Data processing method and device, content evaluation method and device, equipment and storage medium

Abstract

The invention provides a data processing method and device, a content evaluation method and device, equipment, a medium and a product, and relates to the technical field of data processing, in particular to the technical field of big data. According to the specific implementation scheme, data extraction is carried out on an interaction behavior log, a data set is obtained, the data set comprises at least one piece of interaction behavior data of the same object for to-be-evaluated content, and the interaction behavior data comprises an interaction behavior type and operation data corresponding to the interaction behavior type; under the condition that it is determined that multiple pieces of interaction behavior data with the same interaction behavior type exist in the data set, abnormal category recognition is conducted on the multiple pieces of interaction behavior data, and a recognition result is obtained; and processing the multiple pieces of interaction behavior data according to a processing mode matched with the recognition result to obtain target interaction behavior data.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, in particular to technical fields such as computer technology, data processing, and data analysis, and specifically to data processing methods, content evaluation methods, devices, electronic devices, storage media, and program products. Background Art

[0002] With the rapid development of computers, it is no longer an insurmountable difficulty to reconstruct and deeply analyze massive amounts of data. Using computer data processing software or platforms to analyze data, and then to integrate massive amounts of data, provides a basis for corporate decision-making, so as to make wise business strategies. How to analyze quickly and effectively has become a research focus. Summary of the invention

[0003] The present disclosure provides a data processing method, a content evaluation method, an apparatus, an electronic device, a storage medium, and a program product.

[0004] According to one aspect of the present disclosure, a data processing method is provided, comprising: extracting data from an interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type; in the case of determining that there are multiple interaction behavior data with the same interaction behavior type in the data set, identifying abnormal categories for the multiple interaction behavior data to obtain an identification result; and processing the multiple interaction behavior data in accordance with a processing method matching the identification result to obtain target interaction behavior data.

[0005] According to another aspect of the present disclosure, a content evaluation method is provided, comprising: evaluating content to be evaluated based on target interaction behavior data to obtain an evaluation result for characterizing the degree of interest; wherein the target interaction behavior data is obtained using the above-mentioned data processing method.

[0006] According to another aspect of the present disclosure, a data processing device is provided, including: an extraction module, used to extract data from an interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for a content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type; an identification module, used to identify abnormal categories of the multiple interaction behavior data when it is determined that there are multiple interaction behavior data with the same interaction behavior type in the data set to obtain an identification result; and a processing module, used to process the multiple interaction behavior data in a processing manner matching the identification result to obtain target interaction behavior data.

[0007] According to another aspect of the present disclosure, a content evaluation device is provided, comprising: an interest evaluation module, used to evaluate the content to be evaluated based on target interaction behavior data to obtain an evaluation result for characterizing the degree of interest; wherein the above-mentioned target interaction behavior data is obtained using the above-mentioned data processing device.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method as disclosed in the present disclosure.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method of the present disclosure.

[0010] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method of the present disclosure when executed by a processor.

[0011] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it 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

[0012] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0013] Figure 1 An exemplary system architecture to which the data processing method and device according to an embodiment of the present disclosure can be applied is schematically shown;

[0014] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram of determining target interaction behavior data according to a specific embodiment of the present disclosure is schematically shown;

[0016] Figure 4 A schematic diagram schematically shows a method for generating an evaluation result according to a specific embodiment of the present disclosure;

[0017] Figure 5 A schematic diagram schematically shows a data statistics process according to a specific embodiment of the present disclosure;

[0018] Figure 6A schematic diagram schematically shows a schematic diagram of updating target interaction behavior data according to an embodiment of the present disclosure;

[0019] Figure 7 A flowchart of a content evaluation method according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 8 A block diagram schematically shows a data processing device according to an embodiment of the present disclosure;

[0021] Fig. 9 A block diagram schematically shows a content evaluation device according to an embodiment of the present disclosure; and

[0022] Fig.10 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] On content platforms, accurately understanding users' interactive behaviors on content, such as likes and comments, is crucial to evaluating the attractiveness of content and user satisfaction. However, if the interactive behavior data recorded in the interactive behavior log is inaccurate, the interactive behavior log cannot accurately represent the user's true operational intentions.

[0025] Current technologies mostly rely on simple statistical methods, such as directly counting the number of times users click, view, or interact with content in interactive behavior logs. These statistical methods usually collect the behavior of users browsing content on computers or mobile devices, and then report and record it in server logs. The data is usually captured and recorded when the user performs interactive behavior. Subsequently, this data may be transmitted to a data warehouse, where the number of interactive behavior logs is further processed and analyzed. This approach has been proven to be effective in many cases, especially for processing data analysis tasks that do not require high real-time performance.

[0026] However, when the interaction behavior log is obtained in the above manner, the uniqueness of the records in the interaction behavior log is not ensured by callback or other means. Therefore, in the case of user misoperation or network anomaly, the interaction behavior data recorded in the interaction behavior log may be inaccurate.

[0027] For example, a user likes a piece of content and accidentally cancels the like and then likes it again, resulting in the interaction behavior log recording the interaction behavior data of the user liking, canceling the like, and liking the content in sequence; or due to network anomalies, the client repeatedly sends the user's like operation to the server, resulting in the interaction behavior log recording the interaction behavior data of a user liking the content twice.

[0028] Therefore, when statistics are collected on interaction behavior logs based on traditional data statistics methods, the user interaction information obtained is biased, and the exact interaction information cannot be accurately known, which in turn affects the effectiveness of analyzing the degree of interest in the content.

[0029] In view of this, in order to reduce the impact of inaccurate interactive behavior data on the effectiveness of the interest level analysis of the content to be evaluated, an embodiment of the present disclosure provides a data processing method. The data processing method includes: extracting data from the interactive behavior log to obtain a data set, wherein the data set includes at least one interactive behavior data of the same object for the content to be evaluated, and the interactive behavior data includes an interactive behavior type and operation data corresponding to the interactive behavior type; when it is determined that there are multiple interactive behavior data with the same interactive behavior type in the data set, abnormal category identification is performed on the multiple interactive behavior data to obtain an identification result; and the multiple interactive behavior data are processed in a processing method matching the identification result to obtain target interactive behavior data.

[0030] The embodiments of the present disclosure can eliminate abnormal interaction behavior data caused by user misoperation or network anomalies by extracting target interaction behavior data from interaction behavior logs, and can help platform operators understand content that is more attractive to users, thereby guiding operators to create content that is more in line with user preferences, thereby optimizing the way and content of product display and improving user satisfaction.

[0031] In the technical solution disclosed in this disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved are in compliance with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and they do not violate public order and good morals. For example, the acquisition and use of interactive behavior data are known and agreed by users, and are in compliance with the provisions of relevant laws and regulations, and do not violate public order and good morals.

[0032] In the technical solution of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0033] Figure 1 An exemplary system architecture to which the data processing method and apparatus according to an embodiment of the present disclosure can be applied is schematically shown.

[0034] It should be noted that Figure 1The examples shown are only examples of system architectures to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios. For example, in another embodiment, the exemplary system architecture to which the content processing method and apparatus can be applied may include a terminal device, but the terminal device may implement the content processing method and apparatus provided by the embodiments of the present disclosure without interacting with the server.

[0035] like Figure 1 As shown, the system architecture 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 used to provide a medium for 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 and / or wireless communication links, etc.

[0036] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only as examples).

[0037] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0038] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for the content 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 user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0039] The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server can also be a server of a distributed system, or a server combined with blockchain.

[0040] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103. Correspondingly, the data processing apparatus provided in the embodiment of the present disclosure can also be set in the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0041] Alternatively, the data processing method provided in the embodiment of the present disclosure may also be generally executed by the server 105. Accordingly, the data processing device provided in the embodiment of the present disclosure may generally be arranged in the server 105. The data processing method provided in the embodiment of the present disclosure may also be executed by a server or server cluster that is different from the server 105 and can communicate 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 device provided in the embodiment of the present disclosure may also be arranged in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0042] For example, the server 105 extracts data from the interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for the content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type; when it is determined that there are multiple interaction behavior data with the same interaction behavior type in the data set, abnormal category identification is performed on the multiple interaction behavior data to obtain an identification result; and the multiple interaction behavior data are processed in a processing method matching the identification result to obtain target interaction behavior data.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0044] It should be noted that the sequence numbers of the operations in the following method are only used as representations of the operations for the purpose of description, and should not be regarded as representing the execution order of the operations. Unless explicitly stated, the method does not need to be executed completely in the order shown.

[0045] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0046] like Figure 2 As shown, the method includes operations S210 to S230.

[0047] In operation S210, data is extracted from the interaction behavior log to obtain a data set.

[0048] The interaction behavior log can record the interaction behavior data of multiple different objects on multiple different contents, where the objects can be users who use the service, the content can be the content published in the service, the service can include digital media, social platforms, online advertising and content distribution networks, etc., and the content can include social dynamics, blogs, advertisements, etc.

[0049] In some embodiments, the interaction behavior log may be obtained by recording the interaction behavior in response to the interaction behavior of any object on any content. Specifically, the user may perform the interaction behavior in the client corresponding to the service, and the client sends the request corresponding to the interaction behavior to the server, and the server records the interaction behavior data corresponding to the interaction behavior in the interaction behavior log in response to receiving the request corresponding to the interaction behavior.

[0050] The interactive behavior data includes the interactive behavior type and the operation data corresponding to the interactive behavior type. Specifically, the interactive behavior type can be determined according to the control operated by the user when performing the interactive behavior, and the operation data can represent the operation performed by the user through the operation control. In addition, the interactive behavior data can also include data such as the operation time when the user performs the interactive behavior.

[0051] In some embodiments, the control may include a "like" control, a "share" control, and a "collection" control, etc. Accordingly, the interaction behavior type may include a "like" type, a "share" type, and a "collection" type, etc.

[0052] For example, when a user operates a “Like” control to perform a “Like” operation, the operation data may represent that the user has performed a “Add Like” operation; and when a user operates a “Like” control to perform a “Cancel Like” operation, the operation data may represent that the user has performed a “Cancel Like” operation.

[0053] The data set includes at least one interactive behavior data of the same object for the content to be evaluated, wherein the same object can represent the same user, the content to be evaluated can be content for interest level evaluation, and the interest level evaluation can be used to evaluate the object's interest level in the content.

[0054] Since interactive behaviors such as “like”, “collect” and “share” can represent the user’s interest in the content, the degree of interest can be evaluated through interactive behavior data.

[0055] When extracting the interaction behavior log, the interaction behavior data matching the identification of the object and the identification of the content to be evaluated in the interaction behavior log can be screened out according to the identification of the object and the identification of the content to be evaluated to obtain a data set.

[0056] In operation S220, when it is determined that there are a plurality of interaction behavior data with the same interaction behavior type in the data set, abnormal category recognition is performed on the plurality of interaction behavior data to obtain a recognition result.

[0057] When the user operates normally or the network is normal, the interaction behavior types of multiple interaction behavior data in the data set are different. Therefore, when there are multiple interaction behavior data with the same interaction behavior type in the data set, it can be characterized that there are abnormal data in the multiple interaction behavior data. Therefore, the abnormal category of the interaction behavior data can be identified to obtain an identification result.

[0058] The anomaly category can characterize the category of anomalies that cause the existence of multiple interactive behavior data with the same interactive behavior type in a data set. Specifically, the anomaly category can include statistical anomaly data in multiple interactive behavior data caused by network anomalies, and can also include invalid data in multiple interactive behavior data caused by user misoperation.

[0059] The identification result may include a sub-result indicating whether each of the plurality of interaction behavior data is abnormal data, and may also include a sub-result indicating the abnormal type to which the plurality of interaction behavior data belongs.

[0060] In some embodiments, the abnormal categories of the plurality of interactive behavior data may be identified based on the operation data and recording time of each of the plurality of interactive behavior data. Specifically, the abnormal categories of the plurality of interactive behavior data may be determined based on whether the operation data and recording time of each of the plurality of interactive behavior data are the same, thereby obtaining the identification result.

[0061] In other embodiments, a plurality of interaction behavior data may be input into a pre-trained anomaly recognition model, and the anomaly recognition model may be used to perform anomaly classification on the plurality of interaction behavior data to obtain an identification result.

[0062] In operation S230, the plurality of interaction behavior data are processed in a processing manner that matches the recognition result to obtain target interaction behavior data.

[0063] The processing method that matches the recognition result can be determined according to the abnormality category represented by the recognition result. Specifically, different processing methods can be pre-set for different abnormality categories. After obtaining the recognition result, according to the abnormality category represented by the recognition result, the processing method corresponding to the abnormality category is determined as the processing method that matches the recognition result.

[0064] The target interaction behavior data may include a plurality of interaction behavior data of different interaction behavior types. Specifically, the target interaction behavior data may include interaction behavior data obtained by processing a plurality of interaction behavior data of the same interaction behavior type, or may include a plurality of interaction behavior data of different interaction behavior categories in a data set.

[0065] In some embodiments, multiple interactive behavior data with the same interactive behavior category may be extracted and processed. Specifically, valid interactive behavior data or normal interactive behavior data may be extracted from the multiple interactive behavior data as target interactive behavior data.

[0066] In other embodiments, multiple interactive behavior data with the same interactive behavior category may be processed to eliminate abnormalities. Specifically, abnormal data in the multiple interactive behavior data may be corrected, and the corrected interactive behavior data may be determined as target interactive behavior data.

[0067] After obtaining the target interaction behavior data, the target interaction behavior data can also be stored. Specifically, in addition to setting up an interaction behavior log for storing all the user's interaction behavior data, an interaction behavior full scale for storing the target interaction behavior data can also be set up. After obtaining the target interaction behavior data, the target interaction behavior data is stored in the interaction behavior full scale.

[0068] The target interactive behavior data stored in the interactive behavior full scale may include information such as recording time, user ID, content ID, operation data, and interactive behavior type.

[0069] Since the target interaction behavior data is obtained by processing a plurality of interaction behavior data, the target interaction behavior data can more accurately represent the object's real operation intention, and further the object's interest in the content to be evaluated can be evaluated based on the target interaction behavior data.

[0070] In an embodiment of the present disclosure, abnormal categories are identified for interactive behavior data with the same interactive behavior type to obtain an identification result, and multiple interactive behavior data are processed according to a processing method that matches the identification result, thereby improving the effectiveness and accuracy of the target interactive behavior data, thereby improving the accuracy and effectiveness of the interest level assessment results obtained using the target interactive behavior data.

[0071] Reference below Figure 3~Figure 7 , taking a specific embodiment as an example, for example Figure 2 The method shown is further explained.

[0072] According to an embodiment of the present disclosure, performing abnormal category identification on multiple interactive behavior data to obtain an identification result includes: performing abnormal category identification on multiple operation data with the same interactive behavior type to obtain an identification result.

[0073] When it is determined that there are multiple interactive behavior data with the same interactive behavior type in the data set, it can be determined that the multiple interactive behavior data are abnormal. At this time, the abnormal categories of the multiple interactive behavior data can be determined according to the multiple operation data with the same interactive behavior type.

[0074] In some embodiments, the operation data of the plurality of interaction behavior data may be compared to obtain a comparison result, and then the abnormal category may be identified based on the comparison result to obtain the identification results of the plurality of interaction behavior data. Specifically, the identification results of the plurality of interaction behavior data may be determined based on whether the operation data are the same or different.

[0075] In other embodiments, the recognition result can be obtained according to the values ​​of the operation data of each of the plurality of interaction behavior data. Specifically, when the operation data of the interaction behavior data is a null value or an abnormal value, it can be determined that the interaction behavior data has an abnormal abnormal category.

[0076] In the embodiments of the present disclosure, by performing abnormal category identification based on the operation data of the interactive behavior data, the accuracy of abnormal category identification is improved, thereby improving the accuracy of subsequent abnormal processing.

[0077] According to an embodiment of the present disclosure, abnormal category identification is performed on multiple interactive behavior data with the same interactive behavior type to obtain an identification result, including: when it is determined that the same multiple operation data exist in the multiple interactive behavior data, determining that the identification result represents the existence of statistical abnormal data in the multiple interactive behavior data.

[0078] In another embodiment, performing abnormal category identification on multiple interactive behavior data of the same interactive behavior type to obtain an identification result may also include: determining network communication data of a terminal device corresponding to the object when it is determined that the multiple interactive behavior data contain the same multiple operation data. When it is determined that the network communication data represents network communication abnormality, determining that the identification result represents the presence of statistical abnormal data in the multiple interactive behavior data.

[0079] The terminal device corresponding to the object may refer to the medium or carrier through which the object accesses the content to be evaluated as the terminal device. The network communication data of the terminal device may refer to the network communication related data between the terminal device and the server.

[0080] When the network is abnormal, the same user interaction behavior may be recorded repeatedly. For example, after the user operates the "Collect" control to perform the "Collect" operation, due to the network abnormality, multiple interaction behavior data of the interaction behavior type "Collect" are recorded in the interaction behavior log. Accordingly, there are multiple interaction behavior data of the interaction behavior type "Collect" in the data set, and the operation data of the multiple interaction behavior data are the same, all of which are "Collect".

[0081] Therefore, multiple interactive behavior data with the same operation data may be generated due to network communication abnormalities. When determining the recognition results of multiple interactive behavior data with the same operation data, it can be determined that the recognition results represent the existence of statistical abnormal data in the multiple interactive behavior data when it is determined that the network communication data represent network communication abnormalities.

[0082] When it is determined that the network communication data indicates that the network communication is normal, and it is determined that the recognition result indicates that there are other problems that affect the statistics of the multiple interactive behavior data, the multiple interactive behavior data can be directly deleted without subsequent analysis operations.

[0083] Therefore, compared with the method of directly determining that the recognition result represents the existence of statistical abnormal data in multiple interactive behavior data when there are multiple identical operation data, the network communication abnormality is further determined through the network communication data. In this case, it is determined that there are statistical abnormal data in multiple interactive behavior data. Multiple factors can be used as references to improve the accuracy of determining statistical abnormal data.

[0084] According to an embodiment of the present disclosure, abnormal category identification is performed on multiple interactive behavior data with the same interactive behavior type to obtain an identification result, including: when it is determined that there are multiple different operation data in the multiple interactive behavior data, determining that the identification result indicates that invalid data exists in the multiple interactive behavior data.

[0085] In another embodiment, performing abnormal category identification on multiple interactive behavior data of the same interactive behavior type to obtain an identification result may also include: determining the recording time of each of the multiple operation data when it is determined that there are multiple different operation data in the multiple interactive behavior data. When it is determined that the maximum interval duration among the multiple recording moments is less than a preset duration threshold, determining that the identification result indicates that the multiple interactive behavior data contain invalid data.

[0086] When users are performing interactive behaviors, they may make mistakes. For example, for the same content, after operating the "Like" control to perform the "Add Like" operation, the user mistakenly operates the "Like" control to perform the "Cancel Like" operation, and then operates the "Like" control again to perform the "Add Like" operation, that is, multiple "Like" type interactive behaviors are performed. Accordingly, there are multiple interactive behavior data of the "Like" type in the data set, and the operation data of the multiple interactive behavior data are different.

[0087] Therefore, multiple interactive behavior data with different operation data may be generated due to invalid misoperation of the user. When determining the recognition result of multiple interactive behavior data with different operation data, it can be determined that the recognition result indicates that invalid data exists in the multiple interactive behavior data.

[0088] When determining the recognition results of multiple interactive behavior data with different operation data, the maximum interval duration of the user can also be combined. When the maximum interval duration is less than the preset duration threshold, it is determined that the recognition result indicates that the multiple interactive behavior data contain invalid data.

[0089] For example, the subject performs a "Like" operation on the content to be evaluated at 10:01, performs a "Cancel Like" operation at 10:02, performs a "Like" operation at 10:03, and performs a "Cancel Like" operation at 10:04. Then, among the recording times of the multiple operation data, the maximum interval duration is the operation duration between 10:04 and 10:01, which is 3 minutes.

[0090] When the preset duration threshold is 10 minutes, and the maximum interval duration of 3 minutes is less than the preset duration threshold of 10 minutes, it is determined that the recognition result indicates that the multiple interactive behavior data contain invalid data due to object misoperation.

[0091] When it is determined that there are multiple different operation data in the multiple interactive behavior data and the maximum interval duration is greater than or equal to the preset duration threshold, it is determined that the recognition result represents that the multiple interactive behavior data are normal data.

[0092] In an embodiment of the present disclosure, by determining the abnormal categories of multiple interactive behavior data based on the sameness or difference of the operation data, the accuracy of the recognition result is improved, thereby improving the accuracy of the exception processing based on the recognition result. However, compared with the method of directly determining the abnormal categories of multiple interactive behavior data based on the sameness or difference of the operation data, the method of jointly referring to other factors to determine the abnormal category can improve the recognition accuracy. In addition, for different application scenarios, different reference factors are combined. For example, for the same multiple operation data, network communication data is used as a reference factor, and for different multiple operation data, the maximum interval duration is used as a reference factor. Targeted reference factors can be used to further improve the recognition accuracy.

[0093] According to an embodiment of the present disclosure, multiple interaction behavior data are processed in a processing manner that matches the recognition result to obtain target interaction behavior data, including: when it is determined that the recognition result represents that the multiple interaction behavior data contain statistical abnormal data, normal data are extracted from the multiple interaction behavior data to obtain target interaction behavior data; when it is determined that the recognition result represents that the multiple interaction behavior data contain invalid data, valid data are extracted from the multiple interaction behavior data to obtain target interaction behavior data.

[0094] The statistical abnormal data may be obtained by counting abnormal interaction behaviors of users due to network abnormalities or server processing abnormalities. Specifically, the server may record repeated or erroneous interaction behavior data in the interaction behavior log due to network abnormalities or server processing abnormalities.

[0095] Normal data extraction can be used to extract normal data from multiple interactive behavior data. Specifically, if the statistical abnormal data is caused by repeated records of the server, the normal data in the data set and any one of the multiple statistical abnormal data can be extracted as the target interactive behavior data. If the statistical abnormal data is caused by error records of the server, the statistical abnormal data can be extracted without extraction, and only the normal data in the data set can be extracted.

[0096] For example, when multiple interactive behavior data all represent the same object's "collection" operation on the content to be evaluated, there are statistical abnormal data in the multiple interactive behavior data. In this case, normal data can be extracted from the multiple interactive behavior data.

[0097] Invalid data can be obtained by counting the interaction behaviors of user misoperations. Specifically, the interaction behaviors performed by the user misoperations can be determined as invalid interaction behaviors, and then the interaction behavior data corresponding to the invalid interaction behaviors can be determined as invalid data.

[0098] The effective data extraction can be used to extract effective data from a plurality of interaction behavior data. Specifically, the interaction behavior data representing the effective interaction behavior of the user from the plurality of interaction behavior data can be extracted to obtain the target interaction behavior data.

[0099] For example, for the same content, after operating the "Like" control to perform the "Like" operation, the user mistakenly operates the "Like" control to perform the "Cancel Like" operation, and then operates the "Like" control again to perform the "Like" operation, that is, the user has performed multiple "Like" type interaction behaviors. Accordingly, there are multiple interaction behavior data of the "Like" type in the data set. At this time, effective interaction behavior data can be extracted from the multiple "Like" type interaction behavior data, such as the interaction behavior data characterizing the user's second "Like" operation.

[0100] In the embodiments of the present disclosure, by extracting normal data when there are statistical abnormal data in multiple interactive behavior data, and extracting valid data when there are invalid data in multiple interactive behavior data, corresponding processing methods are used to process different abnormal categories, thereby improving the targetedness of abnormal processing and thus improving the effectiveness of processing.

[0101] According to an embodiment of the present disclosure, normal data extraction is performed on multiple interactive behavior data to obtain target interactive behavior data, including: performing deduplication processing on the multiple interactive behavior data to obtain the target interactive behavior data.

[0102] Since statistical anomaly data may be generated due to network anomalies and the user actually only performs one interactive behavior, when multiple interactive behavior data include statistical anomaly data with the same operation data, the multiple interactive behavior data can be deduplicated. Specifically, multiple repeated statistical anomaly data can be deduplicated, and one data among the multiple repeated statistical anomaly data is retained as the target interactive behavior data.

[0103] For example, when the network is abnormal, the client sends multiple requests corresponding to a user's "add like" operation to the server, resulting in repeated recording of multiple interactive behavior data corresponding to the "add like" operation in the interactive behavior log on the server, which in turn results in statistical abnormal data in multiple interactive behavior data. At this time, the multiple repeated statistical abnormal data caused by the network abnormality can be deduplicated, and only one interactive behavior data corresponding to the "add like" operation is retained as the target interactive behavior data.

[0104] In the embodiments of the present disclosure, by performing deduplication processing on multiple interactive behavior data with the same operation data, statistical abnormal data caused by network abnormalities can be filtered out, thereby improving the accuracy of the target interactive behavior data.

[0105] According to an embodiment of the present disclosure, effective data extraction is performed on multiple interactive behavior data to obtain target interactive behavior data, including: based on the recording time of the multiple interactive behavior data, the interactive behavior data with the shortest interval between the recording time and the current time is determined from the multiple interactive behavior data as the target interactive behavior data. The recording time can be the time when the interactive behavior data is generated, or it can be the time when the interactive behavior data is written into the interactive behavior log. The current time can be the time when effective data extraction is performed, and the interactive behavior data with the shortest interval between the recording time and the current time can represent the interactive behavior data corresponding to the user's last operation.

[0106] Since invalid data may be obtained by invalid user operations, the interactive behavior data that can represent the user's true operational intention should be the interactive behavior data corresponding to the user's last operation. Therefore, in the case where multiple interactive behavior data include invalid data that is different from the operational data, the interactive behavior data with the shortest interval between the recording time and the current time can be extracted as the target interactive behavior data.

[0107] For example, among the interactive behavior data A, interactive behavior data B and interactive behavior data C generated in chronological order, interactive behavior data A represents the object's "add like" operation on the content to be evaluated, interactive behavior data B represents the object's "cancel like" operation on the content to be evaluated, and interactive behavior data C represents the object's "add like" operation on the content to be evaluated. It can be understood that the user's first "add like" operation and "cancel like" operation are both invalid interactive behaviors, and the second "add like" operation is a valid interactive behavior. Interactive behavior data A and interactive behavior data B are both invalid data, and interactive behavior data C is valid interactive data. Therefore, interactive behavior data C can be extracted from interactive behavior data A, interactive behavior data B and interactive behavior data C as the target interactive behavior data.

[0108] For another example, in the interactive behavior data A and interactive behavior data B generated in chronological order, the interactive behavior data A represents the object's "add like" operation on the content to be evaluated, and the interactive behavior data B represents the object's "cancel like" operation on the content to be evaluated. It can be understood that the user's "add like" operation is an invalid interactive behavior, and the user's original intention is not to perform a "add like" operation on the content to be evaluated. At this time, the interactive behavior data B is extracted as the target interactive behavior data. In this case, the target interactive behavior data can be not stored to reduce the amount of data for subsequent interest level analysis.

[0109] In an embodiment of the present disclosure, invalid data representing invalid operations of users in a plurality of interactive behavior data are filtered according to the recording time of the interactive behavior data, so as to improve the accuracy of the target interactive behavior data.

[0110] Figure 3 A schematic diagram of determining target interaction behavior data according to a specific embodiment of the present disclosure is schematically shown.

[0111] like Figure 3 As shown, the multiple interaction behavior data 310 include three interaction behavior data of user 1 for content 1, all of which are of the "like" type and have different operation data. Specifically, the recording time of interaction behavior data 311 is 12 / 1, and the operation data is "add", the recording time of interaction behavior data 312 is 12 / 1, and the operation data is "cancel", and the recording time of interaction behavior data 313 is 12 / 2, and the operation data is "add". The multiple interaction behavior data can be extracted to determine that the third interaction behavior data with the shortest interval between the recording time and the current time is the target interaction behavior data 320.

[0112] According to another embodiment of the present disclosure, effective data extraction is performed on multiple interactive behavior data to obtain target interactive behavior data, including: based on the recording moments of the multiple interactive behavior data, determining the initial target interactive behavior data with the shortest interval between the recording moment and the current moment from the multiple interactive behavior data; when it is determined that the initial target interactive behavior data represents positive behavior data and the object's access time to the content to be evaluated is greater than a preset access time threshold, the initial target interactive behavior data is used as the target interactive behavior data. However, it is not limited to this. It is also possible to use the initial target interactive behavior data as the target interactive behavior data when it is determined that the initial target interactive behavior data represents positive behavior data or the object represents a access time of the content to be evaluated that is greater than a preset access time threshold.

[0113] Positive behavior data can be understood as behavior data that characterizes the subject's interest in the content to be evaluated. For example, behavior data such as "like", "collect", or "share" can be called positive behavior data. Behavior data such as "cancel like", "cancel collection", "cancel share", or "bad review" can be called negative behavior data.

[0114] In general scenarios, the target interaction behavior data is used to evaluate the object's interest in the content to be evaluated. Therefore, after determining the emotional state represented by the initial target interaction behavior data, such as whether it represents positive behavior data, the object's access time is also counted to reflect the object's interest in the content to be evaluated from different angles, thereby improving the statistical accuracy of the target interaction behavior data.

[0115] In the case where it is determined that the initial target interaction behavior data represents negative behavior data, the initial target interaction behavior data may be directly used as the target interaction behavior data.

[0116] According to the recording time of the interactive behavior data, invalid data representing invalid user operations in multiple interactive behavior data are filtered out. In addition, for the initial target interactive behavior data representing the object's interest in the content to be evaluated, the validity of the initial target interactive behavior data is further judged through the object's access duration, thereby improving the accuracy of the target interactive behavior data.

[0117] According to an embodiment of the present disclosure, the data processing method also includes: obtaining target operation data corresponding to each interaction behavior type based on the target interaction behavior data of each of the different multiple objects; performing data statistical processing on the target operation data to obtain operation data statistical results; and obtaining evaluation results for the content to be evaluated based on the operation data statistical results.

[0118] According to an embodiment of the present disclosure, when evaluating the interest level of the content to be evaluated, the interest levels of multiple different objects in the content to be evaluated may be determined based on the target interaction behavior data of each of the multiple different objects.

[0119] In some embodiments, the different multiple objects may include at least one object that interacts with the content to be evaluated, such as a user that interacts with the content to be evaluated by liking, collecting, sharing, etc. Specifically, one object may correspond to multiple target interaction behavior data with different interaction behavior types.

[0120] In other embodiments, the different multiple objects may also be multiple objects in a group with similar characteristics, such as multiple users interested in news. It is understandable that the object in this embodiment may not have performed any interactive behavior with respect to the content to be evaluated, and the target interactive behavior data corresponding to the object may be empty.

[0121] The target operation data may represent the number of target interaction behavior data corresponding to the interaction behavior. Specifically, the multiple target interaction behavior data may be divided into multiple data sets according to their respective interaction behavior types, such as dividing the multiple target interaction behavior data into a first data set representing a "like" operation, a second data set representing a "collection" operation, and a third data set representing a "share" operation, etc., and the target operation data corresponding to each interaction behavior type may be determined according to the number of target interaction behavior data included in each data set.

[0122] Label values ​​can be used to represent operation data in the target interactive behavior data. Specifically, for the interactive behavior type of "like", the label value corresponding to the "add like" operation can be set to 1, and the label value corresponding to the "cancel like" operation can be set to 0. When determining the target operation data, the label values ​​in the target interactive behavior data can be directly summed to obtain the target operation data.

[0123] By performing data statistics on the target operation data, the operation data statistics results representing the degree of attention received by the content to be evaluated or the degree of interest of the object can be determined. Specifically, the process of performing data statistics can be performed offline.

[0124] The target operation data may be stored in the interactive behavior statistics table. When new target behavior data is subsequently generated, the target operation data in the interactive behavior statistics table may be updated using the new target interactive behavior data.

[0125] In some embodiments, when the different multiple objects include at least one object that interacts with the content to be evaluated, different weights can be set for different types of interactive behaviors. When data processing statistics are performed on the target operation data, the target operation data of each interactive behavior type are weighted and summed according to the respective weights of each interactive behavior type to obtain the operation data statistical results. At this time, the operation data statistical results can represent the degree of attention received by the content to be evaluated.

[0126] For example, the weight corresponding to the "like" interaction behavior type is 1, and the target operation data is 10; the weight corresponding to the "collection" interaction behavior type is 2, and the target operation data is 5; the weight corresponding to the "share" interaction behavior type is 3, and the target operation data is 1. The operation data statistics result can be 1×10+2×5+3×1=23.

[0127] In other embodiments, when the different multiple objects are multiple objects in a group with similar characteristics, the percentages of interactive behaviors of different types with respect to the content to be evaluated among the different multiple objects can be determined based on the number of objects and the target operation data, and the operation data statistics can be determined based on the percentages of each of the multiple interaction behavior types. At this time, the operation data statistics can characterize the degree of interest of the group in the content to be evaluated.

[0128] For example, the target operation data corresponding to the "Like" interaction behavior type is 8, the target operation data corresponding to the "Collect" interaction behavior type is 3, and the target operation data corresponding to the "Share" interaction behavior type is 1. The number of objects of multiple objects is 10. The percentage corresponding to the "Like" interaction behavior type is 0.8, the percentage corresponding to the "Collect" interaction behavior type is 0.3, and the percentage corresponding to the "Share" interaction behavior type is 0.1. It can be determined that the average of the three percentages is the operation data statistical result, that is, the operation data statistical result is 0.4.

[0129] The evaluation result of the content to be evaluated can be determined according to the value of the statistical result of the operation data. Specifically, a threshold value for determining the degree of attention or interest can be set. When the statistical result of the operation data exceeds the threshold value, it is determined that the evaluation result indicates that the content to be evaluated has a high degree of attention, or that the evaluation result indicates that the group has a high degree of interest in the content to be evaluated.

[0130] In the embodiments of the present disclosure, by performing data statistics processing on the target interaction data and evaluating the content to be evaluated based on the statistical results of the operation data obtained, quantitative indicators can be reasonably used to achieve quantitative analysis of the content to be evaluated, thereby improving the effectiveness and clarity of the analysis.

[0131] Figure 4A schematic diagram of generating evaluation results according to a specific embodiment of the present disclosure is schematically shown.

[0132] like Figure 4 As shown, the different multiple objects may include user 1 , user 2 , and user 3 .

[0133] like Figure 4 As shown, the target interaction behavior data corresponding to user 1 includes target interaction behavior data 411_1 representing the "like" interaction behavior type, and target interaction behavior data 411_2 representing the "collection" interaction behavior type. The target interaction behavior data corresponding to user 2 includes target interaction behavior data 412_1 representing the "like" interaction behavior type, and target interaction behavior data 412_2 representing the "share" interaction behavior type. The target interaction behavior data corresponding to user 3 includes target interaction behavior data 413_1 representing the "like" interaction behavior type, and target interaction behavior data 413_2 representing the "share" interaction behavior type.

[0134] like Figure 4 As shown, according to the target interaction behavior data 411_2, the target operation data 421 corresponding to the interaction behavior type of "collection" can be obtained. According to the target interaction behavior data 411_1, the target interaction behavior data 412_1 and the target interaction behavior data 413_1, the target operation data 422 corresponding to the interaction behavior type of "like" can be obtained. According to the target interaction behavior data 412_2 and the target interaction behavior data 413_2, the target interaction data 423 corresponding to the interaction behavior type of "share" can be obtained.

[0135] like Figure 4 As shown, data statistics may be performed on target operation data 421 , target operation data 422 , and target operation data 423 to obtain operation data statistical result 430 , and then evaluation result 440 may be obtained based on operation data statistical result 430 .

[0136] Figure 5 The figure schematically shows a data statistics process according to a specific embodiment of the present disclosure.

[0137] like Figure 5 As shown, the multiple target interaction behavior data 510 for content 1 and the interaction behavior type of "Like" include target interaction behavior data 511 with the recording time of 12 / 1, the user identification of user 1, and the operation data of cancel; target interaction behavior data 512 with the recording time of 12 / 1, the user identification of user 2, and the operation data of cancel; and target interaction behavior data 513 with the recording time of 12 / 2, the user identification of user 3, and the operation data of add.

[0138] like Figure 5As shown, after determining the label values ​​of multiple target interaction data 510, multiple data 520 are obtained, and the multiple data 520 include data 521 with a recording time of 12 / 1, an operation data of cancel, and a label value of 0, data 522 with a recording time of 12 / 1, an operation data of cancel, and a label value of 0, and data 523 with a recording time of 12 / 2, an operation data of add, and a label value of 1.

[0139] like Figure 5 As shown, after data statistics are performed on the label values ​​of multiple data 520, an operation data statistical result 530 is obtained. The operation data statistical result 530 represents that the number of likes for content 1 is 1. Specifically, the number of likes in the operation data statistical result 530 is obtained by summing up the label values ​​in the multiple data 530.

[0140] According to an embodiment of the present disclosure, the data processing method also includes: in response to receiving new interaction behavior data of the object for the content to be evaluated, determining the interaction behavior type of the new interaction behavior data; and when the interaction behavior type is the same as the interaction behavior type of the target interaction behavior data, updating the target interaction behavior data using the new interaction behavior data.

[0141] New interactive behavior data is generated after obtaining the data set. Specifically, a time period can be predetermined, and the interactive behavior data generated in the time period can be determined as new interactive behavior data every other time period, or the interactive behavior data corresponding to the interactive operation can be directly determined as new interactive behavior data in response to the user's interactive operation.

[0142] After obtaining the data set, the subject may still perform new interactive behaviors on the content to be evaluated. At this time, the interactive behavior full scale for storing the target interactive behavior data can be updated according to the new interactive behavior data corresponding to the new interactive behavior.

[0143] Before using the new interaction behavior data to update the full interaction behavior scale, multiple interaction behavior data with the same interaction behavior type in the new interaction behavior data may also be processed, such as normal data extraction or valid data extraction.

[0144] When the interaction behavior type of the new interaction behavior data is the same as the interaction behavior type of the target interaction behavior data, the target interaction behavior data needs to be updated with the new interaction behavior data. Specifically, the new interaction behavior data can be directly used to replace the original target interaction behavior data to obtain new target interaction data.

[0145] For example, when obtaining a data set, the target interactive behavior data representation object performs a "like" operation on the content to be evaluated, and the new interactive behavior data representation object performs a "cancel like" operation on the content to be evaluated. At this time, the new interactive behavior data can be used to update the target interactive behavior data.

[0146] When the interaction behavior type of the new interaction behavior data is different from the interaction behavior type of the target interaction behavior data, since the interaction behavior represented by the new interaction behavior data is different from the interaction behavior represented by the target interaction behavior data, the new interaction behavior data can be stored while retaining the target interaction behavior data.

[0147] In an embodiment of the present disclosure, after a new interactive behavior occurs, the existing target interactive behavior data is updated by utilizing the new interactive behavior data, thereby improving the accuracy and timeliness of the target interactive behavior data, and there is no need to re-count the target interactive behavior data in the interactive behavior log, thereby improving the efficiency of generating the target interactive behavior data.

[0148] Figure 6 A schematic diagram of updating target interaction behavior data according to an embodiment of the present disclosure is schematically shown.

[0149] like Figure 6 As shown, the multiple target interaction behavior data 610 for content 1 include target interaction behavior data 611 with the recording time of 12 / 1, the user identifier of user 1, the operation data of cancel, and the interaction behavior type of like, and target interaction behavior data 612 with the recording time of 12 / 1, the user identifier of user 2, the operation data of add, and the interaction behavior type of like.

[0150] like Figure 6 As shown, the multiple new interaction behavior data 620 for content 1 include new interaction behavior data 621 with a recording time of 12 / 2, a user identifier of user 2, an operation data of cancel, and an interaction behavior type of like, and new interaction behavior data 622 with a recording time of 12 / 2, a user identifier of user 3, an operation data of add, and an interaction behavior type of like.

[0151] like Figure 6As shown, when multiple target interaction behavior data 610 are updated using multiple new interaction behavior data 620, since the interaction behavior type of the new interaction behavior data 621 is the same as that of the target interaction behavior data 612, the new interaction behavior data 621 is used to update the target interaction behavior data 612, and the new interaction behavior data 622 is stored to obtain the updated multiple target interaction behavior data 630, which include the target interaction behavior data 631 with a recording time of 12 / 1, a user identifier of user 1, an operation data of cancel, and an interaction behavior type of like; new interaction behavior data 632 with a recording time of 12 / 2, a user identifier of user 2, an operation data of cancel, and an interaction behavior type of like; and new interaction behavior data 633 with a recording time of 12 / 2, a user identifier of user 3, an operation data of add, and an interaction behavior type of like.

[0152] According to an embodiment of the present disclosure, updating the target interactive behavior data with new interactive behavior data may include: determining the network communication data of the terminal device corresponding to the object and the access duration for the content to be evaluated when it is determined that the new interactive behavior data represents positive behavioral data. When it is determined that the network communication data represents normal network communication and the access duration is greater than a preset access duration threshold, updating the target interactive behavior data with the new interactive behavior data. When it is determined that the new interactive behavior data represents negative behavioral data, updating the target interactive behavior data with the new interactive behavior data.

[0153] In the case where the new interactive behavior data represents positive behavioral data, it means that the new interactive behavior data has a certain statistical significance for the evaluation of the interest level of the subsequent content to be evaluated. Then, other reference factors are used to evaluate the new interactive behavior data to determine whether the new interactive behavior data meets the statistical conditions. If the statistical conditions are met, the new interactive behavior data is used to update the target interactive behavior data. If not, the new interactive behavior data is abandoned.

[0154] The statistical condition can be determined as follows: the network communication data indicates that the network communication is normal and the access time is greater than a preset access time threshold. If the network communication data indicates that the network communication is normal, it means that there is no statistical anomaly problem caused by network anomalies. If the access time is greater than the preset access time threshold, the interest of the object in the content to be evaluated is considered from another interactive factor.

[0155] If the network communication data indicates that the network communication is abnormal or the access duration is less than or equal to the preset access duration threshold, the new interactive behavior data can be abandoned, and the target interactive behavior data is still used as the subsequent analysis data.

[0156] As a result, the updating standard of new interactive behavior data is improved, thereby improving the evaluation accuracy for analyzing the content to be evaluated.

[0157] When it is determined that the new interactive behavior data represents negative behavior data, the target interactive behavior data is updated using the new interactive behavior data.

[0158] Combine data screening with subsequent analysis scenarios to improve screening accuracy and requirements for positive behavior data, and lower screening requirements for negative behavior data. Different screening criteria can be used to ensure analysis accuracy and effectiveness while improving processing efficiency.

[0159] Figure 7 The flowchart of the content evaluation method according to the embodiment of the present disclosure is schematically shown.

[0160] like Figure 7 As shown, the method includes operation S710.

[0161] In operation S710, the content to be evaluated is evaluated based on the target interactive behavior data to obtain an evaluation result for representing the degree of interest.

[0162] According to an embodiment of the present disclosure, the target interaction behavior data is obtained by using a data processing method. Since the target interaction behavior data can characterize the object's interaction behavior with respect to the content to be evaluated, and the interaction behavior can characterize the object's interest in the content to be evaluated to a certain extent, an evaluation result characterizing the interest degree can be obtained based on the target interaction behavior data.

[0163] In some embodiments, since different interaction behavior types can represent different levels of interest of an object in the content to be evaluated, the content to be evaluated can be evaluated according to the interaction behavior type of the target interaction behavior data. Specifically, the interest level evaluation scores corresponding to different interaction behavior types can be pre-set, such as the interest level evaluation score when the interaction behavior type is "like" is 1, the interest level evaluation score when the interaction behavior type is "collection" is 2, and the interest level evaluation score when the interaction behavior type is "share" is 3. When there are multiple target interaction behavior data, the interest evaluation scores corresponding to each target interaction behavior data can be added to obtain an evaluation result.

[0164] In the embodiments of the present disclosure, by evaluating the interest level according to the target interaction behavior data, the obtained evaluation result is more accurate.

[0165] In the Internet era, content interaction has become an important indicator for measuring user engagement, content quality, and platform activity. Especially in the fields of digital media, online advertising, social networking platforms, and content distribution networks, it is extremely critical to accurately obtain and analyze user interactions with content, including but not limited to final statistics of likes, comments, favorites, or shares. These data are important bases for content creators, advertisers, and platform operators to understand content performance, optimize content strategies, and improve advertising effectiveness. The embodiments of the present disclosure provide a data processing method to make the capture and analysis of final interactive data more accurate, thereby helping stakeholders optimize their content and strategies. The present invention is applicable to various digital content management systems, especially those systems that require feedback on interactive data to drive content recommendations and advertising optimization decisions.

[0166] Accurate target interaction behavior data and evaluation results can improve user experience. Optimizing user experience is the key to improving product attractiveness and user retention. Providing accurate target interaction behavior data and evaluation results can help platform operators and content creators gain a deeper understanding of user behavior and preferences. For example, in content creation activities, by analyzing user interactions with content, such as likes, favorites, or comments, direct indicators of user interest in content can be obtained, thereby optimizing the way and content of content display and improving user satisfaction.

[0167] In addition, accurate target interaction behavior data and evaluation results can also optimize resource allocation. For content platforms, understanding the types of content that are more attractive to users can help the operation team allocate resources more effectively, such as promotion budgets and creative energy. By providing accurate target interaction behavior data and evaluation results, the operation team can be guided to focus resources on content types with high user engagement and satisfaction, thereby improving overall operational efficiency.

[0168] In summary, by improving the accuracy of target interaction behavior data and evaluation results, it helps platform operators analyze and optimize user experience and resource allocation, ultimately helping the platform gain an advantage and providing a powerful tool for the operation and management of digital products.

[0169] Figure 8 The block diagram schematically shows a data processing device according to an embodiment of the present disclosure.

[0170] like Figure 8 As shown, the data processing device 800 includes an extraction module 810 , an identification module 820 and a processing module 830 .

[0171] The extraction module 810 is used to extract data from the interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for the content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type.

[0172] The identification module 820 is used to identify abnormal categories of the multiple interactive behavior data when it is determined that there are multiple interactive behavior data with the same interactive behavior type in the data set, and obtain an identification result.

[0173] The processing module 830 is used to process the multiple interactive behavior data in a processing manner that matches the recognition result to obtain target interactive behavior data.

[0174] According to an embodiment of the present disclosure, the processing module 830 includes a first extraction submodule and a second extraction submodule.

[0175] The first extraction submodule is used to extract normal data from the multiple interactive behavior data to obtain target interactive behavior data when the recognition result indicates that the multiple interactive behavior data have statistical abnormal data.

[0176] The second extraction submodule is used to extract valid data from the multiple interactive behavior data to obtain target interactive behavior data when it is determined that the recognition result indicates that the multiple interactive behavior data contain invalid data.

[0177] According to an embodiment of the present disclosure, the first extraction submodule includes a deduplication unit.

[0178] The deduplication unit is used to perform deduplication processing on multiple interactive behavior data to obtain target interactive behavior data.

[0179] According to an embodiment of the present disclosure, the second extraction submodule includes an initial determination unit and a target determination unit.

[0180] An initial determination unit, configured to determine, based on the recording times of the plurality of interaction behavior data, initial target interaction behavior data having the shortest interval between the recording time and the current time from the plurality of interaction behavior data;

[0181] The target determination unit is used to use the initial target interaction behavior data as the target interaction behavior data when it is determined that the initial target interaction behavior data represents positive behavior data and the access time of the object to the content to be evaluated is greater than a preset access time threshold.

[0182] According to an embodiment of the present disclosure, the identification module 820 includes a network determination submodule and an abnormality determination submodule.

[0183] a network determination submodule, configured to determine network communication data of a terminal device corresponding to the object when it is determined that the same plurality of operation data exist in the plurality of interaction behavior data; and

[0184] The abnormality determination submodule is used to determine that the recognition result indicates that there are statistical abnormal data in the plurality of interactive behavior data when it is determined that the network communication data indicates network communication abnormality.

[0185] According to an embodiment of the present disclosure, the identification module includes a time determination submodule and an invalidity determination submodule.

[0186] The time determination submodule is used to determine the recording time of each of the plurality of operation data when it is determined that the plurality of interaction behavior data have the same plurality of operation data.

[0187] The invalid determination submodule is used to determine that the recognition result indicates that invalid data exists in the plurality of interactive behavior data when it is determined that the maximum interval duration among the plurality of recording moments is less than a preset duration threshold.

[0188] According to an embodiment of the present disclosure, the data processing device 800 further includes a target determination module, an operation statistics module and a content evaluation module.

[0189] The target determination module is used to obtain target operation data corresponding to each interaction behavior type based on the target interaction behavior data of different multiple objects.

[0190] The operation statistics module is used to perform data statistics processing on the target operation data to obtain operation data statistics results.

[0191] The content evaluation module is used to obtain evaluation results for the content to be evaluated based on the statistical results of the operation data.

[0192] According to an embodiment of the present disclosure, the data processing device 800 further includes a type determination module and a data update module.

[0193] The type determination module is used to determine the interaction behavior type of the new interaction behavior data in response to receiving the new interaction behavior data of the object for the content to be evaluated, wherein the new interaction behavior data is generated after the data set is obtained.

[0194] The data updating module is used to update the target interactive behavior data with the new interactive behavior data when the interactive behavior type is the same as the interactive behavior type of the target interactive behavior data.

[0195] According to an embodiment of the present disclosure, the data updating module includes a duration determining submodule, a first updating submodule and a second updating submodule.

[0196] A duration determination submodule, for determining network communication data of a terminal device corresponding to the object and a duration of access to the content to be evaluated when it is determined that the new interactive behavior data represents positive behavior data;

[0197] A first updating submodule is used to update the target interactive behavior data with new interactive behavior data when it is determined that the network communication data indicates that the network communication is normal and the access time is greater than a preset access time threshold; and

[0198] The second updating submodule is used to update the target interactive behavior data using the new interactive behavior data when it is determined that the new interactive behavior data represents negative behavior data.

[0199] Fig. 9 The block diagram of the content evaluation device according to the embodiment of the present disclosure is schematically shown.

[0200] like Fig. 9 As shown, the content evaluation device 900 includes an interest evaluation module 910 .

[0201] The interest evaluation module 910 is used to evaluate the content to be evaluated based on the target interaction behavior data to obtain an evaluation result for characterizing the degree of interest; wherein the target interaction behavior data is obtained by using the data processing device 800 .

[0202] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0203] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method as in the embodiment of the present disclosure.

[0204] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute a method as described in an embodiment of the present disclosure.

[0205] According to an embodiment of the present disclosure, a computer program product includes a computer program, and when the computer program is executed by a processor, the method according to the embodiment of the present disclosure is implemented.

[0206] Fig.10A schematic block diagram of an example electronic device 1000 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0207] like Fig.10 As shown, the device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0208] A number of components in the device 1000 are connected to an input / output (I / O) interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0209] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as data processing methods and content evaluation methods. For example, in some embodiments, the data processing method and the content evaluation method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the data processing method and the content evaluation method described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the data processing method and the content evaluation method in any other appropriate manner (eg, by means of firmware).

[0210] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0211] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0212] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0213] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0214] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0215] A computer system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises through computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server combined with a blockchain.

[0216] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0217] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A data processing method, comprising: Extracting data from the interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for the content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type; When it is determined that there are a plurality of interactive behavior data with the same interactive behavior type in the data set, performing abnormal category identification on the plurality of interactive behavior data to obtain an identification result; and The plurality of interaction behavior data are processed in a processing manner matching the recognition result to obtain target interaction behavior data.

2. The method according to claim 1, wherein: The processing of the plurality of interaction behavior data in a processing manner matching the recognition result to obtain target interaction behavior data includes: When it is determined that the recognition result indicates that the plurality of interaction behavior data have statistical abnormal data, normal data is extracted from the plurality of interaction behavior data to obtain the target interaction behavior data; When it is determined that the recognition result indicates that invalid data exists in the plurality of interaction behavior data, valid data is extracted from the plurality of interaction behavior data to obtain the target interaction behavior data.

3. The method according to claim 2, wherein: The step of performing normal data extraction on the plurality of interaction behavior data to obtain the target interaction behavior data includes: Deduplication processing is performed on the plurality of interaction behavior data to obtain the target interaction behavior data.

4. The method according to claim 2, wherein: The extracting effective data from the plurality of interaction behavior data to obtain the target interaction behavior data includes: Based on the recording times of the plurality of interaction behavior data, determining the initial target interaction behavior data having the shortest interval between the recording time and the current time from the plurality of interaction behavior data; When it is determined that the initial target interaction behavior data represents positive behavior data and the access time of the object to the content to be evaluated is greater than a preset access time threshold, the initial target interaction behavior data is used as the target interaction behavior data.

5. The method according to any one of claims 1 to 4, wherein: The abnormal category identification is performed on the plurality of interaction behavior data to obtain the identification result, including: In the case where it is determined that the same plurality of operation data exist in the plurality of interaction behavior data, determining network communication data of a terminal device corresponding to the object; and In the case where it is determined that the network communication data represents network communication anomaly, it is determined that the recognition result represents that statistical abnormal data exists in the plurality of interaction behavior data.

6. The method according to any one of claims 1 to 5, wherein: The performing abnormal category identification on the multiple interactive behavior data with the same interactive behavior type to obtain the identification result includes: In the case where it is determined that there are different multiple operation data in the multiple interaction behavior data, determining the recording time of each of the multiple operation data; and When it is determined that the maximum interval duration among the plurality of recording moments is less than a preset duration threshold, it is determined that the recognition result indicates that invalid data exists in the plurality of interaction behavior data.

7. The method according to any one of claims 1 to 6, further comprising: Based on the target interaction behavior data of each of the different objects, obtaining target operation data corresponding to each interaction behavior type; Performing data statistical processing on the target operation data to obtain operation data statistical results; as well as Based on the statistical result of the operation data, an evaluation result for the content to be evaluated is obtained.

8. The method according to any one of claims 1 to 7, further comprising: In response to receiving new interaction behavior data of the object for the content to be evaluated, determining an interaction behavior type of the new interaction behavior data, wherein the new interaction behavior data is generated after obtaining the data set; and In a case where the interaction behavior type is the same as the interaction behavior type of the target interaction behavior data, the target interaction behavior data is updated using the new interaction behavior data.

9. The method according to claim 8, wherein: The updating of the target interactive behavior data by using the new interactive behavior data includes: In the case where it is determined that the new interactive behavior data represents positive behavior data, determining network communication data of a terminal device corresponding to the object and a duration of access to the content to be evaluated; When it is determined that the network communication data indicates that the network communication is normal and the access duration is greater than a preset access duration threshold, updating the target interaction behavior data using the new interaction behavior data; and In the case where it is determined that the new interactive behavior data represents negative behavior data, the target interactive behavior data is updated using the new interactive behavior data.

10. A content evaluation method, comprising: Evaluate the content to be evaluated based on the target interaction behavior data to obtain an evaluation result used to characterize the degree of interest; Wherein, the target interaction behavior data is obtained by using the data processing method as described in any one of claims 1 to 9.

11. A data processing device, comprising: An extraction module is used to extract data from the interaction behavior log to obtain a data set, wherein the data set includes at least one interaction behavior data of the same object for the content to be evaluated, and the interaction behavior data includes an interaction behavior type and operation data corresponding to the interaction behavior type; an identification module, configured to, when it is determined that there are a plurality of interaction behavior data with the same interaction behavior type in the data set, identify abnormal categories of the plurality of interaction behavior data to obtain an identification result; and A processing module is used to process the plurality of interaction behavior data in a processing manner matching the recognition result to obtain target interaction behavior data.

12. The device according to claim 11, wherein The processing module comprises: A first extraction submodule is used for extracting normal data from the plurality of interaction behavior data to obtain the target interaction behavior data when it is determined that the recognition result indicates that the plurality of interaction behavior data have statistical abnormal data; The second extraction submodule is configured to extract valid data from the plurality of interaction behavior data to obtain the target interaction behavior data when it is determined that the recognition result indicates that the plurality of interaction behavior data contain invalid data.

13. The device according to claim 12, wherein: The first extraction submodule comprises: The deduplication unit is used to perform deduplication processing on the plurality of interaction behavior data to obtain the target interaction behavior data.

14. The device according to claim 12, wherein: The second extraction submodule includes: An initial determination unit, configured to determine, based on the recording times of the plurality of interaction behavior data, initial target interaction behavior data having the shortest interval between the recording time and the current time from the plurality of interaction behavior data; The target determination unit is used to use the initial target interaction behavior data as the target interaction behavior data when it is determined that the initial target interaction behavior data represents positive behavior data and the access time of the object to the content to be evaluated is greater than a preset access time threshold.

15. The device according to any one of claims 11 to 14, wherein: The identification module comprises: a network determination submodule, configured to determine network communication data of a terminal device corresponding to the object when it is determined that the same plurality of operation data exist in the plurality of interaction behavior data; and The abnormality determination submodule is used to determine that the recognition result indicates that there are statistical abnormal data in the plurality of interactive behavior data when it is determined that the network communication data indicates network communication abnormality.

16. The device according to any one of claims 11 to 15, wherein: The identification module comprises: a time determination submodule, configured to determine the recording time of each of the plurality of operation data when it is determined that the plurality of interaction behavior data contain the same plurality of operation data; and The invalid determination submodule is used to determine that the recognition result represents that invalid data exists in the plurality of interactive behavior data when it is determined that the maximum interval duration among the plurality of recording moments is less than a preset duration threshold.

17. The device according to any one of claims 11 to 16, wherein: The data processing device further comprises: A target determination module, used to obtain target operation data corresponding to each interaction behavior type based on target interaction behavior data of different multiple objects; an operation statistics module, used to perform data statistics processing on the target operation data to obtain operation data statistics results; and The content evaluation module is used to obtain an evaluation result for the content to be evaluated based on the statistical result of the operation data.

18. The device according to any one of claims 11 to 17, wherein: The data processing device further comprises: a type determination module, configured to, in response to receiving new interaction behavior data of the object for the content to be evaluated, determine the interaction behavior type of the new interaction behavior data, wherein the new interaction behavior data is generated after obtaining the data set; and A data updating module is used to update the target interactive behavior data using the new interactive behavior data when the interactive behavior type is the same as the interactive behavior type of the target interactive behavior data.

19. The device according to claim 18, wherein: The data updating module comprises: A duration determination submodule, for determining network communication data of a terminal device corresponding to the object and a duration of access to the content to be evaluated when it is determined that the new interactive behavior data represents positive behavior data; A first updating submodule is configured to update the target interactive behavior data using the new interactive behavior data when it is determined that the network communication data indicates that the network communication is normal and the access duration is greater than a preset access duration threshold; and The second updating submodule is configured to update the target interactive behavior data using the new interactive behavior data when it is determined that the new interactive behavior data represents negative behavior data.

20. A content evaluation device, comprising: An interest evaluation module is used to evaluate the content to be evaluated based on the target interaction behavior data to obtain an evaluation result used to characterize the degree of interest; Wherein, the target interaction behavior data is obtained using a data processing device as described in any one of claims 11 to 19.

21. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 10.

23. 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 10.

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