Methods, apparatus, devices, and computer-readable media for generating user recommendation information

Abstract

This disclosure provides a method, apparatus, device, and computer-readable medium for generating user recommendation information. One specific implementation of the method includes: receiving task publishing parameter information; acquiring user information corresponding to each task type as a user recommendation information group; generating recommendation values ​​based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the task-accepting user identifier included in the user recommendation information corresponding to the publishing user account identifier, thus obtaining a recommendation value group; and sorting each user recommendation information in the user recommendation information group based on the recommendation value group, the task value, and the value range of each accepted task included in the user recommendation information group. This implementation improves user experience and thus reduces user traffic loss.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to a method, apparatus, device, and computer-readable medium for generating user recommendation information. Background Technology

[0002] With the development of computer technology and e-commerce, task (item promotion / content creation) publishing platforms can provide a means for information exchange and task collaboration between task publishers and task recipients. Currently, when determining the user information of task recipients that can be screened by task publishers, the common methods are: combining the user information of task recipients who have requested to accept tasks published by the task publisher, or combining the user information of task recipients who have previously collaborated with the task publisher.

[0003] However, when using the above method to determine the user information of task accepters that can be filtered by task publishers, the following technical problems often arise: When there is a large amount of user information for task accepters, task publishers need to filter through this large pool of information, which can easily lead to omissions of key metrics (task type) of the selected users, resulting in low efficiency and a poor user experience; when there is a small amount of user information for task accepters, the diversity of available users is poor, further contributing to a poor user experience; and the inability to filter user information based on the historical price range of tasks accepted by task accepters results in a low match between the selected users and the task publishers, leading to a poor user experience and ultimately causing a loss of user traffic. Summary of the Invention

[0004] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] Some embodiments of this disclosure provide methods, apparatus, devices, and computer-readable media for generating user recommendation information to address one or more of the technical problems mentioned in the background section above.

[0006] In a first aspect, some embodiments of this disclosure provide a method for generating user recommendation information. The method includes: receiving task posting parameter information from a task posting user account, wherein the task posting parameter information includes a posting user account identifier, task value, and a task type group; obtaining user information corresponding to each task type in the task type group as a user recommendation information group, wherein the user recommendation information in the user recommendation information group includes a task accepting user identifier, a user rating value, a task type rating value group, and a task acceptance value range, and the task type in each task type corresponds to the maximum task type rating value in the task type rating value group included in the corresponding user recommendation information; generating recommendation values ​​based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the posting user account identifier corresponding to the task accepting user identifier included in the user recommendation information, thus obtaining a recommendation value group; and sorting each user recommendation information in the user recommendation information group based on the recommendation value group, the task value, and the task acceptance value ranges included in the user recommendation information group, thus obtaining a user recommendation information sequence.

[0007] Optionally, the aforementioned user recommendation information may also include task category rating groups.

[0008] Optionally, after obtaining the user information corresponding to each task type in the task type group as a user recommendation information group, the method further includes: in response to the task release parameter information including a task category group, obtaining the user information corresponding to the maximum accepted task category rating value in the accepted task category rating value group and the task category in the task category group as a first user recommendation information group; adding each first user recommendation information in the first user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0009] Optionally, the aforementioned user recommendation information may also include a group of task output identifier rating values.

[0010] Optionally, after adding each of the first user recommendation information in the first user recommendation information group to the user recommendation information group, the method further includes: in response to the task output identifier group being published, obtaining each user information corresponding to the maximum accepted task output identifier score in the accepted task output identifier score value group and the task output identifier in the task output identifier group as a second user recommendation information group; adding each of the second user recommendation information in the second user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0011] Optionally, before generating the recommendation value, the method further includes: deduplicating the user recommendation information group to update the user recommendation information group.

[0012] Optionally, generating the recommendation value includes: generating the recommendation value based on the user rating value, the maximum task type rating value, the rating value, the maximum accepted task category rating value in the accepted task category rating value group included in the user recommendation information, and the maximum accepted task output identifier rating value in the accepted task output identifier rating value group included in the user recommendation information.

[0013] Optionally, before the above-mentioned response to the task release parameter information including task category group, obtaining the maximum accepted task category rating value in the included accepted task category rating value group and the user information corresponding to each task category in the task category group as the first user recommendation information group, the method further includes: in response to determining that there is no task category group in the above-mentioned task release parameter information, determining the historical task category group of the above-mentioned task release user account as the task category group.

[0014] Optionally, before the above-mentioned response to the task release parameter information including the task output identifier group, obtaining the maximum accepted task output identifier score value in the included accepted task output identifier score value group and the user information corresponding to the task output identifier in the task output identifier group as the second user recommendation information group, the method further includes: in response to determining that there is no task output identifier group in the above-mentioned task release parameter information, determining the historical task output identifier group of the above-mentioned task release user account as the task output identifier group.

[0015] Optionally, before generating the recommendation value, the method further includes: obtaining a blacklist user identifier group corresponding to the published user account identifier; removing user recommendation information from the user recommendation information group whose task-accepting user identifier is the same as the blacklist user identifier in the blacklist user identifier group, so as to update the user recommendation information group.

[0016] Optionally, the above-mentioned task release parameter information may also include the number of recommended users.

[0017] Optionally, the method further includes: selecting the number of user recommendations from the above-mentioned user recommendation information sequence as target user recommendation information to obtain a target user recommendation information set; sending the target user recommendation information set to the terminal corresponding to the task publishing user account, wherein the terminal is used to display each target user recommendation information in the target user recommendation information set.

[0018] Optionally, the above-mentioned task release parameter information may also include the set of recommended user percentages for the corresponding task category group.

[0019] Optionally, the method further includes: selecting a predetermined number of user recommendation information from the above-mentioned user recommendation information sequence, wherein, for each task category in the above-mentioned task category group and the recommended user percentage in the recommended user percentage set corresponding to the above-mentioned task category, the ratio of the number of the maximum accepted task category rating values ​​in each accepted task category rating value group included in the predetermined number of user recommendation information to the predetermined number of maximum accepted task category rating values ​​corresponds to the above-mentioned recommended user percentage; sending the predetermined number of user recommendation information to the terminal corresponding to the task publishing user account, so that the terminal displays each target user recommendation information in the predetermined number of user recommendation information.

[0020] Optionally, the method further includes: sending the above-mentioned user recommendation information sequence to the terminal corresponding to the task-issuing user account, so that the terminal displays each user recommendation information in the above-mentioned user recommendation information sequence.

[0021] Secondly, some embodiments of this disclosure provide a user recommendation information generation apparatus, comprising: a receiving unit configured to receive task publishing parameter information of a task publishing user account, wherein the task publishing parameter information includes a publishing user account identifier, a task value, and a task type group; and an obtaining unit configured to obtain user information corresponding to each task type in the task type group as a user recommendation information group, wherein the user recommendation information in the user recommendation information group includes a task accepting user identifier, a user rating value, a task type rating value group, and a task acceptance value range, wherein the task type in each task type corresponds to the user recommendation information included in the user recommendation information group. The maximum task type score value in the task type score value group corresponds to the following: A generation unit is configured to generate recommendation values ​​based on the user score value included in each user recommendation information in the aforementioned user recommendation information group, the maximum task type score value in the task type score value group included in the aforementioned user recommendation information, and the score value of the user accepting the task corresponding to the publishing user account identifier included in the aforementioned user recommendation information, thus obtaining a recommendation value group; a sorting unit is configured to sort each user recommendation information in the aforementioned user recommendation information group based on the aforementioned recommendation value group, the aforementioned task value, and the respective task acceptance value ranges included in the aforementioned user recommendation information group, thus obtaining a user recommendation information sequence.

[0022] Optionally, the aforementioned user recommendation information may also include task category rating groups.

[0023] Optionally, after the acquisition unit, the device further includes: a first user recommendation information group acquisition unit and a first user recommendation information group addition unit. The first user recommendation information group acquisition unit is configured to, in response to the task release parameter information including a task category group, acquire the user information corresponding to the maximum accepted task category rating value in the included accepted task category rating value group and the task category in the task category group, as a first user recommendation information group. The first user recommendation information group addition unit is configured to add each first user recommendation information from the first user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0024] Optionally, the aforementioned user recommendation information may also include a group of task output identifier rating values.

[0025] Optionally, after the first user recommendation information group adding unit, the device further includes: a second user recommendation information group acquisition unit and a second user recommendation information group adding unit. The second user recommendation information group acquisition unit is configured to, in response to the task output identifier group included in the task release parameter information, acquire each user information corresponding to the maximum accepted task output identifier score value in the accepted task output identifier score value group and the task output identifier in the task output identifier group, as a second user recommendation information group. The second user recommendation information group adding unit is configured to add each second user recommendation information from the second user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0026] Optionally, before the generation unit, the apparatus further includes a deduplication unit configured to perform deduplication processing on the aforementioned user recommendation information group in order to update the aforementioned user recommendation information group.

[0027] Optionally, the generation unit is further configured to generate a recommendation value based on the user rating value, the maximum task type rating value, the rating value, the maximum accepted task category rating value in the accepted task category rating value group included in the user recommendation information, and the maximum accepted task output identifier rating value in the accepted task output identifier rating value group included in the user recommendation information.

[0028] Optionally, before the first user recommendation information group acquisition unit, the device further includes: a first determining unit, configured to determine the historical task category group of the task publishing user account as the task category group in response to determining that there is no task category group in the above-mentioned task publishing parameter information.

[0029] Optionally, before the second user recommendation information group acquisition unit, the device further includes: a second determining unit, configured to determine the historical task output identifier group of the task publishing user account as the task output identifier group in response to determining that there is no task output identifier group in the above-mentioned task publishing parameter information.

[0030] Optionally, prior to the generation unit, the apparatus further includes a blacklist user identifier group acquisition unit and a removal unit. The blacklist user identifier group acquisition unit is configured to acquire a blacklist user identifier group corresponding to the aforementioned publishing user account identifier. The removal unit is configured to remove user recommendation information from the aforementioned user recommendation information group from which the user accepting the task has the same blacklist user identifier as the blacklist user identifier in the aforementioned blacklist user identifier group, thereby updating the aforementioned user recommendation information group.

[0031] Optionally, the above-mentioned task release parameter information may also include the number of recommended users.

[0032] Optionally, the device further includes a first selection unit and a first sending unit. The first selection unit is configured to select the specified number of user recommendations from the user recommendation information sequence as target user recommendations, thereby obtaining a target user recommendation information set. The first sending unit is configured to send the target user recommendation information set to the terminal corresponding to the task-issuing user account, wherein the terminal is used to display each target user recommendation information in the target user recommendation information set.

[0033] Optionally, the above-mentioned task release parameter information may also include the set of recommended user percentages for the corresponding task category group.

[0034] Optionally, the device further includes a second selection unit and a second sending unit. The second selection unit is configured to select a predetermined number of user recommendation information entries from the user recommendation information sequence. For each task category in the task category group and the recommended user percentage in the set of recommended user percentages corresponding to the task category, the ratio of the number of maximum accepted task category rating values ​​in each accepted task category rating value group included in the predetermined number of user recommendation information entries to the predetermined number of maximum accepted task category rating values ​​corresponds to the recommended user percentage. The second sending unit is configured to send the predetermined number of user recommendation information entries to the terminal corresponding to the task-publishing user account, causing the terminal to display each target user recommendation information entry in the predetermined number of user recommendation information entries.

[0035] Optionally, the apparatus further includes a third sending unit configured to send the aforementioned user recommendation information sequence to the terminal corresponding to the task-issuing user account, so that the terminal displays each user recommendation information in the aforementioned user recommendation information sequence.

[0036] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0037] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0038] The above embodiments of this disclosure have the following beneficial effects: User traffic loss is reduced through the user recommendation information generation method of some embodiments of this disclosure. Specifically, the reasons for user traffic loss are as follows: When there is a large amount of user information of users who accept tasks, the task publisher needs to filter the user information of users who accept tasks from a large number of user information, which easily leads to omissions of the indicators (task type) of the user information of the filtered user information, resulting in low efficiency in selecting user information and poor user experience; when there is a small amount of user information of users who accept tasks, the diversity of user information that can be selected is poor, further leading to a poor user experience; and it is not possible to filter the user information of users who accept tasks by the historical price range of the tasks accepted by the user information, resulting in a low matching degree between the user information of the filtered user information and the task publisher, thus leading to a poor user experience. Based on this, the user recommendation information generation method of some embodiments of this disclosure first obtains a user recommendation information group by using the task type group included in the task publishing parameter information of the task publisher's account, and then sorts the obtained user recommendation information group by using the generated recommendation value group, task value, and the task value range of each user accepting the task. Because user recommendation information is obtained through task type groups, it's possible to match the acquired user recommendations with the various task types provided by the task-posting user's account, reducing the number of missed metrics and increasing the diversity of available task-accepting users. Furthermore, sorting the user recommendation information groups based on generated recommendation value groups improves the efficiency of task-posting users in selecting task-accepting users. Additionally, since the sorting of user recommendation information groups considers the value range of tasks accepted by task-accepting users, it improves the matching degree between task-accepting users and task-posting users. This enhances the user experience and reduces user churn. Attached Figure Description

[0039] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0040] Figure 1-Figure 2 This is a schematic diagram illustrating an application scenario of a user recommendation information generation method according to some embodiments of the present disclosure;

[0041] Figure 3 These are flowcharts of some embodiments of the user recommendation information generation method according to this disclosure;

[0042] Figure 4 These are flowcharts of some other embodiments of the user recommendation information generation method according to this disclosure;

[0043] Figure 5 These are schematic diagrams illustrating the structure of some embodiments of the user recommendation information generation apparatus according to this disclosure;

[0044] Figure 6 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0045] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0046] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0047] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0048] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0049] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0050] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] Figure 1-Figure 2 This is a schematic diagram illustrating an application scenario of a user recommendation information generation method according to some embodiments of the present disclosure.

[0052] exist Figure 1 In the application scenario, firstly, the computing device 101 can receive task publishing parameter information 102 from the task publishing user account. This task publishing parameter information 102 includes a publishing user account identifier 103, a task value 104 (e.g., 100 yuan), and a task type group 105 (e.g., live streaming, text / image, and video). Then, the computing device 101 can obtain user information corresponding to each task type in the task type group 105 as a user recommendation information group 106. This user recommendation information group 106 includes a task accepting user identifier 107, a user rating value 108, a task type rating value group 109, and a task value range 110 (e.g., 80 yuan - 200 yuan). Each task type corresponds to the maximum task type rating value in the corresponding task type rating value group included in the user recommendation information. Subsequently, the computing device 101 can generate recommendation values ​​based on the user rating value included in each user recommendation information in the aforementioned user recommendation information group 106, the maximum task type rating value in the task type rating value group included in the aforementioned user recommendation information, and the rating value of the user accepting the task corresponding to the publishing user account identifier 103 in the aforementioned user recommendation information, thus obtaining a recommendation value group 111. Finally, the computing device 101 can sort each user recommendation information in the aforementioned user recommendation information group 106 based on the aforementioned recommendation value group 111, the aforementioned task value 104, and the various accepted task value ranges included in the aforementioned user recommendation information group 106, thus obtaining a user recommendation information sequence 112.

[0053] Optionally, the aforementioned user recommendation information may further include a group of task category ratings. After obtaining the user information corresponding to each task type in the aforementioned task type group as a user recommendation information group, as follows... Figure 2As shown, computing device 101 can respond to the task parameter information 102, which includes task category group 113, by obtaining the user information corresponding to the maximum accepted task category score in the accepted task category score value group and the task category in the task category group 113, as a first user recommendation information group 114. Then, computing device 101 can add each first user recommendation information from the first user recommendation information group 114 to the user recommendation information group 106 to update the user recommendation information group 106.

[0054] It should be noted that the aforementioned computing device 101 can be either hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed within the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0055] It should be understood that Figure 1-Figure 2 The number of computing devices shown is merely illustrative. Any number of computing devices can be used depending on implementation needs.

[0056] Continue to refer to Figure 3 The flowchart 300 illustrates some embodiments of a user recommendation information generation method according to the present disclosure. This user recommendation information generation method includes the following steps:

[0057] Step 301: Receive the task publishing parameter information from the task publishing user account.

[0058] In some embodiments, the entity executing the user recommendation information generation method (e.g.) Figure 1The computing device 101 shown can receive task publishing parameter information from the terminal of the task publishing user account via a wired or wireless connection. The task publishing user account can be an account that publishes tasks (item promotion / content creation). The task publishing parameter information can include a publishing user account identifier, task value, and task type group. The publishing user account identifier can be a unique identifier for the task publishing user account. The task value can be the value set by the task publishing user account, such as "XX yuan". The task type group can be a set of task types, for example, "live streaming, text and image, video". "Live streaming" can indicate completing the task through live streaming. "Text and image" can indicate completing the task by submitting created text and images. "Video" can indicate completing the task by submitting a created video. It should be noted that the wireless connection method can include, but is not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future known wireless connection methods. Therefore, the task publishing parameter information can support the retrieval of corresponding user information.

[0059] Step 302: Obtain the user information corresponding to each task type in the task type group as the user recommendation information group.

[0060] In some embodiments, the aforementioned executing entity can obtain user information corresponding to each task type in the aforementioned task type group from the terminal via a wired or wireless connection, forming a user recommendation information group. The user recommendation information in the aforementioned user recommendation information group may include a task-accepting user identifier, user rating value, task type rating value group, and task acceptance value range. Each task type corresponds to the highest task type rating value in the corresponding task type rating value group included in the user recommendation information. The task-accepting user identifier can be a unique identifier for the task-accepting user's account. The task-accepting user can be a user who accepted the task (item promotion / content creation) posted by the task-posting user. The user rating value can be a weighted rating value for the task-accepting user. The weighted rating value can be generated by the task matching platform through the following steps: a weighted sum of the comprehensive rating value corresponding to the obtained task-accepting user identifier and the task completion rate. The comprehensive rating value can be the rating value of the task posting platform corresponding to the task-accepting user identifier. The task posting platform can be a platform that provides task posting and task acceptance, such as an XX content platform. The aforementioned task acceptance value range can be a price range determined based on the tasks accepted by the user within a predetermined historical time period.

[0061] The above task type score groups can be generated by the task matching platform through the following steps:

[0062] The first step is to determine the task type group corresponding to the user identifier who accepted the task within the predetermined historical time period.

[0063] The second step is to determine the sorting value of each indicator value in the indicator value set corresponding to each acceptance task type in the above acceptance task type group, so as to generate a sorting value group and obtain a sorting value set.

[0064] The third step is to normalize each sorted value in each sorted value group of the above sorted value set that corresponds to each index value in the above index value set, so as to obtain the normalized sorted value set.

[0065] The fourth step is to determine the percentage of tasks corresponding to each task type in the above task acceptance type group, thus obtaining the task quantity percentage group.

[0066] The fifth step is to take the weighted sum of the sorted values ​​in the sorted value group corresponding to each task type in the task type group, and the weighted sum of the task quantity percentage corresponding to the task type in the task quantity percentage group, and determine the task type score value to obtain the task type score value group.

[0067] The aforementioned task matching platform can be a platform that provides task matching for users, such as Task X platform. The aforementioned sorting value can be the ordinal value of the indicator value after descending order. The aforementioned set of indicator values ​​can be "page views, detail page views, transaction volume, and active fan views". The aforementioned task quantity ratio can be the ratio of the number of tasks of the same task type accepted on the aforementioned task matching platform to the number of tasks of the same task type accepted on the aforementioned task publishing platform, the ratio of the total number of tasks on the aforementioned task matching platform to the total number of tasks on the aforementioned task publishing platform, and / or the average of predetermined ratios. Here, there are no restrictions on the setting of the predetermined ratio. There are also no restrictions on the setting of weights in the weighting process.

[0068] Step 302 allows you to obtain the user recommendation information group by accessing the task type group within the task posting parameter information of the task posting user account.

[0069] Step 303: Based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the user accepting the task included in the user recommendation information corresponding to the publishing user account identifier, generate recommendation values ​​to obtain a recommendation value group.

[0070] In some embodiments, the executing entity can generate a recommendation value group based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the publishing user account identifier corresponding to the task-accepting user identifier included in the user recommendation information. In practice, the executing entity can generate a recommendation value by weighting and summing the user rating value, the maximum task type rating value, and the rating value. Here, the setting of the weights in the weighting process is not limited. The rating value of the publishing user account identifier corresponding to the task-accepting user identifier included in the user recommendation information can be generated by the task docking platform through the following steps:

[0071] The first step is to determine the evaluation value by weighting the task pass rate corresponding to the task accepting user ID and the task publishing user account ID, in response to the existence of task cooperation records corresponding to the task accepting user ID and the task publishing user account ID, and the ratio of the number of cooperations corresponding to the task accepting user ID and the task publishing user account ID to the maximum number of cooperations corresponding to the task accepting user ID.

[0072] The second step is to determine the predetermined value as the evaluation value in response to the absence of a task cooperation record corresponding to the user ID that accepted the task and the aforementioned user account ID that published the task.

[0073] The aforementioned task collaboration record represents the number of times a user accepting a task has accepted a task posted by a user posting a task. The aforementioned collaboration count represents the number of times the user corresponding to the task-accepting user ID has accepted tasks posted by the user corresponding to the posting user account ID. Here, there are no restrictions on the predetermined values ​​or the weighting process. Therefore, the generated recommendation value set can support the sorting of the acquired user recommendation information set.

[0074] Step 304: Based on the recommended value group, task value, and the value range of each accepted task included in the user recommended information group, sort the user recommended information in the user recommended information group to obtain the user recommended information sequence.

[0075] In some embodiments, the executing entity can sort the user recommendation information in the user recommendation information group based on the recommended value group, the task value, and the various task acceptance value ranges included in the user recommendation information group, to obtain a user recommendation information sequence. In practice, the executing entity can first sort the recommended values ​​in the recommended value group in descending order to obtain a recommended value sequence. Then, it can determine the sequence number of each recommended value in the recommended value sequence corresponding to the user recommendation information in the user recommendation information group, obtaining a sequence number group. Afterwards, it can sort the user recommendation information in the user recommendation information group according to the sequence number group, obtaining a descending sequence of user recommendation information. Next, it can arrange the user recommendation information whose task acceptance value range includes the task value in the descending sequence of user recommendation information at the head position of the descending sequence, obtaining a user recommendation information sequence. The arrangement of the recommended values ​​corresponding to the user recommendation information arranged at the head position is in descending order. Therefore, the obtained user recommendation information group can be sorted using the generated recommended value group, task value, and the task acceptance value range of each user accepting the task.

[0076] Optionally, before generating the recommendation value, the executing entity may first obtain a blacklist of user identifiers corresponding to the published user account identifier. The blacklist of user identifiers can be user identifiers corresponding to users who cannot accept tasks associated with the published user account identifier. Then, user recommendation information can be removed from the user recommendation information group if the user identifier accepting the task matches a blacklisted user identifier in the blacklist of user identifiers, thus updating the user recommendation information group. This ensures that the obtained user recommendation information group does not include user recommendation information corresponding to blacklisted user identifiers from the blacklist of user identifiers.

[0077] Optionally, the aforementioned task publishing parameter information may also include the number of recommended users. This number of recommended users can be a preset number of recommended user information items to be obtained for the task publishing user account. The executing entity can first select the specified number of recommended user information items from the aforementioned user recommendation information sequence as target user recommendation information, thus obtaining a target user recommendation information set. In practice, the specified number of recommended user information items can be selected sequentially, starting from the beginning of the user recommendation information sequence. Then, the target user recommendation information set can be sent to the terminal corresponding to the task publishing user account. The terminal can be used to display each target user recommendation information item in the target user recommendation information set. Therefore, a corresponding number of user recommendation information items can be selected for display based on the preset number of recommended users.

[0078] Optionally, the executing entity can send the user recommendation information sequence to the terminal corresponding to the task-issuing user account, enabling the terminal to display each user recommendation information in the sequence. This allows the terminal to display each user recommendation information in the sequence.

[0079] The above embodiments of this disclosure have the following beneficial effects: User traffic loss is reduced through the user recommendation information generation method of some embodiments of this disclosure. Specifically, the reasons for user traffic loss are as follows: When there is a large amount of user information of users who accept tasks, the task publisher needs to filter the user information of users who accept tasks from a large number of user information, which easily leads to omissions of the indicators (task type) of the user information of the filtered user information, resulting in low efficiency in selecting user information and poor user experience; when there is a small amount of user information of users who accept tasks, the diversity of user information that can be selected is poor, further leading to a poor user experience; and it is not possible to filter the user information of users who accept tasks by the historical price range of the tasks accepted by the user information, resulting in a low matching degree between the user information of the filtered user information and the task publisher, thus leading to a poor user experience. Based on this, the user recommendation information generation method of some embodiments of this disclosure first obtains a user recommendation information group by using the task type group included in the task publishing parameter information of the task publisher's account, and then sorts the obtained user recommendation information group by using the generated recommendation value group, task value, and the task value range of each user accepting the task. Because user recommendation information is obtained through task type groups, it's possible to match the acquired user recommendations with the various task types provided by the task-posting user's account, reducing the number of missed metrics and increasing the diversity of available task-accepting users. Furthermore, sorting the user recommendation information groups based on generated recommendation value groups improves the efficiency of task-posting users in selecting task-accepting users. Additionally, since the sorting of user recommendation information groups considers the value range of tasks accepted by task-accepting users, it improves the matching degree between task-accepting users and task-posting users. This enhances the user experience and reduces user churn.

[0080] Further reference Figure 4 This illustrates a flow 400 of another embodiment of the user recommendation information generation method. Flow 400 of this user recommendation information generation method includes the following steps:

[0081] Step 401: Receive the task publishing parameter information from the task publishing user account.

[0082] Step 402: Obtain the user information corresponding to each task type in the task type group as the user recommendation information group.

[0083] In some embodiments, the implementation of steps 401-402 and the resulting technical effects can be referred to Figure 3 Steps 301-302 of the corresponding embodiments will not be repeated here.

[0084] Step 403: In response to the task parameter information including the task category group, obtain the maximum accepted task category rating value in the accepted task category rating value group and the user information corresponding to each task category in the task category group as the first user recommendation information group.

[0085] In some embodiments, the entity executing the user recommendation information generation method (e.g.) Figure 1 The computing device 101 shown can, in response to the task category group included in the task release parameter information, obtain, via wired or wireless connection, user information corresponding to the maximum accepted task category score in the task category score group and the corresponding task category from the terminal as a first user recommendation information group. For example, the maximum accepted task category score in the task category score group included in the user information of user "001" can be "0.6", and the task category corresponding to the maximum accepted task category score "0.6" can be "mobile phone". The task category group can include the task category "mobile phone". Then the execution entity can obtain the user information of user "001". The task category group can be the categories to which each task belongs, preset by the task release user account. For example, the task category group can be "books, mobile phone, computer". The user recommendation information can also include the task category score group. The task category score group can be generated by the task docking platform through the following steps:

[0086] The first step is to determine the task acceptance category group corresponding to the user IDs of the users who accepted the tasks, which are included in the user recommendation information above within the predetermined historical time period.

[0087] The second step is to determine the category index value ranking value of each category index value in the category index value set corresponding to each category of the above-mentioned task acceptance category group, so as to generate the category index value ranking value group and obtain the category index value ranking value group set.

[0088] The third step is to normalize the sorted values ​​of each category indicator value in each category indicator value group in the above category indicator value sorted value group set, which correspond to each category indicator value in the above category indicator value set, to obtain the normalized category indicator value sorted value group set.

[0089] The fourth step is to determine the task acceptance category score by weighting the normalized category index value ranking set and the category index value ranking set corresponding to each task acceptance category in the task acceptance category group.

[0090] The aforementioned task acceptance category group can be any category to which the task accepted by the user corresponding to the aforementioned task acceptance user identifier belongs. The aforementioned category indicator value sorting value can be the ordinal value of the category indicator value after descending order. For example, the aforementioned task acceptance category group can be "Books, Mobile Phones, Computers". The aforementioned category indicator value set can be "Category Transaction Volume, Active Fan Category Browsing Ratio, Active Fan Category Browsing Count". The aforementioned "Category Transaction Volume" can be the total transaction volume corresponding to a task acceptance category. The aforementioned "Active Fan Category Browsing Ratio" can be the ratio of the number of active fans browsing a task acceptance category to the total number of fans browsing a task acceptance category. The aforementioned "Active Fan Category Browsing Count" can be the number of active fans browsing a task acceptance category. The weighting settings in the weighting process are not limited here.

[0091] Step 403 allows you to obtain the corresponding user recommendation information through the task category group, thereby increasing the diversity of users who can be selected to accept tasks.

[0092] In some optional implementations of certain embodiments, before step 403, the executing entity may, in response to determining that a task category group is missing in the task publishing parameter information, determine the historical task category group of the task publishing user account as the task category group. The historical task category group may include the categories to which tasks published by the task publishing user account in a historical time period belong. This allows for the completion of missing task category groups.

[0093] Step 404: Add each first user recommendation information in the first user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0094] In some embodiments, the executing entity may add each first user recommendation information from the first user recommendation information group to the user recommendation information group to update the user recommendation information group. Thus, the previously acquired user recommendation information group can be updated using the acquired first user recommendation information group.

[0095] Step 405: In response to the published task parameter information including the task output identifier group, obtain the maximum accepted task output identifier score value in the accepted task output identifier score value group and the user information corresponding to each task output identifier in the task output identifier group as the second user recommendation information group.

[0096] In some embodiments, the executing entity may, in response to the task output identifier group included in the task publishing parameter information, obtain the user information corresponding to the maximum accepted task output identifier score value in the included accepted task output identifier score value group and the task output identifier in the task output identifier group as a second user recommendation information group. The task output identifier group can be the identifiers of the various brands to which the tasks published by the task publishing user account are preset. The user recommendation information may further include the accepted task output identifier score value group. The accepted task output identifier score value group can be generated by the task docking platform through the following steps:

[0097] The first step is to determine the task output identifier group corresponding to the task-accepting user identifier included in the above-mentioned user recommendation information within the predetermined historical time period;

[0098] The second step is to determine the output index value ranking value of each output index value in the output index value set corresponding to each output index identifier in the above-mentioned task output identifier group, so as to generate the output index value ranking value group and obtain the output index value ranking value set.

[0099] The third step is to normalize the output index value ranking values ​​in each output index value ranking group in the above output index value ranking group set, which correspond to each output index value in the above output index value set, to obtain the normalized output index value ranking group set.

[0100] The fourth step is to determine the weighted sum of the output index value ranking set after normalization and the output index value ranking set corresponding to each output index value in the task acceptance output identifier set to obtain the task acceptance output identifier score value set.

[0101] The aforementioned task-accepting output identifier group can be the identifiers of the brands to which each task belongs to the user accepting the task. The aforementioned output indicator value sorting value can be the ordinal value of the output indicator value after descending order. The aforementioned output indicator value set can be "output identifier (brand) fan pageviews, output identifier fan transaction volume". The aforementioned "output identifier (brand) fan pageviews" can be the total pageviews of fans corresponding to a task-accepting output identifier. The aforementioned "output identifier fan transaction volume" can be the total transaction volume of fans corresponding to a task-accepting output identifier. The weighting settings in the weighting process are not limited here.

[0102] Step 405 allows for the acquisition of corresponding user recommendation information through the task output identifier group, thereby increasing the diversity of selectable users for accepting tasks.

[0103] In some optional implementations of certain embodiments, before step 405, the executing entity may, in response to determining that a task output identifier group is missing from the task publishing parameter information, determine the historical task output identifier group of the task publishing user account as the task output identifier group. The historical task output identifier group may include the identifier of the brand to which the task published by the task publishing user account belonged in a historical period. This allows for the completion of missing task output identifier groups.

[0104] Step 406: Add each second user recommendation information in the second user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0105] In some embodiments, the executing entity may add each of the second user recommendation information entries in the second user recommendation information group to the user recommendation information group to update the user recommendation information group. Thus, the previously updated user recommendation information group can be updated using the acquired second user recommendation information group.

[0106] Step 407: Deduplicate the user recommendation information group to update the user recommendation information group.

[0107] In some embodiments, the executing entity may perform deduplication on the user recommendation information group to update the user recommendation information group. This allows for the deduplication of the final obtained user recommendation information group.

[0108] Step 408: Generate a recommendation value based on the user rating value, the maximum task type rating value, the rating value, the maximum accepted task category rating value in the accepted task category rating value group included in the user recommendation information, and the maximum accepted task output identifier rating value in the accepted task output identifier rating value group included in the user recommendation information.

[0109] In some embodiments, the executing entity may generate a recommendation value based on the user rating, the maximum task type rating, the rating, the maximum accepted task category rating in the group of accepted task category ratings included in the user recommendation information, and the maximum accepted task output identifier rating in the group of accepted task output identifier ratings included in the user recommendation information. In practice, the executing entity may determine the recommendation value as a weighted sum of the user rating, the maximum task type rating, the rating, the maximum accepted task category rating, and the maximum accepted task output identifier rating. Thus, the generated recommendation value group can support the sorting of the acquired user recommendation information group.

[0110] Step 409: Based on the recommended value group, task value, and the value range of each accepted task included in the user recommended information group, sort the user recommended information in the user recommended information group to obtain the user recommended information sequence.

[0111] In some embodiments, the implementation of step 409 and the resulting technical effects can be referred to Figure 3 Step 304 of the corresponding embodiment will not be described again here.

[0112] Optionally, the aforementioned task release parameter information may further include a set of recommended user percentages corresponding to the aforementioned task category group. The executing entity may first select a predetermined number of user recommendation information entries from the aforementioned user recommendation information sequence. Specifically, for each task category in the aforementioned task category group and the recommended user percentage in the set of recommended user percentages corresponding to that task category, the ratio of the number of the maximum accepted task category rating values ​​in each accepted task category rating value group included in the predetermined number of user recommendation information entries to the predetermined number of maximum accepted task category rating values ​​corresponds to the aforementioned recommended user percentage. Then, the predetermined number of user recommendation information entries can be sent to the terminal corresponding to the task release user account, allowing the terminal to display the target user recommendation information entries from the predetermined number of user recommendation information entries. The recommended user percentage in the set of recommended user percentages may be the ratio of the number of user recommendation information entries corresponding to the task category in the preset task category group to the number of user recommendation information entries included in the user recommendation information group. Therefore, user recommendation information for a corresponding number of task categories can be obtained based on the set of recommended user percentages for display.

[0113] from Figure 4 It can be seen from this that, with Figure 3 Compared to the description of some corresponding embodiments, Figure 4The process 400 of the user recommendation information generation method in some corresponding embodiments embodies the steps of updating the user recommendation information group through task category groups and task output identifier groups, and expanding the generated recommendation values. Therefore, the solutions described in these embodiments can update the acquired user recommendation information group through task category groups and task output identifier groups, enabling the acquired user recommendation information to match with each task category and task output identifier group provided by the task-publishing user account. This further reduces the number of missed indicators, improves the diversity of selectable task-accepting users, and increases the efficiency of task-publishing users in selecting task-accepting users, as well as the matching degree between task-accepting users and task-publishing users corresponding to user recommendation information. This further improves the user experience and, consequently, reduces user traffic loss.

[0114] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a user recommendation information generation apparatus, which are similar to... Figure 3 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0115] like Figure 5 As shown, a user recommendation information generation device 500 in some embodiments includes: a receiving unit 501, an acquiring unit 502, a generating unit 503, and a sorting unit 504. The receiving unit 501 is configured to receive task publishing parameter information from a task publishing user account, wherein the task publishing parameter information includes a publishing user account identifier, task value, and a task type group. The acquiring unit 502 is configured to acquire user information corresponding to each task type in the task type group as a user recommendation information group, wherein the user recommendation information in the user recommendation information group includes a task accepting user identifier, user rating value, task type rating value group, and task acceptance value range, and the task type in each task type corresponds to the largest task type in the task type rating value group included in the corresponding user recommendation information. Corresponding to the rating values, the generation unit 503 is configured to generate recommendation values ​​based on the user rating values ​​included in each user recommendation information in the aforementioned user recommendation information group, the maximum task type rating value in the task type rating value group included in the aforementioned user recommendation information, and the rating value of the user accepting the task corresponding to the publishing user account identifier in the aforementioned user recommendation information, thus obtaining a recommendation value group; the sorting unit 504 is configured to sort each user recommendation information in the aforementioned user recommendation information group based on the aforementioned recommendation value group, the aforementioned task value, and the respective task acceptance value ranges included in the aforementioned user recommendation information group, thus obtaining a user recommendation information sequence.

[0116] In some optional implementations of the embodiments, the user recommendation information mentioned above also includes a group of task category rating values.

[0117] In some optional implementations of embodiments, after the acquisition unit 502, the user recommendation information generation device 500 may further include: a first user recommendation information group acquisition unit and a first user recommendation information group adding unit (not shown in the figure). The first user recommendation information group acquisition unit is configured to, in response to the task release parameter information including a task category group, acquire the user information corresponding to the maximum accepted task category rating value in the included accepted task category rating value group and the task category in the task category group as a first user recommendation information group. The first user recommendation information group adding unit is configured to add each first user recommendation information from the first user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0118] In some optional implementations of the embodiments, the aforementioned user recommendation information also includes a group of task output identifier rating values.

[0119] In some optional implementations of embodiments, after the first user recommendation information group addition unit, the user recommendation information generation device 500 may further include: a second user recommendation information group acquisition unit and a second user recommendation information group addition unit (not shown in the figure). The second user recommendation information group acquisition unit is configured to, in response to the task output identifier group included in the published task parameter information, acquire each user information corresponding to the maximum accepted task output identifier score value in the accepted task output identifier score value group and the task output identifier in the task output identifier group as a second user recommendation information group. The second user recommendation information group addition unit is configured to add each second user recommendation information from the second user recommendation information group to the user recommendation information group to update the user recommendation information group.

[0120] In some optional implementations of the embodiments, before the generation unit 503, the user recommendation information generation device 500 may further include: a deduplication unit (not shown in the figure), configured to perform deduplication processing on the user recommendation information group in order to update the user recommendation information group.

[0121] In some optional implementations of the embodiments, the generation unit 503 may be further configured to generate a recommendation value based on the user rating value, the maximum task type rating value, the rating value, the maximum accepted task category rating value in the accepted task category rating value group included in the user recommendation information, and the maximum accepted task output identifier rating value in the accepted task output identifier rating value group included in the user recommendation information.

[0122] In some optional implementations of the embodiments, before the first user recommendation information group acquisition unit, the user recommendation information generation device 500 may further include: a first determining unit (not shown in the figure), configured to determine the historical task category group of the task publishing user account as the task category group in response to determining that there is no task category group in the above-mentioned task publishing parameter information.

[0123] In some optional implementations of the embodiments, before the second user recommendation information group acquisition unit, the user recommendation information generation device 500 may further include: a second determining unit (not shown in the figure), configured to determine the historical task output identifier group of the task publishing user account as the task output identifier group in response to determining that there is no task output identifier group in the above-mentioned task publishing parameter information.

[0124] In some optional implementations, before generation unit 503, user recommendation information generation apparatus 500 may further include: a blacklist user identifier group acquisition unit and a removal unit (not shown in the figure). The blacklist user identifier group acquisition unit is configured to acquire a blacklist user identifier group corresponding to the aforementioned publishing user account identifier. The removal unit is configured to remove user recommendation information from the aforementioned user recommendation information group where the user identifier of the task-accepting user is the same as a blacklist user identifier in the aforementioned blacklist user identifier group, thereby updating the aforementioned user recommendation information group.

[0125] In some optional implementations of the embodiments, the above-mentioned task release parameter information also includes the number of recommended users.

[0126] In some optional implementations of the embodiments, the user recommendation information generation device 500 may further include a first selection unit and a first sending unit (not shown in the figure). The first selection unit is configured to select the specified number of user recommendation information entries from the user recommendation information sequence as target user recommendation information, thereby obtaining a target user recommendation information set. The first sending unit is configured to send the target user recommendation information set to the terminal corresponding to the task-issuing user account, wherein the terminal is used to display each target user recommendation information entry in the target user recommendation information set.

[0127] In some optional implementations of the embodiments, the above-mentioned task release parameter information also includes a set of recommended user percentages corresponding to the above-mentioned task category group.

[0128] In some optional implementations of the embodiments, the user recommendation information generation device 500 may further include: a second selection unit and a second sending unit (not shown in the figure). The second selection unit is configured to select a predetermined number of user recommendation information items from the user recommendation information sequence. For each task category in the task category group and the recommended user percentage in the recommended user percentage set corresponding to the task category, the ratio of the number of maximum accepted task category rating values ​​in each accepted task category rating value group included in the predetermined number of user recommendation information items to the predetermined number of maximum accepted task category rating values ​​corresponds to the recommended user percentage. The second sending unit is configured to send the predetermined number of user recommendation information items to the terminal corresponding to the task publishing user account, so that the terminal displays each target user recommendation information item in the predetermined number of user recommendation information items.

[0129] In some optional implementations of the embodiments, the user recommendation information generation device 500 may further include: a third sending unit (not shown in the figure), configured to send the above-mentioned user recommendation information sequence to the terminal corresponding to the task publishing user account, so that the terminal displays each user recommendation information in the above-mentioned user recommendation information sequence.

[0130] It is understandable that the units described in the device 500 are related to the reference. Figure 3 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 500 and the units contained therein, and will not be repeated here.

[0131] The following is for reference. Figure 6 It illustrates electronic devices suitable for implementing some embodiments of this disclosure (e.g., Figure 1 A schematic diagram of the structure of the computing device 101)600. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0132] like Figure 6 As shown, electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 602 or a program loaded from storage device 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of electronic device 600. Processing device 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0133] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic device 600 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 An electronic device 600 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 6 Each box shown can represent a device or multiple devices as needed.

[0134] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0135] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0136] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0137] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs. When the electronic device executes the aforementioned one or more programs, the electronic device causes the following to occur: receive task posting parameter information from a task posting user account, wherein the task posting parameter information includes a posting user account identifier, task value, and task type group; acquire user information corresponding to each task type in the task type group as a user recommendation information group, wherein the user recommendation information in the user recommendation information group includes a task accepting user identifier, user rating value, task type rating value group, and task acceptance value range, and the task type in each task type corresponds to the maximum task type rating value in the task type rating value group included in the corresponding user recommendation information; generate recommendation values ​​based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the posting user account identifier corresponding to the task accepting user identifier included in the user recommendation information, thereby obtaining a recommendation value group; and sort each user recommendation information in the user recommendation information group based on the recommendation value group, the task value, and the task acceptance value ranges included in the user recommendation information group, thereby obtaining a user recommendation information sequence.

[0138] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0140] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a receiving unit, a transmitting unit, a generating unit, and a sorting unit. The names of these units do not necessarily limit the unit itself; for example, a sorting unit may be described as "a unit that sorts each user recommendation information in the user recommendation information group based on the aforementioned recommendation value group, the aforementioned task value, and the respective accepted task value ranges included in the aforementioned user recommendation information group to obtain a user recommendation information sequence."

[0141] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0142] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for generating user recommendation information, comprising: Receive task publishing parameter information from the task publishing user account, wherein the task publishing parameter information includes the publishing user account identifier, task value, and task type group, and the task value is the value of the task set by the task publishing user account; Obtain user information corresponding to each task type in the task type group as a user recommendation information group. The user recommendation information in the user recommendation information group includes the user ID of the user accepting the task, the user rating value, the task type rating value group, and the value range of the accepted task. The task type in each task type corresponds to the maximum task type rating value in the task type rating value group included in the corresponding user recommendation information. Based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the user accepting the task corresponding to the publishing user account identifier in the user recommendation information, a recommendation value is generated to obtain a recommendation value group. Based on the recommended value group, the task value, and the various accepted task value ranges included in the user recommendation information group, the user recommendation information in the user recommendation information group is sorted to obtain a user recommendation information sequence.

2. The method according to claim 1, wherein, The user recommendation information also includes a group of task category rating values; as well as After obtaining the user information corresponding to each task type in the task type group as a user recommendation information group, the method further includes: In response to the task release parameter information including task category groups, the maximum accepted task category rating value in the accepted task category rating value group and the user information corresponding to each task category in the task category group are obtained as the first user recommendation information group. Each first user recommendation in the first user recommendation information group is added to the user recommendation information group to update the user recommendation information group.

3. The method according to claim 2, wherein, The user recommendation information also includes a group of task output identifier rating values; as well as After adding each of the first user recommendation information entries in the first user recommendation information group to the user recommendation information group, the method further includes: In response to the task output identifier group being published, the user information corresponding to the maximum accepted task output identifier score value in the accepted task output identifier score value group and the task output identifier in the task output identifier group is obtained as a second user recommendation information group. Each second user recommendation in the second user recommendation information group is added to the user recommendation information group to update the user recommendation information group.

4. The method according to claim 3, wherein, Before generating the recommended values, the method further includes: The user recommendation information group is deduplicated in order to update the user recommendation information group.

5. The method according to claim 4, wherein, The generation of recommended values ​​includes: A recommendation value is generated based on the user rating value, the maximum task type rating value, the rating value, the maximum accepted task category rating value in the accepted task category rating value group included in the user recommendation information, and the maximum accepted task output identifier rating value in the accepted task output identifier rating value group included in the user recommendation information.

6. The method according to claim 3, wherein, Before the step of responding to the task release parameter information including a task category group, and obtaining the maximum accepted task category rating value in the included accepted task category rating value group and the user information corresponding to each task category in the task category group as the first user recommendation information group, the method further includes: In response to determining that no task category group exists in the task publishing parameter information, the historical task category group of the task publishing user account is determined as the task category group; and Before responding to the task output identifier group by obtaining the maximum accepted task output identifier score from the accepted task output identifier score value group and the user information corresponding to each task output identifier in the task output identifier group as the second user recommendation information group, the method further includes: In response to determining that there is no task output identifier group in the published task parameter information, the historical task output identifier group of the task publishing user account is determined as the task output identifier group.

7. The method according to any one of claims 1-6, wherein, Before generating the recommended values, the method further includes: Obtain the blacklist user identifier group corresponding to the published user account identifier; The user recommendation information group is updated by removing user recommendation information whose user ID for accepting the task is the same as the blacklist user ID in the blacklist user ID group.

8. The method according to any one of claims 1-6, wherein, The task release parameter information also includes the number of recommended users; as well as The method further includes: Select the recommended user information of the specified number of users from the user recommendation information sequence as the target user recommendation information to obtain the target user recommendation information set; The target user recommendation information set is sent to the terminal corresponding to the task-issuing user account, wherein the terminal is used to display each target user recommendation information in the target user recommendation information set.

9. The method according to claim 5, wherein, The task release parameter information also includes a set of recommended user percentages corresponding to the task category group; as well as The method further includes: A predetermined number of user recommendation information are selected from the user recommendation information sequence. For each task category in the task category group and the recommended user percentage in the recommended user percentage set corresponding to the task category, the ratio of the number of the maximum accepted task category rating values ​​corresponding to the task category to the predetermined number of the predetermined number of user recommendation information includes the maximum accepted task category rating values ​​in each accepted task category rating value group. The predetermined number of user recommendation messages are sent to the terminal corresponding to the task-issuing user account, so that the terminal displays the target user recommendation messages among the predetermined number of user recommendation messages.

10. The method according to claim 1, wherein, The method further includes: The user recommendation information sequence is sent to the terminal corresponding to the task-issuing user account, so that the terminal displays each user recommendation information in the user recommendation information sequence.

11. A user recommendation information generation device, comprising: The receiving unit is configured to receive task publishing parameter information from the task publishing user account, wherein the task publishing parameter information includes the publishing user account identifier, task value, and task type group, and the task value is the value of the task set by the task publishing user account. The acquisition unit is configured to acquire user information corresponding to each task type in the task type group as a user recommendation information group, wherein the user recommendation information in the user recommendation information group includes the user identifier of the user accepting the task, the user rating value, the task type rating value group, and the value range of the accepted task, and the task type in each task type corresponds to the maximum task type rating value in the task type rating value group included in the corresponding user recommendation information; The generation unit is configured to generate recommendation values ​​based on the user rating value included in each user recommendation information in the user recommendation information group, the maximum task type rating value in the task type rating value group included in the user recommendation information, and the rating value of the user accepting the task corresponding to the publishing user account identifier in the user recommendation information, to obtain a recommendation value group. The sorting unit is configured to sort each user recommendation information in the user recommendation information group based on the recommendation value group, the task value, and the various accepted task value ranges included in the user recommendation information group, to obtain a user recommendation information sequence.

12. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-10.

13. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1-10.

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