Data processing method and device, equipment and medium

Abstract

The embodiment of the invention relates to a data processing method and device, equipment and a medium. The method comprises the following steps: acquiring various content data in a content database and correlation data corresponding to the various content data respectively; the method further includes generating a first correspondence between the content data and the product based on the various content data. And generating a second corresponding relationship between the content data and the product units based on the first corresponding relationship and a predefined product unit mapping relationship. The method further includes generating, based on the correlation data and the second correspondence, a data processing result indicating a third correspondence between the user and a product unit level, where the product unit level indicates a degree of interest of the user in the product unit. Through the method, the corresponding relationship between the user and the product unit level of the product unit can be generated based on the content data of multiple sources, so that the user can be classified when no interaction exists between the user and the product, and the practicability is higher.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to the field of computer technology, and in particular to a data processing method, apparatus, device, and medium. Background Art

[0002] With the popularization of the Internet and people's increasing dependence on the Internet, there is more and more data on the Internet, which gradually forms a massive data resource. It becomes increasingly important to process and classify this data.

[0003] At present, the categories of products and services are becoming more and more diversified, and users have different demands for different products and services. In order to improve the user experience, we can combine the data resources on the Internet and use data processing technology to classify users and provide personalized products and services. Summary of the invention

[0004] Embodiments of the present disclosure provide a data processing method, apparatus, device, and medium.

[0005] According to a first aspect of the present disclosure, a data processing method is provided. The method includes obtaining a variety of content data in a content database and correlation data corresponding to the various content data, wherein the correlation data indicates the correlation between the user and the content data, and the various content data include at least graphic content data, video content data, and live content data. The method also includes generating a first correspondence based on the multiple content data, wherein the first correspondence indicates the product corresponding to the various content data in the multiple content data. The method also includes generating a second correspondence based on the first correspondence and a predefined product unit mapping relationship, wherein the second correspondence indicates the product unit corresponding to the various content data in the multiple content data. The method also includes generating a data processing result based on the correlation data and the second correspondence, wherein the data processing result indicates a third correspondence between the user and the product unit level, wherein the product unit level indicates the user's interest in the product unit.

[0006] In a second aspect of the present disclosure, a data processing device is provided. The device includes an acquisition module configured to acquire a variety of content data in a content database and correlation data corresponding to the various content data, wherein the correlation data indicates the correlation between a user and the content data, and the various content data at least include graphic content data, video content data, and live content data; a first generation module configured to generate a first correspondence based on the various content data, wherein the first correspondence indicates the product corresponding to the various content data in the various content data; a second generation module configured to generate a second correspondence based on the first correspondence and a predefined product unit mapping relationship, wherein the second correspondence indicates the product unit corresponding to the various content data in the various content data; and a result generation module configured to generate a data processing result based on the correlation data and the second correspondence, wherein the data processing result indicates a third correspondence between the user and the product unit level, wherein the product unit level indicates the user's interest in the product unit.

[0007] In a third aspect of the present disclosure, an electronic device is provided, comprising at least one processor; and a storage device for storing at least one program, wherein when the at least one program is executed by the at least one processor, the at least one processor implements the method according to the first aspect of the present disclosure.

[0008] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0009] It should be understood that the contents described in this content section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0011] Figure 1 A schematic diagram illustrating an example environment in which the apparatus and / or method of embodiments of the present disclosure may be implemented;

[0012] Figure 2 A schematic diagram illustrating a data processing method according to some embodiments of the present disclosure is shown;

[0013] Figure 3 A schematic diagram of a scenario of determining a user's product unit level according to an embodiment of the present disclosure is illustrated;

[0014] Figure 4 Another schematic diagram illustrating an implementation scenario of a data processing method according to some embodiments of the present disclosure;

[0015] Figure 5 A schematic diagram of a display interface for displaying data processing results generated by a data processing device to a user according to an embodiment of the present disclosure is illustrated;

[0016] Figure 6 A schematic diagram illustrating an example of a data processing method according to an embodiment of the present disclosure;

[0017] Figure 7 A schematic block diagram of a data processing device according to an embodiment of the present disclosure is illustrated; and

[0018] Figure 8 A schematic block diagram of an example device suitable for implementing embodiments of the present disclosure is illustrated.

[0019] In the various drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0020] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of the relevant laws, regulations and relevant provisions. In response to receiving the user's active request, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require the acquisition and use of the user's personal information. Thus, the user can autonomously choose whether to provide personal information to the electronic device, application, server or storage medium and other software or hardware that performs the operation of the technical solution of the present disclosure according to the prompt message. The user interaction operation or the interaction between the user and the content involved in this disclosure, as well as the data related to the user operation (including but not limited to the data used for analysis, the stored data, the displayed data, etc.), are all recorded, collected or stored with the authorization of the user or with full authorization of all parties, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse. In the technical solution of the embodiment of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of the information related to the user involved are in accordance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0022] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0023] As mentioned above, various products appear in people's lives, but users do not have demand for all products. Therefore, before providing products to users, it is possible to first determine whether the user is interested in the product. For example, users can be classified, and different categories can correspond to different levels of interest of users in products. In the past, the method of classifying users was usually based on the existing interaction operations between users and products, such as purchase operations. This method requires obtaining the interaction data between users and products. For products that users have not yet acquired or used, since there is no interaction data between users and products, it is impossible to classify users, so this classification method is poor in practicality.

[0024] At least to solve the above and other potential problems, the embodiment of the present disclosure proposes a data processing method. In this method, a data processing device first obtains content data and correlation data corresponding to the content data, generates a first correspondence indicating the product corresponding to the content data and a second correspondence indicating the product unit corresponding to the content data. The data processing device also generates a data processing result based on the second correspondence and the correlation data, and the data processing result includes a correspondence between the user and the product unit level, and the product unit level indicates the user's interest in the product unit. Through this method, the data processing device can generate a data processing result based on the content data and the correlation data to indicate the user's interest in the product unit (the product unit can, for example, correspond to a type of product, such as a Bluetooth headset, a smart phone or a laptop computer). In this way, the user can be classified without obtaining the interaction data between the user and the product. When the user has not yet obtained the product or has an interaction operation with the product, the user's interest in the product unit can also be determined. This method is highly practical. In addition, classifying users at the granularity of product units rather than at the granularity of products can avoid generating too many results due to too many products, is more conducive to the use of product providers, and has higher practicality.

[0025] The embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings. Figure 1 An example environment in which the devices and / or methods of embodiments of the present disclosure may be implemented is shown. Figure 1 As shown, the environment 100 may include a data source 110 and a data processing device 120. The data source 110 may include multiple databases, and each database may store different content data. Exemplarily, the data source 110 may include a graphic database 110-1, a video database 110-2, and a live database 110-3, wherein the graphic database 110-1 may store graphic content data, the video database 110-2 may store video content data, and the live database 110-3 may store live content data.

[0026] In the embodiments of the present disclosure, content data corresponds to content and is a data storage form of content. Content may be, for example, video content, graphic content, live content, etc., wherein video content may include a complete video or clip of a movie or TV, or a personal video shot and uploaded by a user, graphic content may include articles and / or pictures created by a user or a platform, etc., and live content may include a video stream intercepted by a live platform when the anchor is broadcasting live. In the embodiments of the present disclosure, the content may include a product, for example, a content clip of a video content may include an introduction of a product by the content author, and another example is that a video frame of a video content may include an image of a product, and another example is that a live content may include an introduction of a product by the anchor. In some embodiments, the content data may include indication data indicating a product, and the client may display the product on the interface of the content through the indication data when displaying the content through the content data. Each database in the embodiments of the present disclosure may exist in the form of an independently configured storage device, or may be configured in the same memory, and the database may also be configured together with other devices, such as being configured in a server. In some embodiments, content data of the same type may also be stored in two or more databases.

[0027] Each database in the data source 110 may also store correlation data corresponding to the content data, indicating the correlation between the user and the content, for example, indicating the identification (UID) of the user who interacts with the content. The interaction between the user and the content may be, for example, operations such as the user clicking or browsing the content. The correlation data may also indicate the length of time the user browses the content, the number of times the content is browsed, and the number of times the content is clicked. In some embodiments, the user's interaction with the content may include the user's interaction with the product corresponding to the content, and the correlation data may also indicate the number of times the user interacts with the product in the content. In some embodiments, the correlation data also includes the user correlation time, which is used to indicate the time when the user's interactive operation on the content occurs. In some embodiments, the correlation time may indicate the release time of the content data. In the embodiments of the present disclosure, the correlation data may be stored together with the corresponding content data in a database, or the correlation data may be stored independently in a separate database.

[0028] Examples of the data processing device 120 include, but are not limited to, server computers, minicomputers, mainframe computers, personal computers, multiprocessor systems, handheld or laptop devices, mobile devices (such as mobile phones, personal digital assistants (PDAs), media players, etc.), consumer electronics products, and distributed computing environments including any of the above systems or devices. The data processing device 120 can obtain content data from the data source 110 and process the content data. In some embodiments, the data processing device 120 can parse the content data and obtain indication data included in the content data, such as indication data indicating a product. In some embodiments, the data processing device 120 can process the content data through a predefined recognition algorithm or a neural network model to identify the product in the content.

[0029] like Figure 1 As shown, the data processing device 120 can obtain content data and correlation data from the data source 110, and the data processing device 120 can process the content data and correlation data and generate a data processing result 130. The data processing result 130 may include a correspondence between users and product unit levels of product units. In an embodiment of the present disclosure, a product unit is also referred to as a standardized product unit (SPU), which is a set of reusable, easily retrievable standardized information that describes product characteristics, and is the smallest unit of product information aggregation. A product unit may correspond to one or more products. For example, the product unit "Brand A Bluetooth Headset" may correspond to multiple Bluetooth headset products of Brand A. It should be understood that the enumeration of product units in the present disclosure is only illustrative, and the product unit may also have a smaller or larger classification granularity, which is not limited in the present disclosure. It should also be understood that Figure 1 The environment 100 shown is only for illustration, and the present disclosure does not limit the specific forms of the data source 110 and the data processing device 120 .

[0030] Combined with the above Figure 1 A schematic diagram of an example environment in which the device and / or method of the embodiments of the present disclosure may be implemented is described below. Figures 2 to 6 The data processing method provided by the present disclosure is described. Figure 2 A schematic diagram of a data processing method according to some embodiments of the present disclosure is shown. Figure 2 The method shown can be found in Figure 1 The data processing device 120 shown in the figure or any other suitable device is executed. Figure 2 , method 200 may include blocks 202 to 208 .

[0031] At box 202, the data processing device obtains a variety of content data in the content database and correlation data corresponding to the various content data, wherein the correlation data indicates the correlation between the user and the content data. The content database may include a variety of content data, such as video content data, text content data, etc. Each type of content data may include multiple content data, each content data may correspond to a correlation data, the correlation data may be stored together with the content data in a database, and the data processing device may obtain the content data and the correlation data from the database simultaneously or successively. In some embodiments, the correlation data and the content data exist together in a content data packet. In some embodiments, the data processing device may obtain multiple content data from a content database, exemplarily, obtain multiple graphic content data from the graphic database 110-1. In some embodiments, the data processing device may obtain multiple content data from multiple content databases, exemplarily, multiple graphic content data in the graphic database 110-1 and multiple video content data in the video database 110-2 may be obtained.

[0032] At box 204, the data processing device generates a first correspondence based on the content data, and the first correspondence indicates the product corresponding to the content data. The first correspondence may indicate the products corresponding to various content data in the multiple content data. In some embodiments, each content data may correspond to a first correspondence, and a first correspondence indicates a product corresponding to a content data. In some embodiments, the first correspondence may include the content ID of the content data and the indication data indicating the product, and the indication data may be, for example, the product ID. In some embodiments, the first correspondence may exist in the form of a table. In an embodiment of the present disclosure, the content data may correspond to the product. In one example, when a video is played through the video content data, a specific product is included in the video clip. In another example, when a video is played through the video content data, a picture or logo indicating the product is displayed on the interface of the video. In some embodiments, the content data may include indication data indicating the product, such as a product ID or a product link, so that the player can display the product on the interface of the video through the indication data when playing the video through the content data.

[0033] The data processing device can process the acquired content data to determine the indication data of the indicating product corresponding to the content data, and then generate a first corresponding relationship based on the indication data. In some embodiments, the data processing device can identify the content data and identify the candidate products in the content data. In one example, when the content data is video content data, the data processing device can parse the video content data to obtain multiple video frames of the video, and then perform feature extraction on each video frame to extract product features, thereby identifying the candidate products. In another example, the content data is graphic content data, and the data processing device can identify the text content corresponding to the product in the graphic content through natural language processing (NLP), thereby identifying the candidate products. The data processing device can match the candidate products with the pre-configured pre-stored product list, thereby determining the indication data of the indicating product, and then generating a first corresponding relationship. It should be understood that the present disclosure does not limit the method of identifying the candidate products from the content data. In some embodiments, the product can also be identified from the content data through a pre-configured neural network model or a computer vision model.

[0034] At box 206, the content processing device generates a second corresponding relationship based on the first corresponding relationship and the predefined product unit mapping relationship, and the second corresponding relationship indicates the product unit corresponding to the content data. Among them, the product unit mapping relationship can indicate the product unit to which the product belongs, and multiple different products can belong to one product unit together. In some embodiments, the tea grower unit mapping relationship can be, for example, a predefined mapping table, which can include indication data indicating the product, such as a product ID, and the product unit corresponding thereto. The first corresponding relationship includes indication data indicating the product corresponding to the content data, and the data processing device can determine the product unit to which the product corresponding to the content data belongs based on the matching of the indication data included in the first corresponding relationship and the indication data included in the predefined product unit mapping relationship.

[0035] In some embodiments, the products corresponding to the various content data obtained in the aforementioned box 202 may belong to multiple product units, and the second correspondence generated by the data processing device may indicate the multiple product units corresponding to the various content data. The content processing device may generate a second correspondence, which may indicate the product units corresponding to various content data in the multiple content data. In some embodiments, the content processing device may generate multiple second correspondences, each second correspondence indicating a product unit corresponding to a content data. In some embodiments, the second correspondence may exist in the form of a table.

[0036] At box 208, the data processing device can generate a data processing result based on the correlation data and the second correspondence, and the data processing result indicates a third correspondence between the user and the product unit level. The correlation data indicates the correlation of the user to the content data, such as the user's interactive operation on the content, including but not limited to the user's clicks on the content and the number of clicks, or the user's browsing of the content and the number of browsings, or the user's browsing time of the content. In some embodiments, the content data includes indication data indicating the product, and the correlation data corresponding to the content can also indicate the user's interactive operation on the product, such as a click operation on the product. In some embodiments, the correlation data can also include correlation time, which is used to indicate the time when the user's interactive operation on the content occurs. In some embodiments, the correlation time also indicates the time when the user's interactive operation on the product occurs. In some embodiments, the correlation time is used to indicate the release time of the content.

[0037] The product unit level may indicate the user's interest in the product unit level. For example, the product unit level may include five levels, namely "A1", "A2", "A3", "A4" and "A5", wherein "A1" indicates that the user's interest in the product unit is understanding, "A2" indicates that the user's interest in the product unit is attraction, "A3" indicates that the user's interest in the product unit is inquiry, "A4" indicates that the user's interest in the product unit is action, and "A5" indicates that the user's interest in the product unit is advocacy. The product unit level may also be other types of level divisions, which may be predefined, and the present disclosure does not limit this.

[0038] The data processing device may determine the correspondence between the user and the product unit level based on the predefined product unit level determination relationship. For example, the predefined product unit level determination rule may be as shown in Table 1:

[0039] Table 1

[0040] Product unit level Relevance of user to content A1 View content 1-4 times A2 View content more than 5 times A3 Click the product more than 1 time A4 Click the product more than 2 times A5 Click the product more than 5 times

[0041] For a user, it is possible to belong to different product unit levels of a product unit at the same time. For example, if a user browses the content once and clicks on the product in the content once, the user can belong to the A1 level and the A3 level of the product unit corresponding to the content data, and the data processing device can determine that the user corresponds to the A1 level and the A3 level. In some embodiments, a user can correspond to different product unit levels of different product units at the same time, for example, it can correspond to the A1 level of product unit 1 and the A2 level of product unit 2 at the same time. It should be understood that the product unit level determination rules shown in Table 1 are only exemplary, and the present disclosure is not limited to this. In some embodiments, different product units can correspond to different product unit level determination rules. In some embodiments, multiple product units can belong to an industry, and all product units in an industry have the same product unit level determination rules. In some embodiments, multiple product units can belong to different industries, for example, the product unit level determination rules vary from industry to industry. The industry in the embodiment of the present disclosure can be, for example, the "food industry", "home appliance industry" or "beauty industry", or other types of industries, and the industry division granularity can also be finer or coarser, which is not limited by the present disclosure. The product unit grade determination rules may be pre-configured in the data processing device. Optionally, the product unit grade determination rules may be obtained by the data processing device from a predefined rule database based on the type of product unit, such as the industry to which the product unit belongs.

[0042] In some embodiments, the data processing result generated by the data processing device may be in the form of a table, which may include information indicating a product unit (e.g., a product unit ID), information indicating a user (e.g., a user ID), and a product unit level to which the user belongs. In some embodiments, the table may also include information indicating a correlation time.

[0043] Through the above method 200, the data processing device can determine one or more content data related to a user, determine the products corresponding to these content data, and determine one or more product units corresponding to the products, and then determine the product unit level of the product unit to which the user belongs. The data processing device can also determine the product units and product unit levels corresponding to multiple users. In this way, based on the content data and the correlation data, the data processing device can generate the corresponding relationship between the user and the product unit level, realize the classification of users, so that the user classification can be realized without obtaining the interaction data between the user and the product. When the user has not yet obtained the product or has an interaction operation with the product, the user's interest in the product unit can also be determined. This method is highly practical. In method 200, the product is corresponded to the product unit, and a product unit can include multiple products. Therefore, when generating the result, it is not necessary to generate the number of users corresponding to each product based on the product, which can avoid the problem of too many products and complicated results caused by too large a content data volume. Generating the result based on the product unit can make the generated result simpler. In addition, the user classification result of a product unit can be determined based on multiple content data, with a larger sample size, which can make the result more reliable.

[0044] Figure 3 This is a schematic diagram of a scenario 300 for determining a user's product unit level provided by an embodiment of the present disclosure. In the scenario 300, a user 350 browses or clicks on content 310, or clicks on a product in content 310, and user 350 is related to content 310. The data processing device can determine the product 320 corresponding to content 310 by the method in the aforementioned box 204, and determine the product unit 330 corresponding to product 320 by the method in the aforementioned box 206. The product unit level 340 of the product unit 330 may include three levels of "A1", "A2" and "A3". The data processing device can determine the correspondence between user 350 and product unit level 340 based on the correlation between user 350 and content 310 by the method in the aforementioned box 208. For example, it can be determined that user 350 belongs to level A3. It should be understood that Figure 3 The illustrated scenario 300 is merely an illustration of an embodiment of the present disclosure and does not constitute a limitation to the present disclosure.

[0045] In the embodiment of the present disclosure, the data processing device may obtain multiple content data associated with a user from a data source, or may obtain multiple content data from a data source and determine one or more users associated with the content data. Figure 4 A method of acquiring various content data, identifying users, and then counting the number of users is described. Figure 4 is another schematic diagram of an implementation scenario of the data processing method of some embodiments of the present disclosure, in Figure 4The scene 400 shown includes a data source 4001, a data processing device 4002, and a client 4003. The data source 4001 may store content data such as video data 4001-1, graphic data 4001-2, and live broadcast data 4001-3. The data source 4001 may also store other content data. The content data may include data indicating a user who has interactive operations on the content, such as a user ID, and the content data may also include the number of interactive operations of the user on the content. In some embodiments, one content data may include data indicating multiple users. The data processing device 4002 may obtain multiple content data from the data source 4001, and execute the methods of boxes 402 to 408 to determine the number of users of each product unit level of the product unit. The data processing device 4002 may provide the number of users included in the determined product unit level to the client 4003, and the client 4003 is configured with a user interaction interface, so that the client 4003 can display the number of users of each product unit level of the product unit in response to the user's query operation. The data source 4001 in the scene 400 may correspond to the aforementioned data source 110, and the data processing device 4002 may correspond to the aforementioned data processing device 120. The method executed by the data processing device 4002 is described below.

[0046] See also Figure 4 , the data processing method performed by the data processing device 4002 may include boxes 402 to 408. Each of the multiple content data acquired by the data processing device may include one or more content data. In box 402, the data processing device 4002 may identify the indication data indicating the product from the acquired multiple content data, thereby associating the content with the product. In some embodiments, in box 402, the data processing device 4002 may generate a list, which includes multiple content IDs indicating multiple content data, and product IDs corresponding to the content. In some embodiments, the list may also include the IDs of users who have interactive operations on the content, and the number of interactive operations of each user on the content. The method for the data processing device 4002 to identify the indication data from the content data may be performed with reference to the method in the aforementioned box 204.

[0047] In block 404, the data processing device 4002 determines a product unit corresponding to the product, wherein one product unit may correspond to multiple products. The multiple content data acquired by the data processing device 4002 may each correspond to a product, and these products may belong to one product unit together, or may each belong to a different product unit. The data processing device 4002 may determine the product unit to which the product belongs based on a predefined product unit mapping relationship. The product unit mapping relationship may be pre-configured in the memory of the data processing device 4002, or may be acquired by the data processing device 4002 from other devices.

[0048] In block 406, the data processing device 4002 determines the product unit level to which the user belongs. In the aforementioned block 402, the data processing device 4002 can determine the users who have interactive operations on each content and the number of interactive operations of each user on the content from the acquired multiple content data. The data processing device 4002 can determine the correspondence between the product unit and the user based on the correspondence between the content and the product and the correspondence between the product and the product unit determined in blocks 402 and 404, and further determine the product unit level to which the user belongs. The method for the data processing device 4002 to determine the product unit level of the user can also be performed with reference to the method in the aforementioned block 208.

[0049] In box 408, the data processing device 4002 may count the number of users included in each product unit level of the product unit to generate a data processing result. A product unit may correspond to multiple product unit levels, and multiple users may belong to a product unit level of a product unit. The data processing device 4002 may determine the number of users of each product unit level. In some embodiments, the data processing device 4002 may count the total number of users of all product unit levels corresponding to the product unit. In some embodiments, the content data may also include data indicating the time when the user interacts with the content. The data processing device 4002 may determine the correlation time corresponding to the product unit and the user based on this. For example, the time when user A interacts with content A is T, content A corresponds to product A, and product A belongs to product unit A, then the correlation time corresponding to product unit A and user A may be determined to be T. The result generated by the data processing device 4002 in the aforementioned box 406 may include the correlation time. In box 408, when the data processing device 4002 determines the number of users included in the product unit level, it may only count the results of the correlation time within the preset time. In some embodiments, the preset time may be 15 days or 30 days. In this way, the data processing device 4002 can determine the number of users at each product unit level of the product unit based on the recent user interaction operations on the product, and the number of users finally determined by the data processing device will be more timely.

[0050] In some embodiments, in box 408, the data processing device 4002 may store information indicating a product unit (e.g., product unit ID), a product unit level, and the number of users corresponding to the product unit level in association as aggregated data. In some embodiments, in box 404, each of the multiple products corresponds to a different product unit, that is, multiple product units are determined. In box 406, each product unit may have the same product unit level division rule and product unit level determination rule. In box 408, the data processing device may determine the average number of users of the multiple product units at the same product unit level. For example, in box 404, product unit 1 and product unit 2 are determined, the number of users of product unit 1 at level A is X, and the number of users of product unit 2 at level A is Y, then the average number of users of level A may be determined to be (X+Y) / 2. The aggregated data stored by the data processing device may also include the average number of users. In some embodiments, the data processing device 4002 may provide the locally stored aggregated data to the client 4003. In this way, when client 4003 obtains aggregate data and displays it to the user, it can display the number of users of a product unit at a product unit level, and at the same time display the average value of the number of users of all product units at the product unit level, which is convenient for user reference and improves the user experience.

[0051] It should be understood that Figure 4 The illustrated scenario is for illustration only and does not constitute a limitation of the present disclosure. In some embodiments, the data source 4001 may be integrated with the data processing device 4002, for example, the data source 4001 may be one or more memories configured in the data processing device 4002. In some embodiments, the data processing device 4002 may have the function of the client 4003, for example, the client 4003 and the data processing device 4002 may be configured as a whole. In some embodiments, the data processing device 4002 may execute the aforementioned method 200, or combine the aforementioned method 200 with the methods in blocks 402 to 408, for example, block 408 may be executed after block 208 is executed.

[0052] Figure 5 is a schematic diagram of a display interface for displaying data processing results generated by a data processing device to a user in an embodiment of the present disclosure. Figure 5 , the interface 500 may include a selection area 510 and a number display area 520. It should be understood that Figure 5 The dashed box in the figure is only used to distinguish the areas in the interface 500, and does not mean that the interface 500 will display Figure 5The dotted box shown. In response to a user's click operation, the selection area 510 can display candidate product units and receive a user's operation of selecting a product unit. In response to the selection area 510 receiving the user's operation of selecting a product unit, the headcount display area 520 can display the number of users of each product unit level of the product unit selected by the user. Exemplarily, in response to receiving the user's operation of selecting product unit 3, the headcount display area 520 can display the number of users of the five product unit levels of product unit 3, including the number of users of each of the five levels of "A1", "A2", "A3", "A4" and "A5", and the headcount display area 520 can also display the total number of users of product unit 3. It should be understood that Figure 5 This is only an example of an interface displayed based on data processing results in an embodiment of the present disclosure, and does not constitute a limitation of the present disclosure. The present disclosure does not limit the specific location of each area in the display interface, nor does it limit the display content of each area.

[0053] Figure 6 is a schematic diagram of an example of a data processing method according to an embodiment of the present disclosure. The method shown in example 600 may be executed by a data processing device, which may be, for example, the aforementioned data processing device 120 or the data processing device 4002. Figure 6Example 600 may include blocks 602 to 622. In block 602, the data processing device obtains content data and correlation data corresponding to the content data, the correlation data including a user ID and interaction data, the interaction data may indicate the user's interaction operation on the content, such as click or browse operations, and the number of times the user clicks or browses the content. In block 604, the data processing device parses the content data to determine whether the content data includes a product ID, if so, executes block 610; if not, executes block 606. In block 606, the data processing device identifies a candidate product from the content data through a predefined product recognition model, for example, the features of the candidate product may be extracted from the content data. In block 608, the data processing device determines the product ID corresponding to the content data based on the matching of the candidate product with the pre-stored product in the pre-stored product list, the pre-stored product list including the features of multiple pre-stored products and the product IDs corresponding thereto, the data processing device may determine the pre-stored product in the pre-stored product list having the same features as the candidate product as the product corresponding to the content data, thereby determining the product ID corresponding to the content data. In box 610, the data processing device parses the content data and directly obtains the product ID from the content data. In box 612, the data processing device generates a first list to indicate the product corresponding to the content data. The first list includes the content ID, product ID and the release time of the content data corresponding to the content data. One first list can correspond to one content. In box 614, the data processing device generates a second list to indicate the product unit corresponding to the content based on the predefined product unit mapping relationship and the first list. The second list may include the content ID, product unit ID and the release time of the content data. One content may correspond to one second list. In box 616, the data processing device obtains the product unit level determination rule corresponding to the product unit ID from the predefined rule database based on the product unit ID in the second list. In box 618, the data processing device determines the product unit level to which the user corresponding to the content belongs based on the product unit level determination rule, the second list and the correlation data corresponding to the content, and generates a third list. The third list includes the product unit ID, the product unit level, the user ID corresponding to the product unit level and the release time of the content data. Through blocks 604 to 618, the data processing device can generate a third list based on one content data, and can generate multiple third lists based on multiple content data. In block 620, for multiple third lists, the data processing device can count the third lists with a release time within a preset time based on the release time of the content data included therein, and determine the number of users of each product unit level in multiple product unit levels of the product unit. In block 622, the data processing device sends the number of users of each product unit level in multiple product unit levels of the product unit to the client in response to receiving a data query request from the client.

[0054] Based on the above technical solution, the data processing device can determine the product unit level to which the user belongs, and count the number of users of each product unit level of a product unit, and provide it to the client, so that the user can obtain the number of users of each product unit level of the product unit through the client, and the user's needs are met. It should be understood that the method in Example 600 is only an example of an embodiment of the present disclosure, and does not constitute a limitation of the present disclosure. The method in Example 600 can also be implemented in combination with the aforementioned method 200, or in combination with the method performed by the data processing device 4002 in the aforementioned scenario 400.

[0055] Combined with the above Figures 2 to 6 The method provided in the embodiment of the present disclosure is described, and then Figure 7 and Figure 8 The device provided by the embodiment of the present disclosure is described. Figure 7 FIG. 7 is a schematic diagram of a data processing device 700 according to an embodiment of the present disclosure. Figure 7 As shown, the data processing device 700 may include an acquisition module 702, which is configured to acquire a variety of content data in a content database and correlation data corresponding to the various content data, wherein the correlation data indicates the correlation between the user and the content data, and the various content data include at least graphic content data, video content data and live content data; a first generation module 704, which is configured to generate a first correspondence based on the various content data, and the first correspondence indicates the products corresponding to the various content data in the various content data; a second generation module 706, which is configured to generate a second correspondence based on the first correspondence and a predefined product unit mapping relationship, and the second correspondence indicates the product units corresponding to the various content data in the various content data; and a result generation module 708, which is configured to generate a data processing result based on the correlation data and the second correspondence, and the data processing result indicates a third correspondence between the user and the product unit level, wherein the product unit level indicates the user's interest in the product unit.

[0056] In some embodiments, the multiple content data include first content data, the first content data includes indication data corresponding to a product, the indication data is used to present the product on an interface for presenting content when the client presents the content through the first content data, and the first generation module 704 includes: a parsing module, configured to parse the first content data to determine the indication data of the first content data; and a first generation sub-module, configured to generate a first corresponding relationship based on the indication data.

[0057] Optionally, the correlation data acquired by the acquisition module 702 includes the number of clicks on the product by users.

[0058] In some embodiments, the multiple content data include second content data, which does not include product indication data, and the first generation module 704 includes: an identification module, configured to identify candidate products from the second content data through a product identification model; a matching module, configured to determine the product indication data based on matching the candidate products with pre-stored products in a pre-stored product list; and a second generation sub-module, configured to generate a first corresponding relationship based on the product indication data.

[0059] In some embodiments, the first correspondence includes a first list, which includes at least a content identification ID indicating content data and a product ID indicating a product, the second correspondence includes a second list, which includes at least a content ID and a product unit ID indicating a product unit, and the result production module 708 includes: a third list generation module, which is configured to generate a third list based on the correlation data and the second list, and the third list includes at least a product unit ID, a user ID corresponding to the user, and a product unit level.

[0060] In some embodiments, the result generation module 708 also includes: a rule acquisition module, configured to acquire a product unit level determination rule corresponding to the product unit based on the type of the product unit; and a third correspondence generation module, configured to generate a third correspondence between the user and the product unit level based on the product unit level determination rule, correlation data and the second correspondence.

[0061] In some embodiments, the data processing device 700 also includes: a user quantity determination module, configured to determine the number of users corresponding to the product unit level of the target product unit based on the data processing results of multiple users; and a storage module, configured to store the number of users, information indicating the target product unit, and the product unit level in association as aggregated data.

[0062] In some embodiments, the correlation data also includes correlation time, the data processing result also includes correlation time, and the user quantity determination module includes: a first quantity determination submodule, configured to determine the number of users corresponding to the product unit level of the target product unit based on the data processing result of the correlation time within a preset time.

[0063] In some embodiments, the data processing device 700 also includes: an average value determination module, configured to determine the average values ​​of multiple user quantities corresponding to product unit levels of multiple product units, respectively, and the storage module includes: a first storage submodule, configured to store the user quantity, the average value of multiple user quantities, information indicating the target product unit and the product unit level in an associated manner as aggregated data.

[0064] In some embodiments, the data processing device 700 further includes: a data providing module configured to provide aggregated data in response to receiving a data query request from a client, wherein the data query request is used to request the number of users corresponding to the product unit level of the target product unit.

[0065] In some embodiments, the correlation data includes at least one of the following: the number of views of users on the content corresponding to the content data; or the number of clicks on the content by users.

[0066] Figure 8 A schematic block diagram of an example device 800 that may be used to implement embodiments of the present disclosure is shown. Figure 1 The data processing device 120 or Figure 4 The data processing device 4002 in the embodiment can be implemented by using the device 800. Figure 8 As shown, the device 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 802 or computer program instructions loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0067] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage page 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0068] The various processes and processing described above, such as method 200, may be performed by processing unit 801. For example, in some embodiments, method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by CPU 801, one or more actions of method 200 described above may be performed.

[0069] The present disclosure may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present disclosure.

[0070] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of computer readable storage medium include: portable computer disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination of the above. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (e.g., a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0071] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0072] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0073] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0074] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0075] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0076] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0077] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A data processing method, comprising: Acquire multiple content data and correlation data respectively corresponding to the various content data in a content database, wherein the correlation data indicates correlation between a user and the content data, and the multiple content data at least includes graphic content data, video content data, and live content data; Based on the multiple content data, generating a first corresponding relationship, the first corresponding relationship indicating a product corresponding to each content data in the multiple content data; generating a second corresponding relationship based on the first corresponding relationship and a predefined product unit mapping relationship, wherein the second corresponding relationship indicates a product unit corresponding to each content data among the plurality of content data; and Based on the correlation data and the second correspondence, a data processing result is generated, the data processing result indicating a third correspondence between the user and a product unit level, the product unit level indicating the user's interest level in the product unit.

2. The method according to claim 1, wherein the multiple content data include first content data, the first content data include indication data corresponding to the product, the indication data is used to present the product on an interface for presenting the content when the client presents the content through the first content data, and generating the first correspondence based on the multiple content data includes: parsing the first content data to determine the indication data of the first content data; as well as Based on the indication data, the first corresponding relationship is generated. The method according to claim 2 , wherein the correlation data includes the number of clicks by the user on the product.

4. The method according to claim 1, wherein the plurality of content data includes second content data, the second content data does not include the indication data of the product, and generating the first correspondence based on the plurality of content data comprises: identifying candidate products from the second content data by using a product identification model; Determining the indication data of the product based on the matching of the candidate product with the pre-stored products in the pre-stored product list; as well as The first corresponding relationship is generated based on the indication data of the product.

5. The method according to claim 1, wherein the first correspondence relationship comprises a first list including at least a content identification ID indicating content data and a product ID indicating a product, the second correspondence relationship comprises a second list including at least the content ID and a product unit ID indicating the product unit, and Generating the data processing result includes: Based on the correlation data and the second list, a third list is generated, the third list including at least the product unit ID, a user ID corresponding to the user, and the product unit level.

6. The method according to claim 1, wherein generating the data processing result comprises: Based on the type of the product unit, obtaining a product unit grade determination rule corresponding to the product unit; as well as The third correspondence between the user and the product unit level is generated based on the product unit level determination rule, the correlation data, and the second correspondence.

7. The method according to any one of claims 1 to 6, further comprising: Determining the number of users corresponding to the product unit level of the target product unit based on the data processing results of the plurality of users; The number of users, information indicating the target product unit, and the product unit level are stored in association as aggregate data.

8. The method according to claim 7, wherein the correlation data further includes correlation time, the data processing result further includes the correlation time, and wherein determining the number of users corresponding to the product unit level of the target product unit comprises: Based on the data processing result of the correlation time within a preset time, the number of users corresponding to the product unit level of the target product unit is determined.

9. The method according to claim 7, further comprising: determining an average value of a plurality of numbers of users respectively corresponding to the product unit levels of a plurality of product units; as well as The method further comprises storing the number of users, the information indicating the target product unit, and the product unit level in association with each other as aggregate data, including: The number of users, the average value, the information indicating the target product unit, and the product unit level are stored in association as the aggregate data.

10. The method according to claim 7, further comprising: The aggregate data is provided in response to receiving a data query request from a client, wherein the data query request is used to request the number of users corresponding to the product unit level of the target product unit.

11. The method according to claim 1, wherein the correlation data comprises at least one of the following: the number of views of the content corresponding to the content data by the user; or The number of clicks on the content by the user.

12. A data processing device, comprising: An acquisition module is configured to acquire a variety of content data and correlation data respectively corresponding to the various content data in a content database, wherein the correlation data indicates the correlation between the user and the content data, and the variety of content data at least includes graphic content data, video content data and live content data; A first generating module is configured to generate a first corresponding relationship based on the multiple content data, wherein the first corresponding relationship indicates a product corresponding to each content data in the multiple content data; A second generating module is configured to generate a second corresponding relationship based on the first corresponding relationship and a predefined product unit mapping relationship, wherein the second corresponding relationship indicates a product unit corresponding to each content data in the multiple content data; as well as A result generation module is configured to generate a data processing result based on the correlation data and the second correspondence, wherein the data processing result indicates a third correspondence between the user and the product unit level, and the product unit level indicates the user's interest level in the product unit.

13. An electronic device, comprising: processor; as well as A memory coupled to the processor, the memory having instructions stored therein, wherein when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 11.

14. A computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions are executed by a processor to implement the method according to any one of claims 1 to 11.

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