CN113674011A - Data processing method, device, computing equipment and medium for user behaviors - Google Patents

Data processing method, device, computing equipment and medium for user behaviors Download PDF

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CN113674011A
CN113674011A CN202010416741.3A CN202010416741A CN113674011A CN 113674011 A CN113674011 A CN 113674011A CN 202010416741 A CN202010416741 A CN 202010416741A CN 113674011 A CN113674011 A CN 113674011A
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李慧
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Beijing Jingdong Century Trading Co Ltd
Beijing Wodong Tianjun Information Technology Co Ltd
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Abstract

The present disclosure provides a data processing method for user behavior, including: acquiring user data of a user to be processed; and determining the behavior of the user to be processed based on the sample behavior data and the user data, wherein the sample behavior data is obtained based on the following modes: acquiring a plurality of historical behavior data of each historical user in a plurality of time periods; classifying the plurality of historical behavior data into at least one behavior category based on similarity of the plurality of historical behavior data to each other; and for each behavior category in the at least one behavior category, processing the historical behavior data to obtain sample behavior data, wherein the sample behavior data characterizes behavior characteristics of historical users in the behavior category. The present disclosure also provides a data processing apparatus for user behavior, a computing device, and a computer-readable storage medium.

Description

Data processing method, device, computing equipment and medium for user behaviors
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a data processing method for user behavior, a data processing apparatus for user behavior, a computing device, and a computer-readable storage medium.
Background
Current techniques typically determine user behavior manually, which may include, for example, user life activity behavior, user consumption behavior, user storage asset behavior, user preference change behavior, and so forth. Taking the consumption behavior of the user as an example, the related art generally determines the consumption behavior of the user according to the information of the consumption amount, the consumption frequency, whether to consume recently, and the like of the user, and simply determines the consumption behavior type of the user to be active, non-consumption, and the like.
In the process of implementing the present disclosure, the inventor finds that the related art judges the behavior of the user in a manual manner, and depending on the accuracy of rich experience and manual rules, it is difficult to scientifically and reasonably judge the behavior of the user and predict the future behavior of the user.
Disclosure of Invention
In view of the above, the present disclosure provides an optimized data processing method for user behavior, a data processing apparatus for user behavior, a computing device, and a computer-readable storage medium.
One aspect of the present disclosure provides a data processing method for user behavior, including: the method comprises the steps of obtaining user data of a user to be processed, and determining the behavior of the user to be processed based on sample behavior data and the user data. Wherein the sample behavior data is obtained based on: the method comprises the steps of obtaining a plurality of historical behavior data of each historical user in a plurality of time periods, dividing the plurality of historical behavior data into at least one behavior category based on the similarity among the plurality of historical behavior data, and processing the historical behavior data to obtain sample behavior data for each behavior category in the at least one behavior category, wherein the sample behavior data represents the behavior characteristics of the historical users in the behavior category.
According to an embodiment of the present disclosure, the determining the behavior of the user to be processed based on the sample behavior data and the user data includes: and determining one sample behavior data of at least one sample behavior data as target behavior data based on the user data, wherein the at least one sample behavior data is in one-to-one correspondence with the at least one behavior category, and determining the behavior of the user to be processed based on at least one of the target behavior data and the user data.
According to an embodiment of the present disclosure, the plurality of time periods include at least one first time period, and the user data includes historical behavior sub-data of the user to be processed in the at least one first time period. Each of the at least one sample behavior data includes sample behavior sub-data within the at least one first time period. Wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises: and determining one sample behavior data in the at least one sample behavior data as target behavior data based on the similarity between the historical behavior sub-data and the at least one sample behavior sub-data respectively.
According to an embodiment of the present disclosure, the plurality of time periods includes at least one second time period, which is subsequent to the at least one first time period. The target behavior data includes target behavior sub-data within the at least one second time period. Wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the behavior of the user to be processed in the at least one second time period based on the behavior characteristics represented by the historical behavior subdata and the target behavior subdata.
According to an embodiment of the present disclosure, the user data includes attribute data of the user to be processed. Wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises: training a classification model by using the at least one sample behavior data and the attribute data of each historical user in the plurality of historical users to obtain a trained classification model, and processing the attribute data of the user to be processed by using the trained classification model to obtain the target behavior data. Wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the target behavior data as the behavior data of the user to be processed.
According to the embodiment of the present disclosure, the plurality of historical behavior data includes N pieces of historical behavior data, where N is an integer greater than or equal to 2. Wherein the classifying the plurality of historical behavior data into at least one behavior category based on the similarity between the plurality of historical behavior data comprises: determining K historical behavior data in the N pieces of historical behavior data as clustering centers, wherein K is an integer greater than or equal to 2 and smaller than N, calculating the similarity between each piece of historical behavior data in the N pieces of historical behavior data and the K clustering centers, and clustering the N pieces of historical behavior data based on the similarity to obtain K behavior categories.
Another aspect of the present disclosure provides a data processing apparatus for user behavior, including: the device comprises a first acquisition module, a determination module, a second acquisition module, a classification module and a processing module. The first acquisition module acquires user data of a user to be processed. And the determining module is used for determining the behavior of the user to be processed based on the sample behavior data and the user data. Wherein the sample behavior data is obtained based on: the second acquisition module acquires a plurality of historical behavior data of each historical user in a plurality of time periods. The classification module classifies the historical behavior data into at least one behavior category based on the similarity among the historical behavior data. And the processing module is used for processing the historical behavior data to obtain sample behavior data aiming at each behavior category in the at least one behavior category, wherein the sample behavior data represents the behavior characteristics of the historical user in the behavior category.
According to an embodiment of the present disclosure, the determining the behavior of the user to be processed based on the sample behavior data and the user data includes: and determining one sample behavior data of at least one sample behavior data as target behavior data based on the user data, wherein the at least one sample behavior data is in one-to-one correspondence with the at least one behavior category, and determining the behavior of the user to be processed based on at least one of the target behavior data and the user data.
According to an embodiment of the present disclosure, the plurality of time periods include at least one first time period, and the user data includes historical behavior sub-data of the user to be processed in the at least one first time period. Each of the at least one sample behavior data includes sample behavior sub-data within the at least one first time period. Wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises: and determining one sample behavior data in the at least one sample behavior data as target behavior data based on the similarity between the historical behavior sub-data and the at least one sample behavior sub-data respectively.
According to an embodiment of the present disclosure, the plurality of time periods includes at least one second time period, which is subsequent to the at least one first time period. The target behavior data includes target behavior sub-data within the at least one second time period. Wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the behavior of the user to be processed in the at least one second time period based on the behavior characteristics represented by the historical behavior subdata and the target behavior subdata.
According to an embodiment of the present disclosure, the user data includes attribute data of the user to be processed. Wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises: training a classification model by using the at least one sample behavior data and the attribute data of each historical user in the plurality of historical users to obtain a trained classification model, and processing the attribute data of the user to be processed by using the trained classification model to obtain the target behavior data. Wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the target behavior data as the behavior data of the user to be processed.
According to the embodiment of the present disclosure, the plurality of historical behavior data includes N pieces of historical behavior data, where N is an integer greater than or equal to 2. Wherein the classifying the plurality of historical behavior data into at least one behavior category based on the similarity between the plurality of historical behavior data comprises: determining K historical behavior data in the N pieces of historical behavior data as clustering centers, wherein K is an integer greater than or equal to 2 and smaller than N, calculating the similarity between each piece of historical behavior data in the N pieces of historical behavior data and the K clustering centers, and clustering the N pieces of historical behavior data based on the similarity to obtain K behavior categories.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, the data processing method or device for the user behavior can at least partially solve the problem that it is difficult to scientifically and reasonably judge the user behavior and predict the future behavior of the user depending on the accuracy of the rich experience manual rule in the related art by judging the user behavior in a manual manner, and therefore, the technical effects of accurately judging the user behavior and predicting the future behavior of the user can be realized.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically shows a system architecture of a data processing method and apparatus for user behavior according to an embodiment of the present disclosure;
2A-2B schematically illustrate a flow diagram of a data processing method for user behavior according to an embodiment of the present disclosure;
FIG. 3 schematically shows a schematic diagram of behavior categories according to an embodiment of the disclosure;
FIG. 4 schematically illustrates a schematic diagram of obtaining sample behavior data according to an embodiment of the disclosure;
FIG. 5 schematically shows a schematic diagram of predictive behavior according to an embodiment of the disclosure;
FIG. 6 schematically shows a schematic diagram of predicted behavior according to another embodiment of the present disclosure;
7A-7B schematically illustrate block diagrams of data processing apparatus for user behavior according to embodiments of the present disclosure; and
FIG. 8 schematically shows a block diagram of a computer system suitable for data processing for user behavior according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
An embodiment of the present disclosure provides a data processing method for a user behavior, including: and acquiring user data of the user to be processed, and determining the behavior of the user to be processed based on the sample behavior data and the user data. Wherein the sample behavior data is obtained based on: the method comprises the steps of obtaining a plurality of historical behavior data of each historical user in a plurality of time periods, dividing the plurality of historical behavior data into at least one behavior category based on the similarity among the plurality of historical behavior data, and processing the historical behavior data to obtain sample behavior data for each behavior category in the at least one behavior category, wherein the sample behavior data represents the behavior characteristics of the historical users in the behavior category.
Fig. 1 schematically shows a system architecture of a data processing method and apparatus for user behavior according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the data processing method for the user behavior provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the data processing apparatus for user behavior provided by the embodiments of the present disclosure may be generally disposed in the server 105. The data processing method for the user behavior provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the data processing apparatus for user behavior provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
For example, the user data of the user to be processed according to the embodiment of the present disclosure may be stored in the terminal devices 101, 102, and 103, and the user data of the user to be processed is sent to the server 105 through the terminal devices 101, 102, and 103, and the server 105 may determine the behavior of the user to be processed based on the sample behavior data and the user data, or the terminal devices 101, 102, and 103 may also determine the behavior of the user to be processed based on the sample behavior data and the user data directly. In addition, the user data of the user to be processed can also be directly stored in the server 105, and the server 105 determines the behavior of the user to be processed directly based on the sample behavior data and the user data.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2A to 2B schematically show a flowchart of a data processing method for user behavior according to an embodiment of the present disclosure.
As shown in fig. 2A, the method may include, for example, the following operations S210 to S220.
In operation S210, user data of a user to be processed is acquired.
According to an embodiment of the present disclosure, the user data may include, for example, historical behavior data or attribute data of the user to be processed. The attribute data may include, for example, the age, sex, location, etc. of the user to be processed.
Next, in operation S220, based on the sample behavior data and the user data, the behavior of the user to be processed is determined.
According to embodiments of the present disclosure, for example, future behavior of a pending user may be predicted based on user data using sample behavior data. The sample behavior data is obtained from historical behavior data of a plurality of historical users, for example, the sample behavior data has generality, and therefore future behaviors of the users to be processed can be predicted through the sample behavior data.
As shown in fig. 2B, sample behavior data of an embodiment of the present disclosure may be obtained, for example, based on the following operations S230 to S250.
In operation S230, a plurality of historical behavior data of each of a plurality of historical users over a plurality of time periods is acquired.
According to an embodiment of the present disclosure, each of a plurality of historical users, for example, has one historical behavior data. Each of the historical behavior data includes, for example, a plurality of time periods and index data corresponding one-to-one to the plurality of time periods. The plurality of time periods may be, for example, a time granularity of months, e.g., the plurality of time periods may include month 1, month 2, month 3, etc. It is to be appreciated that the various time periods of the disclosed embodiments may also be time granular in years, days, hours, etc.
According to embodiments of the present disclosure, historical behavioral data may include, for example, user vital activity behavioral data, user consumption behavioral data, user stored asset behavioral data, user preference change behavioral data, and/or the like.
For example, when the historical behavior data is user life activity behavior data, the plurality of time periods include, for example, month 1, month 2, month 3, and so on from the birth of the user. The index data corresponding to the plurality of time periods includes, for example, a user's activity time a1Hour, b1Hour, c1Hours, and the like.
For example, when the historical behavior data is user consumption behavior data, the plurality of time periods include, for example, month 1, month 2, month 3, and so on from user registration. The index data corresponding to the plurality of time periods includes, for example, a user's consumption amount2Yuan, b2Element, c2Meta, and so on.
For example, when historical behavior data stores asset behavior data for a user, the plurality of time periods include, for example, month 1, month 2, month 3, etc. since the user opened an account. The index data corresponding to the plurality of time periods one by one includes, for example, storing the asset amount of a3Yuan, b3Element, c3Meta, and so on. It is to be understood that the historical behavior data includes, for example, a plurality of types of data, which are not illustrated herein.
Operation S240 will be described below in conjunction with fig. 3 and operation S250 will be described below in conjunction with fig. 4.
FIG. 3 schematically shows a schematic diagram of behavior classes according to an embodiment of the disclosure.
In operation S240, the plurality of historical behavior data is classified into at least one behavior category based on similarity between the plurality of historical behavior data.
As shown in fig. 3, for convenience of understanding, the embodiment of the present disclosure takes historical behavior data as user consumption behavior data as an example. Each of the plurality of historical behavior data is represented by a dotted line in a coordinate system of fig. 3, wherein an abscissa x of the coordinate system represents a time period, for example, and an ordinate y represents a consumption amount, for example. Each dashed line may represent, for example, historical behavior data for a historical user.
Then, the plurality of historical behavior data may be classified into at least one behavior category by calculating similarities between the plurality of historical behavior data and each other. The 3 behavior classes are schematically shown in fig. 3. The similarity of the behavior of the users within each behavior category is high. In one embodiment, the plurality of historical behavior data may be clustered, such as by a clustering algorithm, to separate the plurality of historical behavior data into a plurality of behavior categories.
For example, the plurality of historical behavior data may include N pieces of historical behavior data, N being an integer equal to or greater than 2. The processing of the plurality of historical behavior data by the clustering algorithm includes, for example: firstly, K historical behavior data in the N historical behavior data are determined to be used as a clustering center, and K is an integer which is greater than or equal to 2 and smaller than N. Then, calculating the similarity between each historical behavior data in the N historical behavior data and the K clustering centers, and clustering the N historical behavior data based on the similarity to obtain K behavior categories.
For example, for one of the N pieces of historical behavior data, the similarity between the historical behavior data and the K clustering centers is calculated, and the historical behavior data is classified into the behavior category to which the clustering center most similar to the historical behavior data belongs based on the similarity. The same or similar processing may be performed for each historical behavior data, for example.
During the clustering process, the similarity of each historical behavior data to each cluster center may be represented by cosine similarity, for example. For example, each historical behavior data or each cluster center may be represented by coordinate values in a coordinate system. For example, a historical behavioral data may be tabulatedIs shown as p ═ p1 p2 p3……pn]Wherein p is1、p2、p3、……、pnFor example, the amounts of consumption in one-to-one correspondence with a plurality of time periods, respectively. A cluster center may be represented, for example, as q ═ q1 q2q3……qn]Wherein q is1、q2、q3、……、qnFor example, the amounts of consumption in one-to-one correspondence with a plurality of time periods, respectively. The cosine similarity cos (θ) between a history behavior data and a cluster center can be expressed as:
Figure BDA0002493619560000101
after K behavior classes are obtained through clustering, the difference between the historical behavior data included in each behavior class can be calculated, and the difference between each class in the K behavior classes can be calculated. If the difference between the historical behavior data included in each behavior class is small, and the difference between each of the K behavior classes is large, the K behavior classes can be used as the final clustering result.
In another embodiment, if there are two or more categories with small differences among the K behavior categories, the two or more categories with small differences may be merged into one category, and the final clustering result may be obtained after merging.
According to the embodiments of the present disclosure, the clustering algorithm may include, but is not limited to, a time series clustering method, a curve similarity clustering algorithm, and a curve distance clustering algorithm, for example.
Fig. 4 schematically illustrates a schematic diagram of obtaining sample behavior data according to an embodiment of the disclosure.
In operation S250, for each behavior class of the at least one behavior class, the historical behavior data may be processed to obtain sample behavior data, where the sample behavior data characterizes, for example, behavior characteristics of historical users in the behavior class.
As shown in fig. 4, taking an example that at least one behavior class includes 3 behavior classes, the embodiments of the present disclosure may process each behavior class separately to obtain sample behavior data of the behavior class, where the sample behavior data may, for example, characterize commonality of each historical behavior data in the behavior class.
For each behavior category, a certain historical behavior data in the behavior category can be randomly extracted as a sample behavior data of the behavior category. Or weighting the plurality of historical behavior data in the behavior category to obtain a weighted result, and taking the weighted result as the sample behavior data of the behavior category. Or denoising and weighting can be performed on a plurality of historical behavior data in the behavior category to obtain results after denoising and weighting, and the results after denoising and weighting are used as sample behavior data of the behavior category. For example, taking the weighting processing manner as an example, in an embodiment, the weights of the plurality of historical behavior data in the behavior category are equal, and at this time, an average value of the plurality of historical behavior data in the behavior category may be calculated as the sample behavior data of the behavior category.
As shown in fig. 4, for example, the at least one behavior class includes 3 behavior classes, and the sample behavior data of the 3 behavior classes are, for example, sample behavior data a, sample behavior data B, and sample behavior data C, respectively. The sample behavior data a, the sample behavior data B, and the sample behavior data C in fig. 4 are represented by bold lines, for example.
According to the embodiment of the disclosure, for example, normalization processing may be performed on each historical behavior data, so that the historical behavior data after normalization processing is not affected by the data dimension. Then, the above-described operations S240 to S250 may be performed based on a plurality of historical behavior data obtained after the normalization process. Any normalization processing method can be applied to the embodiments of the present disclosure, for example. For convenience of understanding, the embodiment of the present disclosure takes a normalization processing method as an example. For example, one historical behavior data is denoted as p ═ p1 p2 p3……pn]The historical behavior data p is normalized, and the normalized historical behavior data is expressed as p' ═ p1’ p2’ p3’……pn’]. Wherein p is1’＝(p1-pmin)/(pmax-pmin)、p2’＝(p2-pmin)/(pmax-pmin)、p3’＝(p3-pmin)/(pmax-pmin)、……、pn’＝(pn-pmin)/(pmax-pmin). Wherein p isminFor example, is p1、p2、p3、……、pnMinimum value of (1), pmaxFor example, is p1、p2、p3、……、pnMaximum value of (2).
According to the technical scheme of the embodiment of the disclosure, historical behavior data are classified based on similarity between the historical behavior data of historical users to obtain a plurality of behavior categories. Sample behavior data that characterizes the common points of the various historical behavior data in each behavior category is then determined, and the behavior of the user to be processed can then be predicted based on the sample behavior data. By the technical scheme of the embodiment of the disclosure, not only can the behavior characteristics of the historical user be accurately determined, but also the future behavior of the user to be processed can be predicted by utilizing the sample behavior data. It can be understood that the technical scheme of the embodiment of the disclosure clusters historical behavior data to obtain a plurality of behavior categories, predicts future behaviors of the user to be processed through sample behavior data in each behavior category, does not need excessive manual intervention, and reduces dependency on experience and manual rules.
According to an embodiment of the present disclosure, the determining the behavior of the user to be processed based on the sample behavior data and the user data in operation S220 may include, for example: and determining one sample behavior data of the at least one sample behavior data as a target behavior data based on the user data, and determining the behavior of the user to be processed based on at least one of the target behavior data and the user data. Wherein the at least one sample behavior data corresponds one-to-one to the at least one behavior category.
According to the embodiment of the disclosure, for example, one of the at least one sample behavior data may be determined as target behavior data according to the user data of the user to be processed, and the target behavior data may be used to predict future behavior of the user to be processed.
According to an embodiment of the present disclosure, the user data of the user to be processed may include two types of data, for example.
For example, one type may be historical behavior data of the user to be processed. For example, when the historical behavior data is the user consumption behavior data, if the to-be-processed user is a user registered 3 months ago, the historical behavior data of the to-be-processed user includes, for example, the consumption amount of the to-be-processed user in the past 1 st month, 2 nd month, and 3 rd month. When the user data of the user to be processed is the historical behavior data, the process of predicting the behavior of the user to be processed is as described in fig. 5.
For example, the other type may be attribute data of the user to be processed, and the attribute data may include, for example, the age, sex, region of the user to be processed, and the like. When the user data of the user to be processed is the attribute data, the process of predicting the behavior of the user to be processed is as described in fig. 6.
Fig. 5 schematically shows a schematic diagram of a predicted behavior according to an embodiment of the present disclosure.
According to an embodiment of the present disclosure, the plurality of time periods may include, for example, at least one first time period. For example, taking the historical behavior data as the user consumption behavior data as an example, the plurality of time periods include, for example, month 1, month 2, month 3, month … …, and month n after the user registration. The user data of the user to be processed includes, for example, historical behavior sub-data of the user to be processed in at least one first time period, and the at least one first time period may be, for example, the 1 st month, the 2 nd month, and the 3 rd month after the user to be processed registers. Wherein the consumption amount of the user to be processed in the at least one first time period is, for example, past data.
According to an embodiment of the present disclosure, the plurality of time periods may further include, for example, at least one second time period, for example, after the at least one first time period. For example, the at least one second time period may include month 4, month 5, month … …, and month n after the user registration, and the consumption amount of the user to be processed in the at least one second time period is, for example, future consumption data of the user to be processed.
As shown in fig. 5, each of the at least one sample behavior data includes, for example, sample behavior sub-data in at least one first time period and sample behavior sub-data in at least one second time period. For example, the at least one sample behavior data may include sample behavior data a, sample behavior data B, and sample behavior data C. The sample behavior data A comprises, for example, sample behavior sub-data A over at least one first time period1Including the sample behavior sub-data A in at least one second time period2. The sample behavior data B comprises, for example, sample behavior sub-data B for at least one first time period1Including the sample behavior sub-data B in at least one second time period2. The sample behavior data C comprises, for example, sample behavior sub-data C for at least one first time period1Including the sample behavior sub-data C for at least one second time period2。
As shown in fig. 5, the user data of the user to be processed is, for example, historical behavior sub-data D of the user to be processed in at least one first time period1. Wherein, based on the user data, determining that one of the at least one sample behavior data is the target behavior data includes, for example: subdata D based on historical behavior1Respectively associated with at least one sample line within a first time period1、B1And C1) And determining one sample behavior data in the at least one sample behavior data as the target behavior data.
For example, the historical behavior sub-data D is calculated1Respectively associated with the sample behavior subdata A1Sample behavior subdataB1And sample behavior subdata C1The similarity between them. Wherein the similarity may be characterized, for example, in terms of the distance between the lines in fig. 5. Then, the historical behavior sub-data C with the greatest similarity (e.g., the smallest distance) may be used1And taking the corresponding sample behavior data C as target behavior data. The target behavior data includes, for example, sample behavior subdata C for at least one second time period2。
The sub-data D may then be based on the historical behavior sub-data D1And sample behavior subdata C2And determining the behavior of the user to be processed in at least one second time period according to the characterized behavior characteristics. For example, the behavior of the user to be processed in at least one second time period is represented by data D shown in FIG. 52And (4) showing. Wherein, the data D2Such as predicted future behavior data of the user to be processed. For example, the child data D may be acted upon in history1As data D2And the sub-data C is selected as the sample row2Based on the turning points and the trends, data D are obtained2。
It is understood that when the to-be-processed user is a user whose behavior data is not complete, the future behavior of the to-be-processed user can be predicted based on the partial historical behavior data of the to-be-processed user and the related sample behavior data.
Fig. 6 schematically shows a schematic diagram of a predicted behavior according to another embodiment of the present disclosure.
As shown in fig. 6, at least one sample behavior data of an embodiment of the present disclosure may be, for example, the same as or similar to the at least one sample behavior data shown in fig. 5. The user data of the embodiment of the present disclosure may include, for example, attribute data of the user to be processed, and the attribute data may include, for example, an age, a sex, a location, and the like of the user to be processed. Wherein determining one of the at least one sample behavior data as the target behavior data based on the user data, for example, comprises processing the attribute data of the user with a trained classification model to obtain the target behavior data of the at least one sample behavior data.
According to an embodiment of the present disclosure, a classification model may be trained, for example, using at least one sample behavior data and attribute data of each of a plurality of historical users to arrive at a trained classification model. For example, the plurality of historical users includes, for example, user 1, user 2, user 3, and so on. The historical behavior data of the user 1 belongs to the behavior category in which the sample behavior data a is located, for example, and a label about the sample behavior data a may be added to the attribute data of the user 1. Similarly, corresponding labels may be added to the attribute data of the user 2, the user 3, and the like, and the attribute data with the label information is used as a training sample to train the classification model, so as to obtain a trained classification model.
The trained classification model may then be utilized to process attribute data of the user to be processed to obtain target behavior data. For example, the attribute data of the user to be processed is input into the trained classification model, the output result of the trained classification model may include, for example, the label information of one sample behavior data, for example, the output result is the label information of the sample behavior data C, and the output result may represent that the attribute data of the user to be processed is similar to the attribute data of each historical user in the behavior category in which the sample behavior data C is located. Therefore, the sample behavior data C may be determined as target behavior data, which is behavior data of the user to be processed. That is, when the user to be processed is a new user, the user to be processed does not have relevant historical behavior data, for example, and the future behavior of the user to be processed can be predicted based on the attribute data of the user to be processed.
According to an embodiment of the present disclosure, the classification model may include, but is not limited to, a logistic regression model, a random forest model, a Gradient Boosting Decision Tree (GBDT) model, and an eXtreme Gradient Boosting (Xgboost) model, for example.
Fig. 7A to 7B schematically show block diagrams of a data processing apparatus for user behavior according to an embodiment of the present disclosure.
As shown in fig. 7A, the data processing apparatus 700 for user behavior may include, for example, a first obtaining module 710 and a determining module 720.
The first obtaining module 710 may be configured to obtain user data of a user to be processed. According to the embodiment of the present disclosure, the first obtaining module 710 may, for example, perform the operation S210 described above with reference to fig. 2A, which is not described herein again.
The determination module 720 may be configured to determine the behavior of the user to be processed based on the sample behavior data and the user data. According to an embodiment of the present disclosure, the determining module 720 may perform, for example, operation S220 described above with reference to fig. 2A, which is not described herein again.
As shown in fig. 7B, the data processing apparatus 700 includes, for example, a second obtaining module 730, a classifying module 740, and a processing module 750, in addition to the first obtaining module 710 and the determining module 720 described above. Wherein the sample behavior data is obtained based on the second obtaining module 730, the classifying module 740, and the processing module 750.
The second obtaining module 730 may be configured to obtain a plurality of historical behavior data of each of a plurality of historical users in a plurality of time periods. According to an embodiment of the present disclosure, the second obtaining module 730 may, for example, perform the operation S230 described above with reference to fig. 2B, which is not described herein again.
The classification module 740 may be configured to classify the plurality of historical behavior data into at least one behavior category based on similarity of the plurality of historical behavior data to each other. According to the embodiment of the present disclosure, the classification module 740 may perform, for example, the operation S240 described above with reference to fig. 2B, which is not described herein again.
The processing module 750 may be configured to process the historical behavior data for each of the at least one behavior class to obtain sample behavior data, where the sample behavior data characterizes behavior characteristics of historical users in the behavior class. According to the embodiment of the present disclosure, the processing module 750 may perform, for example, the operation S250 described above with reference to fig. 2B, which is not described herein again.
According to an embodiment of the present disclosure, determining the behavior of the user to be processed based on the sample behavior data and the user data includes: and determining one sample behavior data in the at least one sample behavior data as target behavior data based on the user data, wherein the at least one sample behavior data corresponds to at least one behavior category in a one-to-one manner, and determining the behavior of the user to be processed based on at least one of the target behavior data and the user data.
According to the embodiment of the disclosure, the plurality of time periods comprise at least one first time period, and the user data comprises historical behavior subdata of the user to be processed in the at least one first time period. Each of the at least one sample behavior data includes sample behavior sub-data for at least one first time period. Wherein determining, based on the user data, one of the at least one sample behavior data as the target behavior data comprises: and determining one sample behavior data in the at least one sample behavior data as the target behavior data based on the similarity between the historical behavior sub-data and the at least one sample behavior sub-data respectively.
According to an embodiment of the present disclosure, the plurality of time periods includes at least one second time period, the at least one second time period being subsequent to the at least one first time period. The target behavior data includes target behavior sub-data for at least one second time period. Wherein determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the behavior of the user to be processed in at least one second time period based on the behavior characteristics represented by the historical behavior subdata and the target behavior subdata.
According to an embodiment of the present disclosure, the user data includes attribute data of the user to be processed. Wherein determining, based on the user data, one of the at least one sample behavior data as the target behavior data comprises: training a classification model by using at least one sample behavior data and attribute data of each historical user in a plurality of historical users to obtain a trained classification model, and processing the attribute data of the user to be processed by using the trained classification model to obtain target behavior data. Wherein determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the target behavior data as the behavior data of the user to be processed.
According to the embodiment of the disclosure, the plurality of historical behavior data includes N pieces of historical behavior data, where N is an integer greater than or equal to 2. Wherein classifying the plurality of historical behavior data into at least one behavior category based on the similarity between the plurality of historical behavior data comprises: determining K historical behavior data in the N historical behavior data as clustering centers, wherein K is an integer greater than or equal to 2 and smaller than N, calculating the similarity between each historical behavior data in the N historical behavior data and the K clustering centers, and clustering the N historical behavior data based on the similarity to obtain K behavior categories.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the first obtaining module 710, the determining module 720, the second obtaining module 730, the classifying module 740, and the processing module 750 may be combined into one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 710, the determining module 720, the second obtaining module 730, the classifying module 740, and the processing module 750 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by any other reasonable manner of integrating or packaging a circuit, etc., by hardware or the same, or by any one of three implementations of software, hardware, and firmware, or by a suitable combination of any several of them. Alternatively, at least one of the first obtaining module 710, the determining module 720, the second obtaining module 730, the classifying module 740, and the processing module 750 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.
FIG. 8 schematically shows a block diagram of a computer system suitable for data processing for user behavior according to an embodiment of the present disclosure. The computer system illustrated in FIG. 8 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 8, a computer system 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 806 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 803, various programs and data necessary for the operation of the system 800 are stored. The processor 801, the ROM802, and the RAM 803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
System 800 may also include an input/output (I/O) interface 805, also connected to bus 804, according to an embodiment of the disclosure. The system 800 may also include one or more of the following components connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a computer-non-volatile computer-readable storage medium, which may include, for example and without limitation: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM802 and/or RAM 803 described above and/or one or more memories other than the ROM802 and RAM 803.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. A data processing method for user behavior, comprising:
acquiring user data of a user to be processed; and
determining a behavior of the user to be processed based on sample behavior data and the user data,
wherein the sample behavior data is obtained based on:
acquiring a plurality of historical behavior data of each historical user in a plurality of time periods;
classifying the plurality of historical behavior data into at least one behavior category based on similarity of the plurality of historical behavior data to each other; and
for each behavior category of the at least one behavior category, processing the historical behavior data to obtain sample behavior data, wherein the sample behavior data characterizes behavior characteristics of historical users in the behavior category.
2. The method of claim 1, wherein the determining the behavior of the user to be processed based on the sample behavior data and the user data comprises:
determining one sample behavior data of at least one sample behavior data as target behavior data based on the user data, wherein the at least one sample behavior data is in one-to-one correspondence with the at least one behavior category; and
determining the behavior of the user to be processed based on at least one of the target behavior data and the user data.
3. The method of claim 2, wherein:
the plurality of time periods comprise at least one first time period, and the user data comprises historical behavior subdata of the user to be processed in the at least one first time period;
each of the at least one sample behavior data comprises sample behavior sub-data within the at least one first time period;
wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises: and determining one sample behavior data in the at least one sample behavior data as target behavior data based on the similarity between the historical behavior sub-data and the at least one sample behavior sub-data respectively.
4. The method of claim 3, wherein:
the plurality of time periods includes at least one second time period, the at least one second time period being subsequent to the at least one first time period;
the target behavior data comprises target behavior sub-data within the at least one second time period;
wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the behavior of the user to be processed in the at least one second time period based on the behavior characteristics represented by the historical behavior subdata and the target behavior subdata.
5. The method of claim 2, wherein the user data comprises attribute data of the user to be processed;
wherein the determining, based on the user data, that one of the at least one sample behavior data is a target behavior data comprises:
training a classification model by using the at least one sample behavior data and attribute data of each historical user of the plurality of historical users to obtain a trained classification model; and
processing attribute data of the user to be processed by using the trained classification model to obtain the target behavior data;
wherein the determining the behavior of the user to be processed based on at least one of the target behavior data and the user data comprises: and determining the target behavior data as the behavior data of the user to be processed.
6. The method of claim 1, wherein the plurality of historical behavior data comprises N historical behavior data, N being an integer greater than or equal to 2;
wherein the classifying the plurality of historical behavior data into at least one behavior category based on the similarity between the plurality of historical behavior data comprises:
determining K historical behavior data in the N pieces of historical behavior data as a clustering center, wherein K is an integer which is greater than or equal to 2 and smaller than N;
calculating the similarity between each historical behavior data in the N pieces of historical behavior data and K pieces of clustering centers; and
and clustering the N pieces of historical behavior data based on the similarity to obtain K behavior categories.
7. A data processing apparatus for user behavior, comprising:
the first acquisition module is used for acquiring user data of a user to be processed; and
a determination module that determines a behavior of the user to be processed based on sample behavior data and the user data,
wherein the sample behavior data is obtained based on:
the second acquisition module is used for acquiring a plurality of historical behavior data of each historical user in a plurality of time periods;
a classification module that classifies the plurality of historical behavior data into at least one behavior category based on a similarity between the plurality of historical behavior data; and
and the processing module is used for processing the historical behavior data to obtain sample behavior data aiming at each behavior category in the at least one behavior category, wherein the sample behavior data represents the behavior characteristics of the historical user in the behavior category.
8. The apparatus of claim 7, wherein the determining the behavior of the pending user based on the sample behavior data and the user data comprises:
determining one sample behavior data of at least one sample behavior data as target behavior data based on the user data, wherein the at least one sample behavior data is in one-to-one correspondence with the at least one behavior category; and
determining the behavior of the user to be processed based on at least one of the target behavior data and the user data.
9. A computing device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
10. A computer-readable storage medium storing computer-executable instructions for implementing the method of any one of claims 1 to 6 when executed.
CN202010416741.3A 2020-05-15 2020-05-15 Data processing method, device, computing equipment and medium for user behaviors Withdrawn CN113674011A (en)

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