users are extracted User characteristics.

Preferably, the network behavior of the user includes：Search click behavior, browse webpage behavior and/or by the 3rd The behavior that Fang Hezuo is obtained.

Preferably, the user characteristics includes：The host features of user, n-gram features, surf time section, belonging to online Region and/or browse commodity behavior.

Preferably, the set for the multiple user properties selected according to client and the basic user quantity of client's input, really Determine the positive sample collection of basic user.

Preferably, by all users in the data network and the multiple basic user the user characteristics degree of association The less user of score value, classification become the negative sample collection for including multiple training users.

Preferably, filtering the obvious abnormal dirty sample data of user characteristics in all users in the data network, obtain To negative sample collection.

Preferably, negative sampling is carried out to the user characteristics of all users in the data network, according to the setting threshold Value and basic user quantity, obtain the quantity of the negative sample concentration training user.

Preferably, concentrate multiple training users and the user of the basic user of positive sample concentration special to the negative sample Sign is extracted respectively, compares the relevance of the two, extracts the computation rule.

Preferably, according to the computation rule, the user characteristics of multiple users in the data network is compared one by one To calculating, each user and the degree of association score value of the basic user are assigned according to contrast conting result.

Preferably, multiple users in the data network are ranked up according to its degree of association score value, and to the knot of sequence Fruit is adjusted according to user property.

Preferably, all users in the data network are carried out with negative sampling obtains the negative sample collection, to the basis User carries out positive sampling and obtains positive sample collection；The negative sampling and the downsampling factor just sampled are set as needed.

Preferably, the negative sampling and the downsampling factor just sampled concentrate basic user number according to the positive sample of actual needs Amount and the setting of negative sample concentration training number of users.

According to another aspect of the present invention, there is provided a kind of to be according to what the statistics degree of association determined extending user System, the system comprises：

User characteristics unit, for obtaining the statistics associated with the network behavior of all users in data network, And feature extraction is carried out to the statistics to determine the user characteristics of all users；

Positive sample collection unit, carries out basic user for receiving the extended requests of fellow users extension, to the extension Request is parsed the setting quantity to determine extending user and the positive sample collection including multiple basic users；

Negative sample collection unit, for determining to wrap according to all users in the data network and the multiple basic user The negative sample collection of multiple training users is included, is made a reservation for wherein the ratio of the basic user and the quantity of training user is less than or equal to Threshold value；

Computation rule unit, the user characteristics of multiple training users for being concentrated to the negative sample carry out feature point Analysis, to determine the computation rule for calculating each user's degree of being associated；

Calculation of relationship degree unit, for calculating the degree of association point of each user in all users based on the computation rule Value, is ranked up to generate user list all users according to the descending order of the degree of association score value；

Extending user unit, for degree of association score value in the user list for eliminating the multiple basic user is highest The user of setting quantity is determined as extending user.

Preferably, the system also includes：By degree of association score value in the user list for not removing the multiple basic user The user of highest setting quantity is determined as extending user.

According to another aspect of the present invention, there is provided a kind of mobile terminal, including or for performing as above any one The system.

In the scheme that each embodiment of the present invention is provided, by being scheduled to the customer demand of input, including The negative sample collection of the positive sample collection of basic user and user completely unrelated with basic user composition, by positive sample collection and bears The user characteristics training pattern of user, obtains computation rule in sample set, is counted one by one for each user of the whole network according to computation rule The degree of association score value that user is concentrated with positive sample is calculated, is expanded user according to degree of association score value.Client can obtain real with oneself Audient's demographic data that border demand matches, precision is high, can fully meet the different demands of client.

Described above is only the general introduction of technical solution of the present invention, in order to better understand the technological means of the present invention, And can be practiced according to the content of specification, and in order to allow above and other objects of the present invention, feature and advantage can Become apparent, below especially exemplified by the embodiment of the present invention.

Brief description of the drawings

By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area Technical staff will be clear understanding.Attached drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention Limitation.And in whole attached drawing, identical component is denoted by the same reference numerals.In the accompanying drawings：

Fig. 1 is DMP Look-alike online system structure diagrams provided in an embodiment of the present invention.

Fig. 2 is the method flow diagram provided in an embodiment of the present invention that extending user is determined according to the statistics degree of association.

Fig. 3 is the system structure signal provided in an embodiment of the present invention that extending user is determined according to the statistics degree of association Figure.

Fig. 4 is the method flow diagram provided in an embodiment of the present invention that extending user is determined according to weighted calculation.

Fig. 5 is the system structure diagram provided in an embodiment of the present invention that extending user is determined according to weighted calculation.

Fig. 6 is the method flow diagram provided in an embodiment of the present invention that extending user is determined according to statistics interest-degree.

Fig. 7 is the system structure signal provided in an embodiment of the present invention that extending user is determined according to statistics interest-degree Figure.

Fig. 8 is the method flow diagram provided in an embodiment of the present invention for being used to carry out user characteristics distributed coding.

Fig. 9 is the system structure diagram provided in an embodiment of the present invention for being used to carry out user characteristics distributed coding.

Embodiment

The illustrative embodiments of the present invention are introduced referring now to attached drawing, however, the present invention can use many different shapes Formula is implemented, and is not limited to the embodiment described herein, there is provided these embodiments are to disclose at large and fully The present invention, and fully pass on the scope of the present invention to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements use identical attached Icon is remembered.

Unless otherwise indicated, term (including scientific and technical terminology) used herein has person of ordinary skill in the field It is common to understand implication.Further it will be understood that the term limited with usually used dictionary, be appreciated that and its The linguistic context of association area has consistent implication, and is not construed as Utopian or overly formal meaning.

The each embodiment of the present invention be based on the DMP Look-alike online systems shown in Fig. 1, as shown in Figure 1, its In：

Offline flows：Based on Distributed Computing Platform (Hadoop+Spark), to the network behavior (ratio of the whole network user Such as：Search click behavior, browse webpage behavior, by behavior for being obtained with third company cooperation etc.) extraction user characteristics (compares Such as：Host features, n-gram features, the surf time section, online belonging to region, browse commodity etc.).

Online flows：The whole network user characteristics and scheduler calculated based on offline flows is sent out by task scheduling The seed crowd come, using appropriate machine learning model (such as in disaggregated model in supervised learning, unsupervised learning Clustering Model etc.), by model training and prediction, find similar target audience crowd.

The demand of advertiser greatly with the limited always contradiction of computing resource.In order to ensure the profit of each advertiser from the overall situation Benefit, scheduler modules therein with regard to extremely important, it can consider factors (such as：What advertiser have submitted Look-alike number of tasks, advertiser are DSP launches the consuming capacity at end, advertiser's special demand (for example double 11), is It is not no mine massively to same Ziren with multiple extension multiples) look-alike mission requirements are dispatched, once some look-alike Task is dispatched, then can start online flows and calculate it similar target audience crowd.

During similar crowd is excavated, crowd's extension, i.e., look-alike Main Basiss user base attribute and its The behavioural information possessed, this just needs huge data storage as analysis source.Data management platform (DMP, Data- Management Platform) be crowd's growth data analysis method basis.Crowd's growth data analysis method development company Can be based on a large number of users itself covered, collection and depth excavate the number greatly of corresponding behavioral chain on the premise of individual privacy is protected According to the search of such as user is clicked on, browses webpage network behavior data.In general, client, i.e. advertiser will be apparent that oneself The product of advertisement and behind is wanted to touch the user group reached, for example App advertiser can be accurately grasped in oneself App product Any active ues IMEI or IFA, electric business website advertiser have the cookie or cell-phone number, O2O of user interested in certain commodity Advertiser might have telephone number of client etc..Therefore, the first party data that client has by oneself can also be obtained, as official website visitor, Place an order, pay close attention to the data such as wechat, concern microblogging and installation mobile application client.It can also be obtained outer by cooperating with third party The labeling data of portion affiliate, for example, user access website, using APP, watch video, place an order shopping and connection hot spot etc. Network behavior data.Thus the network behavior off-line data of the user obtained becomes the integration of DMP emphasis after anonymous desensibilization Critical data.

When obtaining the network behavior off-line data of user, Distributed Computing Platform (Hadoop+Spark) can be also based on, According to the network behavior daily record of user, the network behavior off-line data of acquisition the whole network user, by the network behavior daily record of user Data good data can be provided for researchs such as user interest discovery, resource recommendations support, easy to the user's according to acquisition Network behavior off-line data extracts user characteristic data.

By long-term data acquisition and subdivision, user characteristics number can be extracted according to the network behavior off-line data of user According to, such as the extraction host features of user, n-gram features, surf time section, region belonging to online and/or browse commodity behavior Deng user characteristics, user is finely divided management, from latitudes such as the action trail of user, interest preference, consumer behavior, geographical locations Degree realizes seeing clearly and analyzing for all types of user, obtains the characteristic that all types of user matches, is stored, the use of daily output The disk storage overhead of family characteristic is more than TB magnitudes.Delineation mesh can be freely combined in the user characteristic data that analyze extraction Mark crowd, launch displaying, search, brand, using downloads ad when, can quickly and accurately be directed to a certain category feature people Group.

The demand of client greatly with the limited always contradiction of computing resource, in order to ensure the interests of each client from the overall situation, Agree with the personalized focal need of client, need to consider all multi-parameters in scheduling process to dispatch look-alike mission requirements, Specifically, default scheduling parameter includes look-alike number of tasks, client that client have submitted and launches end in DSP Whether consuming capacity, client's special demand and/or client mine massively same Ziren extends multiples etc. with multiple, once some Look-alike tasks are dispatched, then can be according to seed demographic data and user characteristic data after obtaining seed demographic data To calculating the target audience crowd similar to seed demographic data, more similar target audiences are found, expands precision marketing and covers Lid scope.In addition, may also be combined with the first party data that client has by oneself, such as official website visitor, place an order, pay close attention to wechat, concern microblogging and The data such as mobile application client are installed as seed demographic data, in the same of the accurately customer data grasped using client When, it can also meet the personalized customization demand of client.

Client can be based on its own demand, it is autonomous determine look-alike number of tasks, its consumption energy at end is launched in DSP Power, special needs, such as the scheduling parameter such as double 11, Holiday Sale, can also independently determine user's magnitude after extension, be No to mine massively to same Ziren with multiple extension multiples, the specific multiple that extends is how many, passes through and the customer demand of input is carried out Scheduling, obtains seed demographic data, carries out automation extension easy to the follow-up shared attribute according to seed demographic data, can fill Divide and meet different advertisers to the precisely different demands with covering.

DSP (Demand Side Platform) described above is party in request's platform, it is responsible for receiving dispensing demand, Look for demographic data, realize a central management control platform for launching the function such as bid, it is from being mainly characterized by precise positioning mesh Mark crowd.For example, advertiser when launching advertisement, dispensing demand is inputted on DSP, target audience is drawn a circle to approve according to dispensing demand Description, such as age, gender, occupation and hobby etc. can also set fixed condition, as user clicked on every time using PC it is wide The unit price of announcement is no more than 2 points of, and then these conditions are sent in dsp system, and dsp system is linked up with DMP, according to DMP systems The user characteristic data of middle analysis extraction, is found out the crowd of condition coupling, is carried out by using actual environments such as media resources Advertisement putting.

After seed demographic data is obtained, seed crowd need to be analyzed from multiple dimensions, therefrom filtered out most Representational common characteristic, according to these feature combination user characteristic datas, filters out another batch from a large amount of any active ues The user similar to seed crowd.Specifically：Computation model can be established according to machine learning, which can use supervision to learn The Clustering Model in disaggregated model or unsupervised learning in habit, seed demographic data and user characteristic data are substituted into and calculate mould Type, carries out calculating analysis, obtains the user similar to seed crowd, and the user scope that can orient advertisement is from extensive characteristic According to more accurate user is contracted to, advertiser is met to the precisely different demands with covering, improves crowd's expansion efficiency.

In fact, a simple examples of the above-mentioned simply embodiment of the present invention, particular content of the invention will be by following Each embodiment describes one by one.

As shown in Fig. 2, be the method provided in an embodiment of the present invention that extending user is determined according to the statistics degree of association, its In,

Step 201, the statistics associated with the network behavior of all users in data network is obtained, and to the system Count and carry out feature extraction to determine the user characteristics of all users.

Data network can be general internet data or various dedicated networks., wherein it is desired to obtain data The network behavior data of all users in network.The network behavior of user can pass through various operations of the user during online To obtain, for example, which website can be logged in including user, which content is browsed, or watches which video to determine.User Network behavior it is varied, can be the operation content of user or the behavior trace content of user.

Obtaining user network behavior can be carried out by recording the network behavior of user, can also be soft by various networks Hardware obtains.In fact, the acquisition of user network behavior, is more sorted out and is recorded based on the analysis to user network behavior.

, can be by user network by various statistical analyses, it is necessary to carry out statistical analysis after the network behavior of user is recorded Network behavior is sorted out.User network behavior includes polytype behavior and plurality of kinds of contents, it is necessary to classification storage.Deposited in classification On the basis of storage, it is counted, so as to obtain statistics.Include in statistics all user network behaviors with And the various possible network behaviors by user network behavior sorted generalization.The network behavior of user can include：Search is clicked on Behavior, browse webpage behavior and/or the behavior obtained by third party's cooperation.

In fact, the analysis to user network behavior, can extract user characteristics.User characteristics is that user registration exists Various actions feature on network.User characteristics is the motion characteristic of user, including operation behavior of the user on network and can The action behavior of energy.User characteristics has characterized each action details of user's operation, so as to therefrom determine user's debarkation net The behavioural habits of network and corresponding prediction is made to its behavior.

User characteristics can include the host features of user, n-gram features, surf time section, region belonging to online and/ Or browse commodity behavior.User characteristics is different from user property.User property is usually the fixed attribute of user, including with The user identifier at family, age, IP etc. are attached to the attribute information of user itself.And user characteristics is dynamic during user's online Make behavioural information, be the operability breath of user in a network, be dynamic.

Thus, in the environment of mass users, user property is attached to number of users, and magnanimity.And user characteristics, Due to being related to a variety of behaviors of mass users, thus, its data volume is even more more huger than user attribute data.

Step 202, the extended requests that fellow users extension is carried out to basic user are received, the extended requests are solved Analysis is with the setting quantity of definite extending user and the positive sample collection including multiple basic users.

Basic user is the user that client is provided as seed.Client can be that advertiser etc. has user is expanded The client of exhibition.Basic user is put forward by client, is set according to the demand of client.Generally, the generation of basic user, The set for the multiple user properties that can be selected according to client and the basic user quantity of client's input, determine the multiple base Plinth user.Namely client sets basic user according to the user property that itself pays close attention to.

Client after above-mentioned look-alike systems are logged in, can select key user's attribute of itself concern, system According to user property corresponding basic user dynamic listing and quantity are provided for client.The group of the user property inputted according to client Change is closed, dynamic adjusts basic user dynamic listing, untill the quality and quantity of basic user meet the demand of client.

Client also needs to set at the same time the scale of required user's extension, namely client sets extension demand, according to expansion Exhibition demand determines the quantity of extending user.Used for example, client can obtain 1,000,000 bases by the adjustment to user property Family, then inputs overall extending user scale as 10,000,000.At this time, the scale of user's extension is 10 times.

After generating basic user, basic user can be incorporated into positive sample collection.Positive sample is grouped as basic user, passes through These basic users can extract the overall user feature of customer demand, so as to obtain the actual demand of client.

Step 203, determine to include multiple instructions according to all users in the data network and the multiple basic user Practice the negative sample collection of user, wherein the ratio of the basic user and the quantity of training user is less than or equal to predetermined threshold.

After obtaining positive sample collection, it is also necessary to which training obtains negative sample collection.Negative sample collection is all users out of data network User characteristics in extract user composition.Wherein, the user that negative sample is concentrated is the basic user phase concentrated with positive sample The minimum user's set of closing property.That is, negative sample concentrates user substantially to concentrate user completely unrelated with positive sample.

The acquisition of negative sample collection finds it, it is necessary to the user characteristics for aligning basic user in sample set carries out analysis extraction The general character of user characteristics, therefrom extracts the elemental user feature of basic user, then compares one by one in all users of whole network again To user characteristics, so as to find user corresponding with the user characteristics of elemental user feature correlation minimum.These users are put Enter negative sample collection.

The user that negative sample is concentrated also needs to carry out relevant further processing, to dispose wherein unrelated data. For example, it is desired to filter out the user data of obvious exception.

For ease of the training of follow-up computation model, the positive sample collection in the user characteristic data of said extracted can be demarcated It is negative class sample data by the negative sample collection data scaling in the user characteristic data of said extracted for positive class sample data.Cause For data characteristics, the scale of positive class sample is at most in millions of magnitudes, and the scale of negative class sample is in several hundred million magnitudes, so positive class The ratio of sample and negative class sample reaches 1:10 or even 1:100, this is unfavorable for machine learning model, particularly disaggregated model, learns Practise effective model.For this reason, the processing of following step can be made to above-mentioned sample.

Over-sampling is carried out to the positive class sample data, negative sampling is carried out to the negative class sample data.Specifically, can be with According to positive class sample data and the ratio of negative class sample data, the sample rate of adjustment setting over-sampling and time sampling, by multigroup Experiment, determines a feasible ratio.Preferably, before aligning class sample data and negative class sample data sampled, also The user characteristic data of above-mentioned acquisition can be analyzed and filter its apoplexy involving the solid organs sample data, avoid influencing the accurate of follow-up disaggregated model Property.

Step 204, the user characteristics of the multiple training users concentrated to the negative sample carries out signature analysis, to determine to use In the computation rule calculated each user's degree of being associated.

The sampled data obtained to the over-sampling and negative sampling trains computation model.Obtain positive sample collection data it Afterwards, user characteristics need to be analyzed from multiple dimensions, therefrom filters out most representational common characteristic, it is special according to these Sign combines user characteristic data, and another crowd of user similar to seed crowd is filtered out from a large amount of any active ues.Specifically：It is first First need select computation model, computation model may include logistic regression (logistic regression algorithm model) and/or The models such as linear SVM (supporting vector machine model), by the sampled data obtained through above-mentioned over-sampling and negative sampling using calculating Model carries out model training, obtains effective computation model.

Multiple training users and the user characteristics of the basic user of positive sample concentration is concentrated to distinguish to the negative sample Extracted, compare the relevance of the two, extract the computation rule.

Here computation model is computation rule, and complete computation rule is extracted by way of model training.

Step 205, the degree of association score value of each user in all users is calculated based on the computation rule, according to described The descending order of degree of association score value is ranked up all users to generate user list.

The computation rule obtained by above-mentioned model training, can do model prediction to the whole network user, be sorted based on prediction Go out user of the prediction point more than certain threshold value as extension crowd, i.e., similar target audience crowd, the use that advertisement can be oriented Family scope is contracted to more accurate user from extensive characteristic, meet advertiser to precisely and covering different demands, Improve crowd's expansion efficiency.

According to the computation rule, calculating is compared in the user characteristics of multiple users in the data network one by one, Each user and the degree of association score value of the basic user are assigned according to contrast conting result.Will be more in the data network A user is ranked up according to its degree of association score value, and the result of sequence is adjusted according to user property.

One degree of association score value generates each user by computation rule respectively, this degree of association score value characterizes user With the degree of association of basic user.By arrangement of all users with the size of degree of association score value from big to small, user list is obtained, its In include putting in order for all users and respective degree of association score value.

Step 206, by the highest setting quantity of degree of association score value in the user list for eliminating the multiple basic user User be determined as extending user.

After obtaining the relevant user's arrangement of specific degree of association score value, it can be chosen according to the size of degree of association score value The higher certain customers of middle degree of association score value are as extending user.Specifically quantity is determined according to the setting of client, Ke Yishi The extending user scale amounts of client's setting.

The basic user initially selected due to including client in the whole network user, and these basic users not necessarily degree of association Score value is higher, therefore, it is possible to the selection according to client, it is determined whether needs are deleted in final extending user recommendation list Basic user.

Delete basic user time can be before calculating correlation score value, can also calculating correlation score value it Afterwards.Alternatively, can be before or after extending user be recommended.

In the present embodiment, the customer demand of input is scheduled, obtains the positive sample collection and and base for including basic user The negative sample collection of the completely unrelated user's composition of plinth user, the user characteristics for concentrating user by positive sample collection and negative sample are instructed Practice model, obtain computation rule, calculated one by one for each user of the whole network according to computation rule and concentrate associating for user with positive sample Score value is spent, is expanded user according to degree of association score value.Client can obtain the audient crowd's number to match with oneself actual demand According to precision is high, can fully meet the different demands of client.

Fig. 3 shows that the present invention provides a kind of system that extending user is determined according to the statistics degree of association, the system System includes：

User characteristics unit 301, for obtaining the statistical number associated with the network behavior of all users in data network According to, and feature extraction is carried out to the statistics to determine the user characteristics of all users；

Positive sample collection unit 302, carries out basic user for receiving the extended requests of fellow users extension, to the expansion Exhibition request is parsed the setting quantity to determine extending user and the positive sample collection including multiple basic users；

Negative sample collection unit 303, for true according to all users in the data network and the multiple basic user Surely the negative sample collection of multiple training users is included, wherein the ratio of the basic user and the quantity of training user is less than or equal to Predetermined threshold；

Computation rule unit 304, the user characteristics of multiple training users for being concentrated to the negative sample carry out feature Analysis, to determine the computation rule for calculating each user's degree of being associated；

Calculation of relationship degree unit 305, for calculating the association of each user in all users based on the computation rule Score value is spent, all users are ranked up to generate user list according to the descending order of the degree of association score value；

Extending user unit 306, for by degree of association score value in the user list for eliminating the multiple basic user most The user of high setting quantity is determined as extending user.

Preferably, by the highest setting quantity of degree of association score value in the user list for not removing the multiple basic user User is determined as extending user.

As shown in figure 4, be a kind of method that extending user is determined according to weighted calculation provided in an embodiment of the present invention, its In,

Step 401, the statistics associated with the network behavior of all users in data network is obtained, and to the system Count and carry out feature extraction to determine the user characteristics of all users.

Step 402, the extended requests that fellow users extension is carried out to basic user are received, the extended requests are solved Analysis is with the setting quantity of definite extending user and multiple basic users.

Step 403, each corresponding sample set definite respectively being directed in multiple training rules set in advance, and root Signature analysis is carried out according to the user characteristics in each sample set to determine the computation rule calculated each user's degree of being associated.

Training rules set in advance, can include a variety of, it can be common that classification supervised training to the user characteristics, Cluster training to the user characteristics and/or the semi-supervised training to the user characteristics.

Supervised learning training of classifying is instructed by existing training sample (i.e. given data and its corresponding output) Practice, so as to obtain an optimal models, recycle this model that all new data samples are mapped as output accordingly as a result, The simple purpose judged so as to fulfill classification is carried out to output result, then this optimal models is also just provided with to unknown number According to the ability classified.

The unsupervised learning training of cluster is in advance without any training data sample, it is necessary to directly be built to data Mould.It is generally necessary to train cluster centre by clustering algorithm, exercised supervision study using cluster centre as classifying rules.

In addition, also semi-supervised learning training pattern, i.e., with reference to supervised learning training pattern and unsupervised learning training mould The prioritization scheme of type.For example, it may be the model that is clustered on basis of classification calculates or enterprising on cluster basis One step thinks that the model of interference classification calculates.

Specific training rules can be selected according to being actually needed, and be a variety of different training rule of selection in the present embodiment Then, the whole network user is trained respectively according to a variety of training rules, so that it is determined that sample set corresponding with a variety of training rules. Each sample set be as obtained from corresponding training rules, it is uncorrelated mutually.

User characteristics in each sample set further carries out signature analysis, it may be determined that goes out and all users are associated Spend the computation rule of analysis.Equally, each computation rule is to be directed to different training rules, orthogonal.

The present embodiment, sample set is respectively trained by multiple training rules, and computation rule is being extracted by sample set.Calculate The extraction of rule is typically by the way of model training.User characteristics is analyzed from multiple dimensions, is therefrom filtered out most Representative common characteristic, according to these feature combination user characteristic datas, filters out another from a large amount of any active ues Criticize the user similar to seed crowd.Specifically：Firstly the need of selection computation model, computation model may include logistic The models such as regression (logistic regression algorithm model) and/or linear SVM (supporting vector machine model), will be through above-mentioned sample The sampled data of this concentration carries out model training using computation model, obtains effective computation model.

According to the multiple training rules, corresponding sample set is determined respectively；According to the user in each sample set Feature analyzes all users, determines the degree of association of each user, and obtains the computation rule of calculation of relationship degree.

According to the computation rule, all user's degree of being associated are calculated respectively, obtain the degree of association point of each user Value；The user is ranked up according to the degree of association score value of each user.

Step 404, the pass of each user in all users is calculated based on each computation rule in multiple computation rules Connection degree score value, is ranked up all users according to the descending order of the degree of association score value to generate multiple user lists.

A variety of computation rules can calculate the degree of association score value of all users of a set of the whole network respectively.If for example, by three kinds Computation rule, then each user of the whole network 3 degree of association score values can be calculated.According to the degree of association score value of each user, difference The whole network user is arranged, obtains multiple user lists, namely every kind of computation rule corresponds to a user list, including All user and respective degree of association score value put in order.

Step 405, weighted value is set for each user list according to the accuracy of each training rules, according to each user The degree of association score value of each user is weighted in the weighted value of list, to determine each to use according to the result of weighted calculation The output score value at family.

According to the degree of association score value of each user, the accuracy of corresponding computation rule is calculated；According to described accurate Degree sets weighted value to corresponding computation rule.Since the accuracy of different computation rules is different, it is then desired to according to Its accuracy for each user list set a weighted value, this weighted value be according to the accuracy of corresponding computation rule come Setting.

According to the weighted value of each computation rule, the degree of association of each user obtained to each computation rule Score value is weighted processing.

Degree of association score value by each user respectively in multiple user lists is multiplied with its weighted value, then weighted calculation is most Whole output score value.Extending user is chosen according to final output score value.

In the present embodiment, it is the degree of association score value that aggregative weighted calculates each user under a variety of computation rules, then weights Calculate final score value.By the weighting that every kind of computation rule of each user obtains handle as a result, being multiplied or phase Add, obtain the output score value of each user.The user is ranked up according to the output score value of each user, output setting number The user of amount is as extending user.

Multiple users in data network are exported score value according to it to be ranked up, and to the result of sequence according to user Attribute is adjusted.The user of the highest setting quantity of score value will be exported in the user list for eliminating the multiple basic user It is determined as extending user.

In the present embodiment, multiple sample sets are trained by multiple training rules set in advance, and then determine multiple Computation rule；Respectively all users are carried out each user is calculated according to multiple computation rules and be directed to each computation rule Degree of association score value, then calculate the weighted value of each computation rule, the degree of association point of each computation rule corresponded to reference to user The weighted value of value and corresponding computation rule, each user's final output score value of weighted calculation, determines to set according to output score value The extending user of quantity.Client can obtain the audient's demographic data to match with oneself actual demand, and precision is high, can fully expire The different demands of sufficient client.

Fig. 5 shows a kind of system that extending user is determined according to weighted calculation provided in an embodiment of the present invention, described System includes：

User characteristics unit 501, for obtaining the statistical number associated with the network behavior of all users in data network According to, and feature extraction is carried out to the statistics to determine the user characteristics of all users；

Basic user unit 502, carries out basic user for receiving the extended requests of fellow users extension, to the expansion Exhibition request is parsed setting quantity and the multiple basic users to determine extending user；

Computation rule unit 503, it is each definite corresponding respectively in multiple training rules set in advance for being directed to Sample set, and user characteristics in each sample set carries out signature analysis to determine in terms of to each user's degree of being associated The computation rule of calculation；

Degree of association score value computing unit 504, for all to calculate based on each computation rule in multiple computation rules The degree of association score value of each user, is ranked up with life all users according to the descending order of the degree of association score value in user Into multiple user lists；And

Score value computing unit 505 is exported, for setting power according to the accuracy of each training rules for each user list Weight values, are weighted the degree of association score value of each user according to the weighted value of each user list, in terms of according to weighting The result of calculation determines the output score value of each user.

Preferably, the set for the multiple user properties selected according to client and the basic user quantity of client's input, really Fixed the multiple basic user.

Preferably, the multiple training rules, including it is classification supervised training to the user characteristics, special to the user The cluster training of sign and/or the semi-supervised training to the user characteristics.

Preferably, according to the multiple training rules, corresponding sample set is determined respectively；According in each sample set User characteristics all users are analyzed, determine the degree of association of each user, and obtain the computation rule of calculation of relationship degree.

Preferably, according to the computation rule, all user's degree of being associated are calculated respectively, obtain the pass of each user Connection degree score value；The user is ranked up according to the degree of association score value of each user.

Preferably, according to the degree of association score value of each user, the accuracy of corresponding computation rule is calculated；According to institute State accuracy and weighted value is set to corresponding computation rule.

Preferably, according to the weighted value of each computation rule, each user obtained to each computation rule Degree of association score value be weighted processing.

Preferably, by the weighting that every kind of computation rule of each user obtains handle as a result, being multiplied or phase Add, obtain the output score value of each user.

Preferably, the user is ranked up according to the output score value of each user, the use of output setting quantity Family is as extending user.

It is ranked up preferably, multiple users in data network are exported score value according to it, and to the result of sequence It is adjusted according to user property.

Preferably, the use for the highest setting quantity of score value being exported in the user list for eliminating the multiple basic user Family is determined as extending user.

Fig. 6 shows a kind of method that extending user is determined according to statistics interest-degree, the described method includes：

Step 601, the statistics associated with the network behavior of all users in data network is obtained, and to the system Count and carry out feature extraction to determine the user characteristics of all users.

Step 602, the extended requests that fellow users extension is carried out to basic user are received, the extended requests are solved Analysis is with the setting quantity of definite extending user and multiple basic users.

Step 603, user characteristics in set sample set carry out signature analysis with determine to each user into The computation rule of row calculation of relationship degree, and it is each in all users to calculate based on each computation rule in multiple computation rules The initial association degree score value of user.

Training rules are with including a variety of, it can be common that classification supervised training to the user characteristics, special to the user The cluster training of sign and/or the semi-supervised training to the user characteristics.

The degree of association score value of each user in all users is calculated based on each computation rule in multiple computation rules, All users are ranked up according to the descending order of the degree of association score value to generate multiple user lists.

Weighted value is set for each user list according to the accuracy of each training rules, according to the power of each user list The degree of association score value of each user is weighted in weight values, to determine that each user's is initial according to the result of weighted calculation Degree of association score value.

According to the computation rule, all user's degree of being associated are calculated respectively, obtain the initial association of each user Spend score value；The user is ranked up according to the initial association degree score value of each user.

Step 604, interest-degree extraction is carried out to the statistics to determine the interest-degree score value of each user, and base Initial association degree score value is adjusted in interest-degree score value and corrects degree of association score value to generate.

Each user has the interest of uniqueness during surfing the Internet again, can be extracted in the statistics of mass users corresponding Interest-degree, and then determine each user interest-degree score value.This interest-degree score value characterize each user relative to it is specific certain The interest-degree of one user characteristics.For example, for the interest-degree of browse advertisements or particular advertisement, each use is different per family, has User interest degree score value it is high, some user interest degree score values are low.

User characteristics is extracted according to the statistics of the network behavior of the user, according to the statistical number of the user characteristics Extracted according to the interest-degree of extraction user.Corresponding interest-degree score value, the user are calculated according to the interest-degree of user statistics There is different interest-degree score values relative to different interest-degrees.

Multiple basic users are obtained according to the fellow users that the client inputs, are determined according to the basic user relevant Interest-degree, carries out all users according to the interest-degree interest-degree extraction and calculates the interest-degree point corresponding to the interest-degree Value.

According to this interest-degree score value, the initial association degree score value of user can be corrected, obtain the correction degree of association Score value.The interest-degree score value is multiplied or is added with initial association degree score value, generates corrected correction degree of association score value. For example, user is zero for the interest-degree score value of browse advertisements, user shields all advertisements, no matter then the user Initial association degree score value is how high, and in weighted correction after interest-degree score value, it is zero that it, which corrects degree of association score value,.

Step 605, all users are ranked up to generate user according to the descending order of the correction degree of association score value List, the user of the highest setting quantity of the user list lieutenant colonel's positive association degree score value for eliminating the multiple basic user is true It is set to extending user.

By the correction degree of association score value descending arrangement, the correction degree of association of setting quantity is chosen according to described put in order The highest user of score value is as extending user.By all users in the data network according to the correction degree of association score value sequence Afterwards, the highest user of correction degree of association score value for choosing setting quantity is determined as extending user.

, can be according to the size of correction degree of association score value, choosing after obtaining the relevant user's arrangement of specific degree of association score value Take and wherein correct the higher certain customers of degree of association score value as extending user.Specific quantity is true according to the setting of client It is fixed, can be the extending user scale amounts of client's setting.

Multiple users in data network are corrected degree of association score value according to it to be ranked up, and to the result root of sequence It is adjusted according to user property.The highest setting quantity of score value will be exported in the user list for eliminating the multiple basic user User be determined as extending user.

The basic user initially selected due to including client in the whole network user, and these basic users not necessarily correct pass Connection degree score value is higher, therefore, it is possible to the selection according to client, it is determined whether needs in final extending user recommendation list Delete basic user.

In the present embodiment, multiple computation rules are determined by multiple training rules set in advance and sample set；According to Multiple computation rules carry out each user is calculated to all users is directed to the degree of association score value of each computation rule, then counts The weighted value of each computation rule is calculated, corresponds to the degree of association score value of each computation rule and corresponding computation rule with reference to user Weighted value, each user's initial association degree score value of weighted calculation；In conjunction with the interest-degree score value of user, weighted calculation is final Degree of association score value is corrected, the extending user of setting quantity is determined according to correction degree of association score value.Client can obtain actual with oneself Audient's demographic data that demand matches, precision is high, can fully meet the different demands of client.

Fig. 7 shows a kind of system that extending user is determined according to statistics interest-degree provided in an embodiment of the present invention, The system comprises：

User characteristics unit 701, for obtaining the statistical number associated with the network behavior of all users in data network According to, and feature extraction is carried out to the statistics to determine the user characteristics of all users；

Basic user unit 702, carries out basic user for receiving the extended requests of fellow users extension, to the expansion Exhibition request is parsed setting quantity and the multiple basic users to determine extending user；

Initial association degree computing unit 703, signature analysis is carried out for the user characteristics in set sample set With determine to each user's degree of being associated calculate computation rule, and based on each computation rule in multiple computation rules come Calculate the initial association degree score value of each user in all users；

Calculation of relationship degree unit 704 is corrected, for carrying out interest-degree extraction to the statistics with definite each user Interest-degree score value, and based on interest-degree score value initial association degree score value is adjusted with generate correct degree of association score value；

Extending user unit 705, for being arranged according to the descending order of the correction degree of association score value all users Sequence will eliminate the highest setting of user list lieutenant colonel's positive association degree score value of the multiple basic user to generate user list The user of quantity is determined as extending user.

Preferably, user characteristics is extracted according to the statistics of the network behavior of the user, according to the user characteristics Statistics extraction user interest-degree extraction.

Preferably, calculating corresponding interest-degree score value according to the interest-degree of user statistics, the user is not relative to Same interest-degree has different interest-degree score values.

Preferably, multiple basic users are obtained according to the fellow users that the client inputs, it is true according to the basic user All users are carried out interest-degree extraction according to the interest-degree and calculated corresponding to the interest-degree by fixed relevant interest-degree Interest-degree score value.

Preferably, according to the computation rule, all user's degree of being associated are calculated respectively, obtain the first of each user Beginning degree of association score value；The user is ranked up according to the initial association degree score value of each user.

Preferably, the interest-degree score value to be multiplied or be added with initial association degree score value, corrected correction is generated Degree of association score value.

Preferably, by the correction degree of association score value descending arrangement, the school of setting quantity is chosen according to described put in order The highest user of positive association degree score value is as extending user.

Preferably, after all users in the data network are sorted according to the correction degree of association score value, setting is chosen The highest user of correction degree of association score value of quantity is determined as extending user.

Fig. 8 shows a kind of method for being used to carry out user characteristics distributed coding provided in an embodiment of the present invention, institute The method of stating includes：

Step 801, the statistics associated with the network behavior of all users in data network is obtained, and to the system Count and carry out feature extraction to determine multiple user characteristicses and determine the total quantity of the multiple user characteristics.

In above-mentioned each embodiment, actually it is required to handle mass users feature, and in the mode handled, by In the limitation of hardware condition, the calculation amount of mass users characteristic is excessively huge, it is difficult to completed in specific computer, because And, it is necessary to after carrying out distributed coding to user characteristics, then carry out relevant calculating and processing.

In the user characteristic data of magnanimity, it is necessary first to determine the total quantity of user characteristics.The sum of this user characteristics Amount is change, is adjusted according to the demand of client, thus, for each user demand, it is required to redefine user The total quantity of feature.

In fact, after client accesses look-alike systems, mission requirements are submitted.System recalls need according to mission requirements The user characteristics and the processing quantity of user characteristics wanted.

The multiple user characteristics is subjected to classification rejecting, the number of the user characteristics of counting user demand according to user demand Amount, the total quantity as user characteristics.

Step 802, structure includes the tag file of the multiple user characteristics, and will based on division rule set in advance The tag file is divided into multiple subfiles.

For all user characteristicses, it is necessary first to build consumer profiles, include this in consumer profiles All user characteristic datas of mission requirements.Then, it is necessary to be drawn all user characteristicses according to division rule set in advance It is divided into multiple subfiles.User characteristics quantity in each subfile is roughly equal.Alternatively, the user in each subfile is special Sign quantity can be determined according to the load capacity of specific processing equipment.

In general, multiple user characteristicses are respectively divided into different subfiles, it is necessary to using clustering algorithm.Will be the multiple Division rule of the tag file of user characteristics based on hash function, is divided into more height corresponding to the processing number of nodes File.Each user characteristics is divided into any one bucket according to hash function.

One of which clustering algorithm is referred to following proposal：

All user characteristicses to be encoded are divided into N barrels；

Calculating the quantity Array [i] of user characteristics described in each bucket, the i is the numbering of bucket, i=0,1,2,3 ... N；

Accumulation and AccumulatedArray [i], the AccumulatedArray [i] are converted to the Array [i] =AccumulatedArray [i-1]+Array [i]；

Coding proceeded by from start_index [i]+1 to user characteristics in each bucket, the start_index [i]= AccumulatedArray[i-1]。

This method may also include：Analyze the user characteristics to be encoded and filter its apoplexy involving the solid organs sample data, after avoiding influence The accuracy of continuous disaggregated model.

Whole user characteristicses can be divided into N barrels according to user characteristics scale and calculate node regulation setting N, alternatively, can Each user characteristics is divided into any one bucket using Hash hash functions, treats that whole user characteristicses calculate affiliated bucket Afterwards, the user characteristics quantity of each bucket is calculated, this user characteristics quantity can be recorded with array, is denoted as Array [i], wherein, I is the numbering of bucket, and i=0,1,2,3 ... N, Array [0] represent the user characteristics quantity that the 0th bucket contains, Array [i-1] Represent the user characteristics quantity that the i-th -1 bucket contains.

The above-mentioned Array [i] calculated is converted into accumulation and AccumulatedArray [i], calculation formula is AccumulatedArray [i]=AccumulatedArray [i-1]+Array [j], wherein AccumulatedArray [0]= Array[0]；Coding, the start_index are proceeded by from start_index [i]+1 to user characteristics in each bucket again [i]=AccumulatedArray [i-1].

N number of calculate node can be called, each calculate node encodes the user characteristics in a bucket, specifically, first First handle i-th, if i=0, remember start_index [0]=0, otherwise remember start_index=AccumulatedArray [i-1]；Coding is proceeded by from start_index [i]+1 to bucket interior element again.By the above process, you can complete distributed User characteristics coding, and it can guarantee that the coding between each bucket does not have conflict.

Step 803, content scanning is carried out to the user characteristics in each subfile, to determine that user is special in each subfile The quantity of sign.

Each subfile includes a certain number of user characteristicses, in order to accurately encode, it is necessary to each subfile In user characteristics carry out content scanning, determine in each subfile determine user characteristics quantity.

Meanwhile between each subfile and have associated order, it is specific to need by encoding progress.The quantity of subfile It can be determined according to the quantity of the processing unit of actual treatment task.Also need to filter obvious abnormal in the user characteristics Dirty sample data.

Step 804, in the total quantity and each subfile of the space encoder based on user characteristics, the multiple user characteristics The quantum count of user characteristics determines the coding subspace of user characteristics subset in each subfile.

The maximum group/cording quantity that can allow for according to the coding method determines the space encoder of the user characteristics.According to The disposal ability of each processing node determines the coding subspace of each subfile.Thus, the volume of each subfile Numeral space is actually that the user characteristics subset of corresponding each subfile determines.

Step 805, according to processing rule set in advance, each subfile and corresponding coding subspace are sent to more In a processing node accordingly processing node with by it is corresponding processing node to the user characteristics in user characteristics subset into Row coding.

Processing rule set in advance, as combines the disposal ability of each processing node, select the subfile that adapts into Row processing.Saved when being encoded to user characteristics, it is necessary to which the corresponding subfile of corresponding character subset is sent corresponding processing Point carries out.

In the present embodiment, the consumer profiles that all user characteristicses are formed are divided into corresponding to the multiple of processing node Subfile, obtains the numbering of the user characteristics quantity and subfile in each subfile, subfile is sent to corresponding place respectively Reason node is handled.

Fig. 9 shows a kind of method for being used to carry out user characteristics distributed coding provided in an embodiment of the present invention, bag Include：

User characteristics unit 901, for obtaining the statistical number associated with the network behavior of all users in data network According to, and feature extraction is carried out to the statistics to determine multiple user characteristicses and determine the sum of the multiple user characteristics Amount；

Tag file construction unit 902, the tag file of the multiple user characteristics is included for building, and based on advance The tag file is divided into multiple subfiles by the division rule of setting；

Feature quantity confirmation unit 903, it is every to determine for carrying out content scanning to the user characteristics in each subfile The quantity of user characteristics in a subfile；

Encode subspace confirmation unit 904, for the space encoder based on user characteristics, the multiple user characteristics it is total The quantum count of user characteristics determines the coding subspace of user characteristics subset in each subfile in quantity and each subfile；

Node allocation unit 905 is handled, for according to processing rule set in advance, each subfile to be compiled with corresponding Numeral space is sent in multiple processing nodes handles node with sub to user characteristics by corresponding processing node accordingly The user characteristics of concentration is encoded.

Preferably, behavior is clicked on according to the search of all users in the data network, webpage behavior is browsed and/or passes through The behavior that third party's cooperation obtains, obtains the statistics that the network behavior of all users in the data network is associated.

Preferably, according to the host features of user, n-gram features, surf time section, online institute in the statistics Possession domain and/or commodity behavior is browsed, carry out feature extraction, to determine multiple user characteristicses.

Preferably, the multiple user characteristics is subjected to classification rejecting, the user of counting user demand according to user demand The quantity of feature, the total quantity as user characteristics.

Preferably, by division rule of the tag file of the multiple user characteristics based on hash function, correspondence is divided into In multiple subfiles of the processing number of nodes.

Preferably, all user characteristicses are divided into N barrels；

Preferably, each user characteristics is divided into any one bucket according to hash function.

Preferably, the quantity of the processing node is identical with the quantity of the subfile, each node that handles is to an institute The user characteristics stated in bucket is encoded.

Preferably, as the i=0, AccumulatedArray [0]=Array [0].

Preferably, as the i=0, start_index [0]=0.

Preferably, filter the obvious abnormal dirty sample data in the user characteristics.

Preferably, the maximum group/cording quantity that can allow for according to the coding method determines that the coding of the user characteristics is empty Between.

Preferably, the coding subspace of each subfile is determined according to the disposal ability of each processing node.

Further, the present embodiment provides a kind of mobile terminal, including or for performing as above any one embodiment institute The system stated.

In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail And technology, so as not to obscure the understanding of this description.

Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect, Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore, Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself Separate embodiments all as the present invention.

Those skilled in the art, which are appreciated that, to carry out adaptively the module in the equipment in embodiment Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and attached drawing) and so to appoint Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power Profit requires, summary and attached drawing) disclosed in each feature can be by providing identical, equivalent or similar purpose alternative features come generation Replace.

In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments In included some features rather than further feature, but the combination of the feature of different embodiments means in of the invention Within the scope of and form different embodiments.For example, embodiment claimed in detail in the claims is one of any Mode it can use in any combination.

The all parts embodiment of the present invention can be with hardware realization, or to be run on one or more processor Software module realize, or realized with combinations thereof.The present invention is also implemented as being used to perform side as described herein The some or all equipment or program of device (for example, computer program and computer program product) of method.It is such Realizing the program of the present invention can store on a computer-readable medium, or can have the shape of one or more signal Formula.Such signal can be downloaded from internet website and obtained, and either be provided or with any other shape on carrier signal Formula provides.

It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.Word "comprising" is not arranged Except there are element or step not listed in the claims.Word "a" or "an" before element does not exclude the presence of more A such element.The present invention can be by means of including the hardware of some different elements and by means of properly programmed calculating Machine is realized.In if the unit claim of equipment for drying is listed, several in these devices can be by same Hardware branch embodies.

The above is only the embodiment of the present invention, it is noted that for the ordinary skill people of this area Member for, without departing from the spirit of the invention, can make it is some improve, modification and deformation, these improve, modification, It is regarded as in the protection domain of the application with deformation.

Claims

1. a kind of method that extending user is determined according to the statistics degree of association, the described method includes：

The statistics associated with the network behavior of all users in data network is obtained, and the statistics is carried out special Sign extraction is with the user characteristics of definite all users；

The extended requests that fellow users extension is carried out to basic user are received, the extended requests are parsed to determine extension The setting quantity of user and the positive sample collection including multiple basic users；

The negative sample for determining to include multiple training users according to all users in the data network and the multiple basic user This collection, wherein the ratio of the basic user and the quantity of training user is less than or equal to predetermined threshold；

The user characteristics of the multiple training users concentrated to the negative sample carries out signature analysis, to determine to be used for each user The computation rule that degree of being associated calculates；

The degree of association score value of each user in all users is calculated based on the computation rule, according to the degree of association score value Descending order is ranked up all users to generate user list；And

The user of the highest setting quantity of degree of association score value is determined as in the user list that the multiple basic user will be eliminated Extending user.

2. the method as described in claim 1, the method further includes：The user list of the multiple basic user will not be removed The user of the middle highest setting quantity of degree of association score value is determined as extending user.

3. the method as described in claim 1, according to the statistics of the network behavior off-line data of all users of data network, extraction The user characteristics of all users.

4. method as claimed in claim 3, the network behavior of the user includes：Search click behavior, browse webpage behavior And/or the behavior obtained by third party's cooperation.

5. the method as described in claim 3 or 4, the user characteristics includes：The host features of user, n-gram features, on Region and/or commodity behavior is browsed belonging to net period, online.

6. a kind of system that extending user is determined according to the statistics degree of association, the system comprises：

User characteristics unit, for obtaining the statistics associated with the network behavior of all users in data network, and it is right The statistics carries out feature extraction to determine the user characteristics of all users；

Positive sample collection unit, carries out basic user for receiving the extended requests of fellow users extension, to the extended requests Parsed the setting quantity to determine extending user and the positive sample collection including multiple basic users；

Negative sample collection unit is more for determining to include according to all users in the data network and the multiple basic user The negative sample collection of a training user, wherein the ratio of the basic user and the quantity of training user is less than or equal to predetermined threshold Value；

Computation rule unit, the user characteristics of multiple training users for being concentrated to the negative sample carry out signature analysis, with Determine the computation rule for calculating each user's degree of being associated；

Calculation of relationship degree unit, for calculating the degree of association score value of each user in all users based on the computation rule, All users are ranked up to generate user list according to the descending order of the degree of association score value；And

Extending user unit, for by the highest setting of degree of association score value in the user list for eliminating the multiple basic user The user of quantity is determined as extending user.

7. system as claimed in claim 6, the system also includes：The user list of the multiple basic user will not be removed The user of the middle highest setting quantity of degree of association score value is determined as extending user.

8. system as claimed in claim 6, according to the statistics of the network behavior off-line data of all users of data network, extraction The user characteristics of all users.

9. system as claimed in claim 8, the network behavior of the user includes：Search click behavior, browse webpage behavior And/or the behavior obtained by third party's cooperation.

10. system as claimed in claim 8 or 9, the user characteristics includes：The host features of user, n-gram features, on Region and/or commodity behavior is browsed belonging to net period, online.