CN117312398A

CN117312398A - Data processing method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN117312398A
Application number: CN202311345655.8A
Authority: CN
Inventors: 郭松
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2023-10-17
Filing date: 2023-10-17
Publication date: 2023-12-29

Abstract

The disclosure provides a data processing method and device, electronic equipment and a computer readable storage medium, which can be applied to the technical fields of big data, cloud computing and financial science and technology. The data processing method comprises the following steps: acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intended users who have accessed a target page, and the target page is used for displaying the target product; inquiring from a database based on user identifications of a plurality of intention users to obtain respective user labels of the intention users; and carrying out statistical association processing on a plurality of user tags of the plurality of intention users so as to determine a target tag from the plurality of user tags.

Description

Data processing method and device, electronic equipment and computer readable storage medium

Technical Field

The present disclosure relates to the technical field of big data, the technical field of cloud computing, and the technical field of financial science, and more particularly, to a data processing method and apparatus, an electronic device, and a computer readable storage medium.

Background

In the product promotion process, product promotion is generally performed based on user tags. In the process of implementing the disclosed concept, the inventor finds that at least the following problems exist in the related art: in the current product popularization process, user labels are formed by generating user figures for users, the method is mainly used for generating the user labels based on an algorithm, certain deviation exists between the user labels and actual purchasing trends of the users, the product popularization effect based on the user labels is not ideal, a large amount of calculation power is required to be consumed for generating the user labels, and the computer processing pressure and the labor consumption are large.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a data processing method and apparatus, an electronic device, and a computer-readable storage medium.

In one aspect of the present disclosure, there is provided a data processing method including: acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intended users who have accessed a target page, and the target page is used for displaying the target product; inquiring from a database based on user identifications of a plurality of intention users to obtain respective user labels of the intention users; and carrying out statistical association processing on a plurality of user tags of the plurality of intention users so as to determine a target tag from the plurality of user tags.

According to an embodiment of the present disclosure, the data processing method further includes determining a target user including a target tag from among a plurality of intended users; and generating a product promotion label for the target user.

According to an embodiment of the present disclosure, the history data includes N sets of history data, N sets of history data are related to N historical popularization processes of the target product, N is greater than or equal to 2, and performing statistical association processing on a plurality of user tags of a plurality of intended users includes: grouping a plurality of user tags of a plurality of intention users to generate N groups of group user tags corresponding to N times of historical popularization processes; respectively carrying out statistical sorting treatment on N groups of group user labels based on the user number dimension to generate N groups of primary selection labels; the target tag is determined from the N groups of primary tags.

According to an embodiment of the present disclosure, determining a target tag from the N sets of primary tags includes: calculating the intersection of N groups of primary selection labels; and determining the intersection of the N groups of primary selected tags as a target tag under the condition that the number of user tags contained in the intersection of the N groups of primary selected tags meets the preset number condition.

According to an embodiment of the present disclosure, performing statistical ranking processing on N groups of group user tags based on a user number dimension, respectively, generating N groups of primary selection tags includes: based on the dimension of the number of users, respectively counting the number of the intended users corresponding to each user tag in each group of group user tags; according to the number of the intended users corresponding to each user tag in each group of group user tags, sorting the group user tags to obtain N groups of tag sorting results; and generating N groups of primary labels according to the N groups of label sorting results.

According to an embodiment of the present disclosure, performing statistical association processing on a plurality of user tags of a plurality of intended users includes: based on the dimension of the number of users, counting the number of the intended users corresponding to each of the plurality of user tags; according to the number of the intended users corresponding to each of the plurality of user tags, sorting the plurality of user tags to obtain a tag sorting result; and determining the target label from the plurality of user labels according to the label sorting result.

According to an embodiment of the present disclosure, the user tag includes at least one of: user basic attribute labels, user resource holding condition labels, user historical transaction condition labels and user historical browsing behavior labels.

Another aspect of the present disclosure provides a data processing apparatus comprising: the system comprises an acquisition module, a query module and a statistics module.

According to the embodiment of the disclosure, the acquiring module is used for acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intention users who access a target page, and the target page is used for displaying the target product; the query module is used for querying from the database based on the user identifications of the plurality of intention users to obtain the user labels of the plurality of intention users; the statistics module is used for carrying out statistics association processing on a plurality of user tags of a plurality of intended users so as to determine a target tag from the plurality of user tags.

Another aspect of the present disclosure provides an electronic device, comprising: one or more processors; and 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 perform the data processing method described above.

Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described data processing method.

Another aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described data processing method.

According to the data processing method and device, the electronic equipment and the computer readable storage medium, the target label most suitable for the target product can be obtained by analyzing the repeated historical popularization process of the target product and carrying out statistical association processing on the user label of the intended user. Different from the method for constructing user portraits for users in the conventional technology, the method in the embodiment of the disclosure is based on actual product popularization data, the existing labels of the users are related through the actual popularization data, statistical association processing is carried out on the existing labels, the most suitable user labels are determined by back-pushing, the obtained user labels are most suitable for actual attributes and actual purchasing trends of the users because the actual popularization data are obtained by the user labels, the product popularization accuracy is higher based on the labels, the historical data are automatically analyzed through a system, a large number of analysis processes of a data analysis team are replaced, the result is obtained quickly, manual intervention is not needed, the data processing efficiency is improved, and the calculation power and the labor are saved. Therefore, the problems of low product popularization accuracy, large manual workload and low treatment efficiency are at least partially solved, and the technical effects of improving the product popularization accuracy, reducing the manual workload and improving the treatment efficiency are realized.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario diagram of a data processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a data processing method according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a statistical association process for a plurality of user tags for a plurality of intended users, in accordance with an embodiment of the present disclosure;

FIG. 4 schematically illustrates a flow chart of a data processing method according to another embodiment of the present disclosure;

FIG. 5 schematically illustrates a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and

fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a data processing method according to an embodiment of the 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 only exemplary 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 present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to 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/or 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 should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having 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.).

In the technical solution of the present disclosure, the related user information (including, but not limited to, user personal information, user image information, user equipment information, such as location information, etc.) and data (including, but not limited to, data for analysis, stored data, displayed data, etc.) are information and data authorized by the user or sufficiently authorized by each party, and the related data is collected, stored, used, processed, transmitted, provided, disclosed, applied, etc. and processed, all in compliance with the related laws and regulations and standards of the related country and region, necessary security measures are taken, no prejudice to the public order, and corresponding operation entries are provided for the user to select authorization or rejection.

It should be noted that the data processing method and apparatus, the electronic device and the computer readable storage medium according to the embodiments of the present disclosure may be applied to the big data technical field, the cloud computing technical field and the financial technology field, and may also be applied to any field other than the big data technical field, the cloud computing technical field and the financial technology field, and the application fields of the data processing method and apparatus, the electronic device and the computer readable storage medium according to the embodiments of the present disclosure are not limited.

The embodiment of the disclosure provides a data processing method, which comprises the following steps: acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intended users who have accessed a target page, and the target page is used for displaying the target product; inquiring from a database based on user identifications of a plurality of intention users to obtain respective user labels of the intention users; and carrying out statistical association processing on a plurality of user tags of the plurality of intention users so as to determine a target tag from the plurality of user tags.

Fig. 1 schematically illustrates an application scenario diagram of a data processing method and apparatus, an electronic device, and a computer-readable storage medium according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

In an application scenario of the embodiment of the present disclosure, a user may initiate a request for performing data processing through the first terminal device 101, the second terminal device 102, and the third terminal device 103. In response to the request, the server 105 is operable to perform the data processing method of the embodiment of the present disclosure, including: acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intended users who have accessed a target page, and the target page is used for displaying the target product; inquiring from a database based on user identifications of a plurality of intention users to obtain respective user labels of the intention users; and carrying out statistical association processing on a plurality of user tags of the plurality of intention users so as to determine a target tag from the plurality of user tags.

It should be noted that the data processing method provided in the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the data processing apparatus provided by the embodiments of the present disclosure may be generally provided in the server 105. The data processing method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the data processing apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.

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.

The data processing method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 6 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a data processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the data processing method of this embodiment includes operations S201 to S203.

In operation S201, history data related to a history popularization process of a target product is obtained, where the history data includes user identifications of a plurality of intended users who have accessed a target page for displaying the target product;

in operation S202, based on the user identifications of the plurality of intended users, obtaining respective user tags of the plurality of intended users from the database by querying;

in operation S203, a statistical association process is performed on a plurality of user tags of a plurality of intended users such that a target tag is determined from the plurality of user tags.

According to the embodiment of the disclosure, the data processing method can be applied to a scene of determining a target user of product popularization. At present, the purchasing tendency of a user is usually determined based on a user label, and corresponding products are promoted to the user with the corresponding purchasing tendency, for example: based on the user label being 60-70 years old, the product suitable for the old people is pushed for the user with the user label. However, a certain deviation exists between the user tag and the actual purchasing trend of the user, so that the product popularization effect based on the user tag is not ideal, the product popularization object needs to be judged through manual subjective judgment, and a great amount of manpower needs to be input to repeatedly determine the actual purchasing trend of the user corresponding to the user tag. The user labels are generated by consuming a great deal of calculation force, so that the processing pressure and the manpower consumption of the computer are high. Therefore, in order to more accurately determine the product popularization object, the manual workload is reduced, and the process of determining the product popularization object can be automatically processed.

According to the embodiment of the disclosure, when the target product is promoted, the target label corresponding to the user who is easier to receive the promotion of the target product can be counted. When the target product is promoted again, the target product can be preferentially promoted to the user with the target label.

According to an embodiment of the present disclosure, in operation S201, the target product may be a product that needs to be promoted.

According to embodiments of the present disclosure, the historical promotion process may be a promotion for a target product over a period of time. The historical promotion process may be one time, for example, one promotion may be performed only for the target product. The historical popularization process can also be to carry out multiple popularization aiming at the target product, for example, carrying out 1 popularization per month.

According to the embodiment of the disclosure, the target product can be displayed on the target page, so that the target product is promoted. The target pages promoted each time can be the same or different.

According to embodiments of the present disclosure, the intended user may be a user accessing a target page. The user accesses the target page indicating that the user may have a purchase propensity for the target product. Each intended user corresponds to a user identification. The user identity is used to uniquely identify the user identity, and may be, for example, but not limited to, an ID of the user, and may be any other identity that may characterize the user identity.

According to an embodiment of the disclosure, the historical popularization process of the target product corresponds to historical record data, and the historical record data comprises user identifications of a plurality of intended users who have accessed the target page. One piece of history data can correspond to one history popularization process, and also can correspond to multiple history popularization processes.

For example, product a may be promoted once each at 2 months and 3 months, each promotion being to display product a on the same page. 2 months when 3 users access the page, the corresponding user identifications are the IDs of the 3 users respectively; 2 users in total visit the page in 3 months, and the corresponding user identifications are the IDs of the 2 users respectively. Setting each piece of history data to correspond to one history popularization process, so that the history data of 2 months comprises the IDs of the 3 users; the history data of 3 months includes the IDs of the above 2 users.

According to an embodiment of the present disclosure, a user tag may be generated in advance for each user, and each user may correspond to one or more user tags in operation S202. The user tag may include: one or more of a user basic attribute tag, a user resource holding condition tag, a user historical transaction condition tag, and a user historical browsing behavior tag. The user basic attribute tags may include, for example, the age, sex, region of the user, etc.; the user resource holding condition label can represent the asset holding condition of the user; the user history transaction case tag may include the type of product the user has transacted; the user history browse behavior tab may include the type of product that the user browses. It should be noted that, before the information of the user is acquired, the consent or the authorization of the user may be obtained. For example, before operation S202, a request to acquire user information may be issued to the user. In case that the user agrees or authorizes that the user information can be acquired, operation S202 is performed. According to embodiments of the present disclosure, the user tags for each intended user may be the same or different, for example: user 1 and user 2 have user tags of ages 40-50 (tag 1), female (tag 2), and browsed class a products (tag 3). For example, it may also be: user 1 has user tags of ages 40-50 years (tag 1), female (tag 2); user 2 has user tags of ages 15-25 years (tag 3), male (tag 4).

According to an embodiment of the present disclosure, a database stores user identifications and user tags corresponding to the user identifications. Based on the user identification of the intended user, the user tag of the intended user may be queried from the database.

For example, a page is used to display product A. The user labels corresponding to the IDs of the 2 users are respectively queried in a database, namely a user label 1 and a user label 2. User tag 1 is 40-50 years old (tag 1), female (tag 2), browsed type a product (tag 3); user tag 2 is 20-30 years old (tag 4), male (tag 5), browsed type b product (tag 6).

According to an embodiment of the present disclosure, the target tag is a user tag that more easily receives an intended user of the target product promotion in operation S203.

According to the embodiment of the disclosure, the user quantity of the intended user corresponding to each user label can be determined by carrying out statistical association processing on a plurality of user labels of a plurality of intended users. The higher the user quantity corresponding to the user tag, the more the user with the user tag tends to access the target page, so that the popularization of the target product is easier to receive. Through statistical association processing, user labels of intended users which are easier to receive target product popularization can be determined.

For example, product a was promoted once each at 2 months and 3 months. 2 months total 3 intention users, the corresponding user labels are respectively: user tag 1, user tag 2, user tag 3; 2 intent users are all 3 months, and the corresponding user labels are respectively: user tag 1, user tag 4. The statistics association processing is performed on the user tags, so that it can be determined that the user quantity corresponding to the user tag 1 is 2 and the user quantity corresponding to the user tag 2, the user tag 3 and the user tag 4 is 1 in 2 months and 3 months. Since the user tag 1 corresponds to a high number of users, the user tag 1 is determined as the target tag.

According to the embodiment of the disclosure, the target label most suitable for the target product can be obtained by analyzing the multiple historical popularization processes of the target product and carrying out statistical association processing on the user label of the intended user. Different from the method for constructing user portraits for users in the conventional technology, the method in the embodiment of the disclosure is based on actual product popularization data, the existing labels of the users are related through the actual popularization data, statistical association processing is carried out on the existing labels, the most suitable user labels are determined by back-pushing, the obtained user labels are most suitable for actual attributes and actual purchasing trends of the users because the actual popularization data are obtained by the user labels, the product popularization accuracy is higher based on the labels, the historical data are automatically analyzed through a system, a large number of analysis processes of a data analysis team are replaced, the result is obtained quickly, manual intervention is not needed, the data processing efficiency is improved, and the calculation power and the labor are saved.

According to an embodiment of the present disclosure, a target user including a target tag is determined from a plurality of intended users; and generating a product promotion label for the target user.

According to an embodiment of the present disclosure, the target user is a user with a target tag. The product popularization label can be automatically generated by the system and can also be set manually. The product promotion label options can be set on a target product promotion platform, and when the target product is promoted, the product promotion label options can be selected, so that a target page can be pushed for a target user corresponding to the product promotion label.

For example, the target tag is user tag 1, and the target user is a user who includes user tag 1. And setting a user tag 1 applicable to the product A on a target product A promotion platform, and when the target product A is promoted again, after the options are checked, pushing the target page of the product A for a user containing the user tag 1.

According to the embodiment of the disclosure, the product promotion label is arranged, so that the product is promoted to the user who receives the promotion of the target product more easily, the success rate of product promotion is improved, and the manual workload is saved.

Fig. 3 schematically illustrates a flowchart of a statistical association process for a plurality of user tags for a plurality of intended users, according to an embodiment of the present disclosure.

As shown in fig. 3, the flow of the statistical association processing for the plurality of user tags of the plurality of intended users in this embodiment includes operations S301 to S303.

In operation S301, a plurality of user tags of a plurality of intended users are grouped to generate N groups of group user tags corresponding to N historical popularization processes;

in operation S302, statistical ranking is performed on N groups of group user tags based on the user number dimension, respectively, to generate N groups of primary selection tags;

in operation S303, a target tag is determined from the N groups of primary selected tags.

According to the embodiment of the disclosure, one piece of history data can correspond to one history popularization process, and when the history popularization process of the target product is N times, N groups of history data are corresponding, wherein N is more than or equal to 2. For example, for a target product, there are 3 historical popularization processes, and each historical popularization process corresponds to one piece of historical record data, and then there are 3 pieces of historical record data.

According to the embodiment of the disclosure, when one piece of history data corresponds to one history popularization process, the user labels respectively corresponding to the plurality of user identifiers contained in the one piece of history data are a group of user labels, and the group of user labels corresponds to one corresponding history popularization process. For example, the user labels corresponding to the user identifiers included in the 1 st historical record data are respectively: user tag 1, user tag 2, user tag 3. The 5 user tags are the 1 st group of user tags, and the 1 st group of user tags corresponds to the first historical popularization process.

According to embodiments of the present disclosure, since the user tags for the intended users may be the same or different, a group of user tags may contain multiple user tags. In operation S301, grouping the plurality of user tags of the plurality of intended users may be, for example: a plurality of user tags included in a group of user tags are grouped, the same user tag being a group. After grouping is completed, one or more user tags may be included in a group of user tags, where the one or more user tags form a group of group user tags. For example: the first set of user tags comprises user tag 1, user tag 2, user tag 3. Counting the same user labels in the 1 st group of user labels to obtain 3 kinds of user labels: the first user label is user label 1, and the 2 nd user label is user label 2; the 3 rd user tag is user tag 3. Generating group 1 group user tags: user tag 1, user tag 2, user tag 3.

According to the embodiment of the disclosure, when there are N historical popularization processes, there are N pieces of history data corresponding to N groups of group user tags.

For example, the history popularization process of the target product has 2 times, corresponding to 2 pieces of history record data, corresponding to 2 groups of group user tags: the user labels corresponding to the user identifications contained in the first piece of history data are respectively as follows: user label 1, user label 2, the corresponding group 1 subgroup user label is: user tag 1, user tag 2; the user labels corresponding to the user identifications contained in the second piece of history data are respectively as follows: user label 1, user label 3, the corresponding second group of subgroup user labels are: user tag 1, user tag 3.

In accordance with an embodiment of the present disclosure, in operation S302, the statistical ranking process may be, for example: counting the number of users corresponding to each user tag in the group of user tags, and sorting each user tag in the group of user tags based on the number of users to generate a primary selection tag.

In accordance with an embodiment of the present disclosure, determining a target tag from the N groups of initially selected tags in operation S303 may be, for example: user tags included in each of the N groups of initially selected tags are determined, and a predetermined number of user tags are selected from the user tags to be marked as target tags. For example: and 3 groups of group user labels are combined, 3 groups of primary selection labels are correspondingly generated, and the number of target labels is set to be 1. The 3 groups of primary labels comprise a user label 1, a user label 2 and a user label 3, and the user label 1 is marked as a target label.

According to the embodiment of the disclosure, based on the number of users, the system automatically determines the target label, so that the user label which is more prone to access the target page can be simply and clearly determined, the manual workload is reduced, and the matching degree of the user label and the tendency of the user to access the target page is improved.

According to an embodiment of the present disclosure, determining a target tag from the N sets of primary tags includes:

Operation 11, calculating intersection of N groups of primary selection labels;

in operation 12, in a case where the number of user tags included in the intersection of the N groups of primary selected tags satisfies a preset number condition (for example, less than or equal to a preset number threshold value), the intersection of the N groups of primary selected tags is determined as the target tag.

In accordance with an embodiment of the present disclosure, at operation 11, the intersection of the N sets of primary labels may be, for example, user labels contained in each of the N sets of primary labels. For example, there are three sets of primary labels. The first group of primary labels comprises a user label 1, a user label 2, a user label 3, a user label 4 and a user label 5; the second group of primary labels comprises a user label 1, a user label 2, a user label 3, a user label 6 and a user label 7; the third group of primary labels includes user label 1, user label 2, user label 3, user label 6, user label 8. Because the three groups of primary labels all comprise the user label 1, the user label 2 and the user label 3, the intersection of the three groups of primary labels is the user label 1, the user label 2 and the user label 3.

In accordance with an embodiment of the present disclosure, at operation 12, the preset number threshold may be any preset value. And determining the intersection of the N groups of primary labels as a target label when the number of user labels contained in the intersection of the N groups of primary labels meets a preset number condition (for example, the preset number threshold value is smaller than or equal to the preset number threshold value).

Under the condition that the number of the user labels contained in the intersection of the N groups of the primary selection labels does not meet the preset number condition, other historical record data related to the historical popularization process of the target product can be continuously obtained, and the intersection of the primary selection labels is calculated until the number of the user labels contained in the intersection of the primary selection labels meets the preset number condition.

For example: the preset quantity threshold is 1; when three groups of primary selection labels are obtained, the intersection of the three groups of primary selection labels is 1 label: in the case of the user tag 1, the filtering condition is satisfied, and the user tag 1 is taken as the final selected target tag.

For example: the preset quantity threshold is 1; when three groups of primary selection labels are obtained, the intersection of the three groups of primary selection labels is 2 labels: under the condition that the user label 1+the user label 2 does not meet the screening condition, new history data related to the history popularization process of the target product needs to be continuously acquired, a new user label is obtained based on the new history data, a new primary selection label is generated according to the same processing method as the embodiment, and the new primary selection label is further intersected with the new primary selection label until the preset number of conditions are met, and the final target label is obtained.

According to the embodiment of the disclosure, further, on the basis of determining that the user tag of the user in the current stage is obtained based on the history data of the current statistical stage, the history data of the next statistical stage can be continuously obtained, and the user tag of the current stage is updated by combining the history data of the next statistical stage according to the data processing method described in the foregoing embodiment. That is, the method of the embodiment of the disclosure supports real-time dynamic update of the user tag, provides the latest user promotion scheme in real time, and continuously improves the promotion conversion rate.

For example: the target label is determined to be the user label A based on the historical popularization process of the target product of 1 month-5 months. And after the target product is promoted once again in 6 months, the target label can be determined again based on the historical promotion process of 1 month-6 months, and the target label is obtained as the user label B.

According to the embodiment of the disclosure, the target label is determined based on the intersection of the plurality of groups of primary selection labels, so that the matching degree of the target label and the target product can be improved. The target label can be updated in real time by acquiring a new historical popularization process in real time, so that the flexibility of target label selection is improved.

According to an embodiment of the present disclosure, performing statistical ranking processing on N groups of group user tags based on a user number dimension, respectively, generating N groups of primary selection tags includes:

operation 21, based on the user number dimension, counting the number of the intended users corresponding to each user tag in each group of group user tags respectively;

operation 22, sorting the group user tags according to the number of the intended users corresponding to the user tags in the group user tags to obtain N groups of tag sorting results;

and (23) generating N groups of primary selection labels according to the N groups of label sorting results.

According to the embodiment of the disclosure, a group of group user tags may contain multiple user tags, and since 1 user tag may correspond to 1 intended user, when one user tag contains n identical user tags, the number of intended users may be n.

According to an embodiment of the present disclosure, the number of users corresponding to each user tag in the group of user tags is counted separately in operation 21. For example, a first group of group user tags are: user tag 1, user tag 2, user tag 3. Counting the number of the intended users corresponding to the 3 kinds of user labels: the number of the intended users corresponding to the user tag 1 is 3, and the number of the intended users corresponding to the user tag 2 and the user tag 3 is 1.

According to the embodiment of the present disclosure, in operation 22, after counting the number of the intended users corresponding to each user tag, the ranking process may be performed on each user tag according to the number of users, so as to obtain a tag ranking result. The sorting process may be, for example, sorting from large to small or sorting from small to large according to the user quantity.

According to an embodiment of the present disclosure, based on the ranking processing result, a user tag that is more prone to access the target page may be determined, for example: when ordered from a large number to a small number of users according to intent, users corresponding to the top ranked user tags are more prone to access the target page. For example: the label ordering result is: user tag 1, user tag 2, user tag 3, the user to whom user tag 1 corresponds is more inclined to access the target page.

In accordance with an embodiment of the present disclosure, at operation 23, a preliminary tab may be generated based on the tab sorting result, which may be, for example: and selecting user labels ranked in a certain interval to form a primary selection label. The selected interval may be set manually. For example: and sorting the group 1 group user tags from large to small according to the user quantity, and selecting the user tags with the top 5 ranks to form a primary selection tag.

According to the embodiment of the disclosure, statistical ranking processing is performed on each group of group user tags, so as to generate corresponding primary selection tags. N groups of group user tags may generate N groups of primary labels.

Fig. 4 schematically shows a flow chart of a data processing method according to another embodiment of the present disclosure.

As shown in fig. 4, the data processing method of this other embodiment includes operations S401 to S408.

In operation S401, history data is acquired;

in operation S402, obtaining user tags of each of a plurality of intended users;

in operation S403, N groups of group user tags are generated;

in operation S404, statistical sorting processing is performed on the N groups of group user tags, respectively, to generate N groups of primary selection tags;

in operation S405, an intersection of N groups of primary labels is calculated;

in operation S406, it is determined whether the number of user tags included in the intersection of the N groups of primary selected tags satisfies a preset number condition, and when the preset number condition is not satisfied, other history data related to the history popularization process of the target product is continuously acquired, the intersection of the primary selected tags is calculated until the number of user tags included in the intersection of the primary selected tags satisfies the preset number condition, and the target tag is determined;

In operation S407, when a preset number of conditions are satisfied, determining a target tag;

in operation S408, a product promotion tab is generated for the target user.

For example: 3 pieces of history data relating to 3 times of history popularization process of the product a are acquired. User labels corresponding to the user identifications of the plurality of intended users contained in each piece of history data are respectively generated, and group user labels are respectively generated. And respectively carrying out statistical sorting treatment on the 3 groups of group user labels: sorting the user tags of each group of the small groups from large to small according to the user quantity, selecting the user tags with the top 5 ranks to form primary selection tags, and generating 3 groups of primary selection tags: the 1 st group of primary labels comprise a user label a, a user label b, a user label c, a user label d and a user label e; the group 2 primary labels comprise a user label a, a user label b, a user label d, a user label e and a user label f; the 3 rd group of primary labels comprises a user label a, a user label g, a user label k, a user label i and a user label j. The intersection of the 3 groups of primary labels is user label a. The preset number conditions are 1, so that the number of the user tags meets the preset number conditions, the target tag is determined to be the user tag a, and a product popularization tag is generated for the target user containing the user tag a: the user label a is arranged on a product A popularization platform and is suitable for the product A.

According to the embodiment of the disclosure, the history data may correspond to one history popularization process or may correspond to multiple history popularization processes. When the history data corresponds to a plurality of history popularization processes, a target label is determined based on the history data of the plurality of history popularization processes. When the history data corresponds to one history popularization process, determining the target label based on the history data of the one history popularization process.

According to the embodiment of the disclosure, a plurality of historical popularization processes can be performed on a target product, but the target label is determined only based on the historical record data of one historical popularization process.

Based on this, another possible implementation method for performing statistical association processing on a plurality of user tags of a plurality of intended users includes:

operation 31, counting the number of the intended users corresponding to each of the plurality of user tags based on the number dimension of the users;

operation 32, sorting the plurality of user tags according to the number of the intended users corresponding to the plurality of user tags, so as to obtain a tag sorting result;

and an operation 33, determining a target label from the plurality of user labels according to the label sorting result.

In the case of determining the target tag based on the history data of the plurality of history popularizing processes, the number of intended users to which the user tags correspond respectively is determined by operation 31 for the plurality of user tags included in total in the plurality of history popularizing processes, the plurality of user tags are ranked by operation 32, and the target tag is determined from the plurality of user tags by operation 33.

In the case of determining the target tag based on only the history data of 1 history popularization process, for a plurality of user tags included in the 1 history popularization process, the number of intended users corresponding to each of the user tags is determined through operation 31, the plurality of user tags are ranked through operation 32, and the target tag is determined from the plurality of user tags through operation 33.

For example, a certain piece of history data corresponds to 3 history promotion processes (may be for only 1 promotion process). The user labels corresponding to the user identifications contained in the history data are as follows: user tag 1, user tag 2, user tag 3. The history data includes 3 kinds of user tags, namely, user tag 1, user tag 2 and user tag 3. The number of the intended users corresponding to the user tag 1 is 3, and the number of the intended users corresponding to the user tag 2 and the user tag 3 is 1. The user labels are subjected to sorting treatment, and the generated label sorting result is as follows: user tag 1, user tag 2, user tag 3. And selecting the user tag with the first rank as a target tag, and taking the user tag 1 as the target tag. According to the embodiment of the disclosure, the target label can be determined based on various conditions, so that the data processing method of the disclosure can be used for various situations, and the universality of the data processing method of the disclosure is improved.

Based on the data processing method, the disclosure also provides a data processing device. The device will be described in detail below in connection with fig. 5.

Fig. 5 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.

As shown in fig. 5, the data processing apparatus 500 of this embodiment includes an acquisition module 510, a query module 520, and a statistics module 530.

The obtaining module 510 is configured to obtain historical record data related to a historical promotion process of a target product, where the historical record data includes user identifications of a plurality of intended users who have accessed a target page, and the target page is used to display the target product. In an embodiment, the obtaining module 510 may be configured to perform the operation S201 described above, which is not described herein.

The query module 520 is configured to query the database for respective user labels of the plurality of intent users based on the user identifications of the plurality of intent users. In an embodiment, the query module 520 may be configured to perform the operation S202 described above, which is not described herein.

The statistics module 530 is configured to perform statistical association processing on a plurality of user tags of a plurality of intended users, so as to determine a target tag from the plurality of user tags. In an embodiment, the statistics module 530 may be used to perform the operation S203 described above, which is not described herein.

According to an embodiment of the present disclosure, the data processing apparatus 500 further comprises a determination module and a generation module.

The determining module is used for determining target users containing target labels from a plurality of intention users; the generating module is used for generating a product promotion label for a target user.

According to an embodiment of the present disclosure, the statistics module includes a first generation sub-module, a second generation sub-module, and a first determination sub-module.

The first generation sub-module is used for grouping a plurality of user labels of a plurality of intention users to generate N groups of group user labels corresponding to N times of historical popularization processes; the second generation sub-module is used for respectively carrying out statistical sorting treatment on the N groups of group user labels based on the user number dimension to generate N groups of primary selection labels; the first determining submodule is used for determining target labels from N groups of primary labels.

According to an embodiment of the present disclosure, the first determination submodule includes a calculation unit and a determination unit.

The computing unit is used for computing the intersection of N groups of primary selection labels; the determining unit is used for determining the intersection of the N groups of primary selection labels as the target label under the condition that the quantity of the user labels contained in the intersection of the N groups of primary selection labels meets the preset quantity condition.

According to an embodiment of the present disclosure, the second generation submodule includes a statistical unit, an obtaining unit, and a generating unit.

The statistics unit is used for respectively counting the number of the intended users corresponding to each user tag in each group of group user tags based on the dimension of the number of the users; the acquisition unit is used for carrying out sorting treatment on the group user tags according to the number of the intended users corresponding to the user tags in the group user tags to obtain N groups of tag sorting results; the generating unit is used for generating N groups of primary selection labels according to the N groups of label sorting results.

According to an embodiment of the present disclosure, the statistics module further comprises a statistics sub-module, an obtaining sub-module, and a second determining sub-module.

The statistics sub-module is used for counting the number of the intended users corresponding to each of the plurality of user tags based on the dimension of the number of the users; the obtaining submodule is used for carrying out sorting treatment on the plurality of user tags according to the number of the intended users corresponding to the plurality of user tags, so as to obtain a tag sorting result; the second determining submodule is used for determining target labels from a plurality of user labels according to label sorting results.

Any of the acquisition module 510, the query module 520, and the statistics module 530 may be combined in one module to be implemented, or any of the modules may be split into multiple modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the acquisition module 510, the query module 520, and the statistics module 530 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware, such as any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of any of three implementations of software, hardware, and firmware. Alternatively, at least one of the acquisition module 510, the query module 520, and the statistics module 530 may be at least partially implemented as computer program modules that, when executed, perform the corresponding functions.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.

According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to an input/output (I/O) interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to an input/output (I/O) interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: 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 context of this 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, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code means for causing a computer system to carry out the data processing methods provided by the embodiments of the present disclosure when the computer program product is run on the computer system.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts 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 the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are 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 above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A data processing method, comprising:

acquiring historical record data related to a historical popularization process of a target product, wherein the historical record data comprises user identifications of a plurality of intended users who have accessed a target page, and the target page is used for displaying the target product;

Inquiring from a database based on the user identifications of the plurality of intention users to obtain the user labels of the plurality of intention users;

and carrying out statistical association processing on a plurality of user tags of the plurality of intention users so as to determine a target tag from the plurality of user tags.

2. The method of claim 1, further comprising:

determining a target user containing the target tag from the plurality of intention users;

and generating a product promotion label for the target user.

3. The method of claim 1, wherein the history data comprises N sets of the history data relating to N historical popularizing processes for the target product, the N being ≡2, the statistically correlating a plurality of user tags for the plurality of intended users comprising:

grouping a plurality of user tags of the plurality of intention users to generate N groups of group user tags corresponding to the N times of historical popularization processes;

respectively carrying out statistical sorting treatment on the N groups of group user labels based on the user number dimension to generate N groups of primary selection labels;

and determining the target label from the N groups of primary labels.

4. The method of claim 3, wherein determining the target tag from the N sets of initially selected tags comprises:

Calculating the intersection of the N groups of primary selection labels;

and under the condition that the quantity of the user labels contained in the intersection of the N groups of primary labels meets the preset quantity condition, determining the intersection of the N groups of primary labels as the target label.

5. The method of claim 3, wherein statistically ordering the N groups of group user tags based on a user number dimension, respectively, to generate N groups of initial tags comprises:

based on the dimension of the number of users, respectively counting the number of the intended users corresponding to each user tag in each group of the group of user tags;

according to the number of the intended users corresponding to each user tag in each group of the group user tags, sorting the group user tags to obtain N groups of tag sorting results;

and generating the N groups of primary selection labels according to the N groups of label sorting results.

6. The method of claim 1, wherein statistically associating a plurality of user tags for the plurality of intended users comprises:

based on the dimension of the number of users, counting the number of the intended users corresponding to each of the plurality of user tags;

according to the number of the intended users corresponding to each of the plurality of user tags, sorting the plurality of user tags to obtain a tag sorting result;

And determining a target label from a plurality of user labels according to the label sorting result.

7. The method according to claim 1, wherein:

the user tag includes at least one of: user basic attribute labels, user resource holding condition labels, user historical transaction condition labels and user historical browsing behavior labels.

8. A data processing apparatus comprising:

the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring historical record data related to a historical popularization process of a target product, the historical record data comprises user identifications of a plurality of intention users who have accessed a target page, and the target page is used for displaying the target product;

the query module is used for querying from the database based on the user identifications of the plurality of intention users to obtain the user labels of the plurality of intention users; and

and the statistics module is used for carrying out statistics association processing on a plurality of user tags of the plurality of intended users so as to determine a target tag from the plurality of user tags.

9. An electronic device, comprising:

one or more processors;

storage means 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 perform the method of any of claims 1-7.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-7.

11. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.