Candidate user recommendation method and apparatus, electronic device, and storage medium

Abstract

The application relates to the technical field of data processing, and provides a candidate user recommendation method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a processing request of an award to be recommended, the processing request of the award to be recommended comprising the type to be recommended, the identifier of the award to be recommended, the label of a target scene to be applied, and a first type of user group to be selected; determining the target scene corresponding to the label of the target scene according to the processing request of the award to be recommended, and obtaining a target recommendation mode matched with the target scene; obtaining a recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended by using the target recommendation mode, obtaining target data corresponding to the first type of user group required based on the recommendation rule, and determining a candidate user from the first type of user group according to the recommendation rule and the target data. In this way, the award can be quickly and reasonably allocated to the candidate user, the efficiency is improved, and the work intensity is reduced.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for recommending candidate users. Background Technology

[0002] In order to enhance students' learning interest and make learning more enjoyable, teachers can set up a variety of awards to encourage students to actively participate in learning competitions, thereby improving students' intrinsic motivation. Therefore, determining the winners is particularly important.

[0003] In existing technologies, award winners are usually selected based on the teacher's personal subjective intentions. For example, the best-performing student of the week is selected based on the ranking of competition results or the improvement of the ranking in the competition.

[0004] However, using the aforementioned subjective award method cannot quickly and reasonably allocate awards, and it also increases the workload of teachers. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for recommending candidate users, which addresses the problems of unreasonable award allocation, slow efficiency, and high workload associated with award evaluation based on subjective intentions.

[0006] Firstly, this application provides a candidate user recommendation method, the method comprising:

[0007] The system receives a processing request for an award to be recommended. The processing request for the award to be recommended includes: the type of award to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected.

[0008] Based on the processing request of the award to be recommended, determine the target scene corresponding to the tag of the target scene, and obtain the target recommendation mode that matches the target scene;

[0009] Using the target recommendation mode, the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are obtained, and the target data corresponding to the first type of user group is obtained based on the recommendation rules. Candidate users are determined from the first type of user group according to the recommendation rules and the target data.

[0010] Optionally, before receiving a processing request for an award to be recommended, the method further includes:

[0011] Receive a display tag request, and display tags for multiple application scenarios according to the display tag request;

[0012] In response to the selection operation of the second type of user, a processing request is generated containing tags of the target scenario to be applied; the selection operation is used to determine the tag of the target scenario to be applied from the tags of the multiple application scenarios.

[0013] Optionally, the target recommendation mode includes: subjective recommendation mode, profile analysis mode, and hierarchical analysis mode; obtaining recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, and obtaining target data corresponding to the required first type of user group based on the recommendation rules, including:

[0014] If the target recommendation mode adopted is a subjective recommendation mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the ranking rule, and the target data obtained based on the ranking rule is the test score of the first type of user group.

[0015] If the target recommendation mode adopted is the profile analysis method, then the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are the user profile construction rules, and the target data obtained based on the user profile construction rules are the indicator data of the first type of user group; the indicator data includes information profile data, behavioral profile data and group profile data; the information profile data is data describing the learning ability of the first type of user, the behavioral profile data is data describing the daily behavior of the first type of user, and the group profile data is data describing the corresponding identity of the first type of user;

[0016] If the target recommendation mode adopted is the analytic hierarchy process (AHP) mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the hierarchical model construction rule, and the target data obtained based on the hierarchical model construction rule is the proportion of the indicator data of the first user group; the hierarchical model construction rule is the user profile construction rule based on the analytic hierarchy process.

[0017] Optionally, determining candidate users from the first user group based on the recommendation rules and the target data includes:

[0018] When the recommendation rule is determined to be a ranking rule and the target data is the test scores of the first type of user group, the test scores are ranked according to a specific order algorithm to obtain the test scores in the top N positions, and the first type of users corresponding to the test scores in the top N positions are determined as candidate users; where N is a positive integer greater than or equal to 1.

[0019] When the recommendation rule is determined to be a user profile construction rule and the target data is the indicator data of the first type of user group, a predefined algorithm is used to calculate the score value of each first type of user corresponding to the indicator data, and candidate users are determined from the first type of user group based on the score value.

[0020] When the recommendation rule is determined to be a hierarchical model construction rule, and the target data is the proportion of indicator data of the first type of user group, then for each first type of user, the proportion corresponding to the first type of user is divided into multiple dimensions based on the identifier of the award to be recommended, a judgment matrix is ​​constructed based on the multiple dimensions of proportion, and the weight vector of the first type of user is calculated using the hierarchical analysis method and the judgment matrix. Based on the calculated weight vector corresponding to the first type of user group, candidate users are determined from the first type of user group.

[0021] Optionally, the analytic hierarchy process (AHP) includes a hierarchical single ranking method and a hierarchical overall ranking method; the weight vector includes a first weight vector and a second weight vector; the weight vector of the first type of user is calculated using the AHP and the judgment matrix, and candidate users are determined from the first type of user group based on the calculated weight vector corresponding to the first type of user group, including:

[0022] The first weight vector for each dimension is calculated using the hierarchical single sorting method and the judgment matrix.

[0023] Based on the proportion of the indicator data in the first type of users, the dimensions are divided to obtain multiple judgment matrices, and the hierarchical total ranking method and the multiple judgment matrices are used to calculate the second weight vector of the first type of users.

[0024] Calculate the product of the first weight vector and the second weight vector corresponding to each user of the first type to obtain the weight value of the first user group, and determine candidate users from the first user group based on the weight value.

[0025] Optionally, the method further includes:

[0026] Perform a consistency check on the judgment matrix;

[0027] If the judgment matrix fails the consistency test, the weight values ​​in the judgment matrix are adjusted until the judgment matrix passes the consistency test.

[0028] Optionally, the method further includes:

[0029] In response to the touch operation of the second type of user, the award status of the candidate user is displayed, and a share link is generated based on the award status;

[0030] Obtain the contact information of the candidate user, and send the sharing link to the terminal device corresponding to the candidate user based on the contact information.

[0031] Secondly, this application also provides a candidate user recommendation device, the device comprising:

[0032] The receiving module is used to receive processing requests for awards to be recommended. The processing requests for awards to be recommended include: the type to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected.

[0033] The first determining module is used to determine the target scene corresponding to the tag of the target scene according to the processing request of the award to be recommended, and to obtain the target recommendation mode matching the target scene;

[0034] The second determining module is used to adopt the target recommendation mode, obtain recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, obtain target data corresponding to the first type of user group based on the recommendation rules, and determine candidate users from the first type of user group according to the recommendation rules and the target data.

[0035] Thirdly, this application also provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0038] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0039] In summary, this application provides a candidate user recommendation method, apparatus, electronic device, and storage medium. It can receive a processing request for an award to be recommended, which includes: the type of award to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and a first-class user group to be selected. Further, based on the processing request, it determines the target scenario corresponding to the tag of the target scenario and obtains a target recommendation pattern matching the target scenario. Further, using the target recommendation pattern, it obtains recommendation rules corresponding to the identifier and type of the award to be recommended, and obtains the target data corresponding to the first-class user group based on the recommendation rules. Based on the recommendation rules and the target data, it determines candidate users from the first-class user group. This approach is applicable to various scenarios, offers high flexibility, and allows for rapid and reasonable allocation of awards to candidate users, improving efficiency while reducing workload. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] Figure 1 A schematic diagram of a system interface for an application candidate user recommendation method provided in an embodiment of this application;

[0042] Figure 2 A flowchart illustrating a candidate user recommendation method provided in an embodiment of this application;

[0043] Figure 3 A schematic diagram illustrating a process for conducting a group competition, provided as an embodiment of this application;

[0044] Figure 4 A schematic diagram of an interface for starting a game competition and setting score reward and penalty rules, provided for an embodiment of this application;

[0045] Figure 5 A schematic diagram of a competition group matching interface provided in an embodiment of this application;

[0046] Figure 6 A schematic diagram of an interface for uploading preliminary results provided in an embodiment of this application;

[0047] Figure 7 A schematic diagram of a competition group's interface provided in an embodiment of this application;

[0048] Figure 8 A schematic diagram of the interface of a test schedule review poster provided in an embodiment of this application;

[0049] Figure 9This application provides a schematic diagram of a process for obtaining indicator data using user profile construction rules in an embodiment of the present application.

[0050] Figure 10 This is a schematic diagram of the structure of a hierarchical model construction rule provided in an embodiment of this application;

[0051] Figure 11 A flowchart illustrating an application of the Analytic Hierarchy Process (AHP) pattern is provided for an embodiment of this application.

[0052] Figure 12 A flowchart illustrating a specific candidate user recommendation method provided in this application embodiment;

[0053] Figure 13 A schematic diagram of a candidate user recommendation device provided in an embodiment of this application;

[0054] Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0055] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0056] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and purpose. For example, "first device" and "second device" are merely used to distinguish different devices and do not limit their order of execution. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0057] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0058] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0059] In order to enhance students' learning interest and make learning more enjoyable, teachers often set up a variety of awards to encourage students to actively participate in learning competitions, thereby improving students' intrinsic motivation. Therefore, determining the winners is particularly important.

[0060] In one possible implementation, due to the lack of a perfect recommendation method, teachers generally select award recipients based on their personal subjective preferences. For example, the best-performing student of the week may be selected based on their competition ranking or their improvement in competition ranking.

[0061] However, such a selection method not only interferes with the fairness and rationality of the awards, but also increases teachers' working hours. Furthermore, unreasonable award criteria not only lead to inaccurate feedback on students' learning outcomes, but also affect the enthusiasm of students with poorer grades.

[0062] It should be noted that the subjective award evaluation method used in teaching may not allow teachers to comprehensively evaluate student performance when allocating awards, and it lacks data-driven reference standards. Therefore, a fair and reasonable award candidate recommendation mechanism needs to rely on scientific analysis methods and systematic data processing.

[0063] To address the aforementioned issues, this application provides a candidate user recommendation method applicable to various application scenarios. By receiving processing requests for awards to be recommended, the method identifies the target scenario that best suits the current situation and obtains a target recommendation pattern matching the target scenario. Furthermore, it adopts the recommendation rules corresponding to this target recommendation pattern and acquires a series of target data corresponding to the target scenario. Using the target data and recommendation rules, the system performs calculations and processing, automatically providing candidate users for the corresponding awards and quickly and reasonably generating a candidate user list. Therefore, it can fairly and quickly allocate awards while reducing workload.

[0064] Understandably, the aforementioned candidate user recommendation method, when applied to teaching scenarios, comprehensively considers various aspects of students' performance, such as their preliminary and final scores, and praise tag scores, ensuring fairness and reasonableness in award recommendations. It also takes into account the latest learning progress of all participating students, such as their latest test scores, classroom performance, and homework completion, providing timely and accurate feedback on student learning outcomes. Furthermore, using this candidate user recommendation method can shorten the time teachers spend selecting award-winning students, making the award process traceable and based on established rules.

[0065] For example, Figure 1 This application provides a schematic diagram of a system interface for an application candidate user recommendation method, as shown in the following embodiment. Figure 1 As shown, the candidate user recommendation method is applied in a teaching scenario, with the terminal device as the execution entity. This terminal device has a system installed with the candidate user recommendation method. Taking a competitive game as an example, specifically... Figure 1 As shown in Figure A, after students participate in the learning competition, teachers upload and submit students' final test scores via a terminal device. Student ID is optional, while name and score are required. For example, if a teacher fills in the name and score columns, such as "Liu**" with a score of 89, and then clicks the "Save Scores" button, the system calculates a series of related data based on the obtained student final test scores, such as the group final test average score / average ranking, and the class final test average score. Correspondingly, the system interface redirects to... Figure 1 As shown in Figure B, the system is applied to the candidate user recommendation method to calculate the candidate award-winning students, the details of the award points for the candidate award-winning students, and the awards to be recommended. Teachers can manually set the corresponding students to add points based on the candidate award-winning students recommended by the system. For example, the awards can be "Never Ending", "Renowned", and "Disappointing", and the corresponding students and points can be set respectively. For example, "Never Ending" corresponds to "+0.5" comment points, and the corresponding students are "An*" and "Super*". Specifically, this application embodiment does not make specific limitations on each award set, and it can be any comment points or other content.

[0066] Furthermore, after generating the candidate award-winning students, in response to the teacher's touch operation, the system can display the final results announcement page, which can be used to view the group rankings and the students who won bonus points for each award, such as... Figure 1 As shown in C, this is a schematic diagram of the group ranking interface. Figure 1 As shown in D, this is a schematic diagram of the interface showing the bonus points awarded to the winning students.

[0067] It should be noted that the candidate user recommendation method can be applied not only to teaching scenarios but also to other scenarios, such as sports competitions. This application does not make any specific limitations on this.

[0068] The aforementioned terminal devices can be either wireless or wired. A wireless terminal can be a device that provides voice and / or other service data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The wireless terminal can communicate with one or more core network devices via a Radio Access Network (RAN). The wireless terminal can be a mobile terminal, such as a mobile phone (or "cellular" phone) or a computer with a mobile terminal, for example, a portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile device, which exchanges voice and / or data with the RAN. Furthermore, a wireless terminal can also be a Personal Communication Service (PCS) phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), or other similar devices. A wireless terminal can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station, remote station, remote terminal, access terminal, user terminal, user agent, user device, or user equipment, and is not limited thereto. Optionally, the aforementioned terminal devices can be smartphones, tablets, or other similar devices.

[0069] The technical solutions of this application will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0070] Figure 2 This is a flowchart illustrating a candidate user recommendation method provided in an embodiment of this application, as shown below. Figure 2 As shown, the candidate user recommendation method includes the following steps:

[0071] S201. Receive a processing request for an award to be recommended. The processing request for an award to be recommended includes: the type of award to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected.

[0072] In this embodiment, the type can be used to distinguish awards in different occasions. One type can correspond to multiple awards, such as sports type, teaching type, etc. The sports type can have awards for the fastest runner, the most jump rope, etc., and the teaching type can have awards for the fastest improvement, the best performance this week, etc. The identifier of the award is the identification number corresponding to each award, and each award has a corresponding identification number. The tag can refer to the keyword tag corresponding to different application scenarios. The target scenario can be determined according to the selected tag. For example, if the tag is "one day" or "test score", the target scenario is to generate candidate award users based on the test scores within a day. The first type of user group can refer to the student group, or it can be other user groups, such as the user group of game competitions, athletes, etc. This application does not make specific limitations on this.

[0073] It should be noted that the type to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected are the selected type, the identifier of the award, the tag of the target scenario, and the first type of user group in response to the touch operation of the second type of user. For example, if the teaching type is selected, the award identifier is 1 (corresponding to the best test score), the tag is "one day" and "test score", and the student ID is 1-10. This application embodiment does not limit the specific content of the selection; the above is only an example.

[0074] The second type of user can refer to teachers or other administrators, but this application embodiment does not specifically limit this.

[0075] S202. Based on the processing request of the award to be recommended, determine the target scene corresponding to the tag of the target scene, and obtain the target recommendation mode matching the target scene.

[0076] In this embodiment of the application, there can be multiple target recommendation modes. Different application scenarios correspond to different recommendation modes. For example, the recommendation modes can include subjective recommendation mode, profile analysis mode, and hierarchical analysis mode. In the target scenario of generating candidate award users based on test scores within a day, the target recommendation mode that can be matched is the subjective recommendation mode, that is, the system can obtain the list of candidate award students by sorting the test scores from high to low.

[0077] In this step, in response to the touch operation of the second type of user, the terminal device generates a processing request for the award to be recommended. Further, based on the processing request for the award to be recommended, the terminal device determines the target scenario corresponding to the tag of the target scenario, such as determining the best-performing candidate user this week.

[0078] It should be noted that the embodiments of this application do not limit the types of target recommendation modes. A target scenario can be matched with multiple recommendation modes. However, the system will automatically match the most suitable recommendation mode as the target recommendation mode. Correspondingly, the target recommendation mode corresponding to the target scenario can also be manually selected. The embodiments of this application do not make specific limitations on this.

[0079] S203. Using the target recommendation mode, obtain recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, and obtain the target data corresponding to the first type of user group based on the recommendation rules, and determine candidate users from the first type of user group according to the recommendation rules and the target data.

[0080] In this embodiment, the recommendation rules may include a series of predefined algorithms for calculating rating data or performing data processing to evaluate candidate users. Different recommendation modes correspond to different recommendation rules, such as user profile construction rules for the profile analysis mode and hierarchical model construction rules for the hierarchical analysis mode. This embodiment does not specifically limit the recommendation rules corresponding to the recommendation mode. For example, the hierarchical analysis mode may also correspond to two rules: hierarchical model construction rules and user profile construction rules. After calculating the analysis results using these two rules, the optimal one can be selected, or a comprehensive approach can be taken, such as using a weighted average method to process the analysis results. This embodiment does not specifically limit this approach.

[0081] In this step, the target data corresponding to the first type of user group is obtained based on the recommendation rule. The target data is data applicable to the recommendation rule and the target scenario. For example, if the target scenario is the candidate user with the best performance this week, the obtained recommendation rule can be the profile analysis method. Correspondingly, the target data is the user's historical data within a week, including preliminary scores, competition scores, praise tag scores, etc. The specific content corresponding to the target data is not limited in this application embodiment.

[0082] Furthermore, based on the recommendation rules corresponding to the profile analysis model and the historical data of users within a week, calculations are performed to obtain analysis results, and then the analysis results are used to determine candidate users from the first type of user group.

[0083] Therefore, this application provides a candidate user recommendation method. It can receive a processing request for an award to be recommended, determine the target scenario corresponding to a tag of the target scenario, and obtain a target recommendation pattern matching the target scenario. Furthermore, using the target recommendation pattern, it obtains recommendation rules corresponding to the identifier of the award to be recommended and the type of the award to be recommended, and obtains target data corresponding to the required first type of user group based on the recommendation rules. Candidate users are then determined from the first type of user group according to the recommendation rules and the target data. In this way, the candidate user recommendation method can be applied to various scenarios for obtaining candidate users, offering high flexibility and enabling rapid and reasonable allocation of awards to candidate users, improving efficiency while reducing workload.

[0084] Optionally, taking a teaching scenario as an example, before the system recommends candidate students using the candidate user recommendation method, it can assign competition groups and obtain application data corresponding to the first group of candidate users. This application data can be obtained through group competitions. Specifically... Figure 3 This application provides a flowchart illustrating a group competition process, taking students as an example of the first user group. Figure 3 As shown, the overall process of the group competition includes the following steps:

[0085] Step 1: The teacher pre-assigns students to groups, accesses the quiz game page, clicks the "Start Competition" button, sets the scoring and reward / penalty rules, and then begins the competition. For example... Figure 4 This application provides an example of an interface diagram for starting a game competition and setting score reward and penalty rules; as shown. Figure 4 As shown in Figure A, teachers can pre-assign student groups, operate the terminal device to enter the test game page, and then click the competition start button. For example, clicking the start button in the "Dual-Wheel Championship" box will redirect to... Figure 4 In the interface shown in Figure B, after setting the scoring and reward / penalty rules, the teacher clicks the "Save and Start Competition" button to begin the competition game.

[0086] Step 2: After the competition begins, you will enter the group competition page. The system will automatically pair all groups into pairs for the competition. If a class has an odd number of groups, one group will compete against the entire class. For example, Figure 5 This application provides a schematic diagram of a competition group matching interface; as shown in the embodiments of this application. Figure 5 As shown, the system can assign student groups within a class using a competition group allocation method, matching each student group with a matchup group, such as... Figure 5 The interface shown matches the "Study Hard Team" with the "Strive for Excellence Team". Understandably, it also matches other student teams with other teams, which will not be elaborated here.

[0087] Step 3: Teachers upload and submit students' preliminary test scores using their terminal devices. Student ID is optional, while name and score are required. For example, Figure 6 This application provides an example of an interface diagram for uploading preliminary results, as shown in the embodiment of the present application. Figure 6 As shown, teachers fill in the name column and the score column, such as Wang**'s score being 95.

[0088] Step 4: Based on the student preliminary results data obtained in Step 3, the system calculates the average score / average ranking of the preliminary group and the average score of the class in the preliminary round. Based on the average scores, the groups are divided into the upper and lower zones of the competition game.

[0089] Specifically, in the two-team competition, the teams are compared by their average score. The team with the higher average score enters the upper zone of the competition, while the team with the lower average score enters the lower zone. If the average scores are the same, the average ranking (sum of group members' rankings / number of participants in the group) is compared. The team with the lower average ranking enters the upper zone, and the team with the higher average ranking enters the lower zone. If both the average score and average ranking are the same, the average score is compared with the class average. If the average score is greater than or equal to the class average, both teams enter the upper zone; otherwise, they enter the lower zone.

[0090] For example, Figure 7 This application provides a schematic diagram of a competition group's interface, as shown in the embodiments of this application. Figure 7 As shown, the competition groups are divided into upper and lower zones based on their average scores. For example, the upper zone includes teams like "Study Hard Team", "Sprint Forward!!", "Nanbowan", and "Strive to Break Through Yourself!", while the lower zone includes teams like "Strive for Progress Every Day Team", "Yes, Yes, Yes, All Correct", "Win First Place", and "The Whole Class". It should be noted that this embodiment does not specifically limit the team names for each group; the above is merely an example.

[0091] It should be noted that the division of the group into upper and lower regions is not necessary, and step 5 can be performed directly. This application embodiment does not specifically limit this.

[0092] Step 5: Teachers upload and submit students' final test scores using their terminal devices. Student ID is optional, while name and score are required.

[0093] Step 6: Based on the student finals performance data obtained in Step 5, the system calculates the group finals average score / average ranking and the class finals average score. Based on the upper and lower ranking zones of the competition game obtained in Step 4, the upper-ranked groups are ranked first in the finals, initially based on average score. If the average scores are the same, the average ranking is compared; if the average rankings are also the same, they are ranked equally. The ranking rules for the lower-ranked groups are the same as those for the upper-ranked groups, and will not be repeated here. This process then yields the overall group ranking order.

[0094] Step 7: Based on the data obtained in Step 6, the system uses a candidate user recommendation method to calculate the details of team and individual award points, as well as special awards (which may include endless pursuit, illustrious reputation, disappointment, etc.). Teachers can manually set the students to receive bonus points based on the system's intelligently recommended student list.

[0095] Step 8: Go to the final results announcement page to view the group rankings and the students who received bonus points for each award.

[0096] Step 9: The system generates a test schedule review poster, which teachers can download. For example, Figure 8 This application provides an example of an interface diagram for a test schedule review poster, as shown in the embodiment of the present application. Figure 8 The image shown is a schematic diagram of the interface for the generated test schedule review posters for each group.

[0097] Therefore, the candidate user recommendation method provided in this application can quickly and reasonably generate a list of candidate students, reduce the workload of teachers, and shorten the time for teachers to select award-winning students, thereby achieving fairness and reasonableness in recommending candidate award-winning students.

[0098] Optionally, before receiving a processing request for an award to be recommended, the method further includes:

[0099] Receive a display tag request, and display tags for multiple application scenarios according to the display tag request;

[0100] In response to the selection operation of the second type of user, a processing request is generated containing tags of the target scenario to be applied; the selection operation is used to determine the tag of the target scenario to be applied from the tags of the multiple application scenarios.

[0101] In this embodiment of the application, the system can display a series of tag options for the second type of user to select. After the second type of user selects the corresponding tag option based on the terminal device, the system automatically determines the corresponding target scenario. For example, if the user is unsure which recommendation mode to use in a certain scenario, he / she can select the corresponding tag that matches the scenario, so that the system can automatically determine the target scenario that matches the scenario and the target recommendation mode that matches the target scenario.

[0102] For example, upon receiving a request to display a label, the system can display labels such as "week", "day", "year", "test score", "overall performance", "best", and "worst". Further, a second type of user selects any of the above labels based on the terminal device. In response to the second type of user's selection operation, the system generates a processing request containing the label of the target scenario to be applied, such as a processing request user containing the label of the target scenario to be applied, such as "week", "overall performance", and "best".

[0103] Optionally, this application may also allow the second type of user to make a subjective judgment to select the corresponding target recommendation mode, without needing to generate a processing request containing tags of the target scenario to be applied. This application embodiment does not specifically limit this.

[0104] Therefore, the embodiments of this application determine the target scene based on some tags by the system, without requiring a second type of user to make a subjective judgment, which improves the rationality of the selection of the recommendation mode. Each target scene corresponds to a corresponding recommendation mode.

[0105] Optionally, the target recommendation mode includes: subjective recommendation mode, profile analysis mode, and hierarchical analysis mode; obtaining recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, and obtaining target data corresponding to the required first type of user group based on the recommendation rules, including:

[0106] If the target recommendation mode adopted is a subjective recommendation mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the ranking rule, and the target data obtained based on the ranking rule is the test score of the first type of user group.

[0107] If the target recommendation mode adopted is the profile analysis method, then the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are the user profile construction rules, and the target data obtained based on the user profile construction rules are the indicator data of the first type of user group; the indicator data includes information profile data, behavioral profile data and group profile data; the information profile data is data describing the learning ability of the first type of user, the behavioral profile data is data describing the daily behavior of the first type of user, and the group profile data is data describing the corresponding identity of the first type of user;

[0108] If the target recommendation mode adopted is the analytic hierarchy process (AHP) mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the hierarchical model construction rule, and the target data obtained based on the hierarchical model construction rule is the proportion of the indicator data of the first user group; the hierarchical model construction rule is the user profile construction rule based on the analytic hierarchy process.

[0109] For example, if the selected award is first place (the person with the best test score), the target recommendation mode is a subjective recommendation mode. Furthermore, the system can obtain the test scores of all participants in this competition and sort the test scores from high to low to determine the list of winning students.

[0110] If the target recommendation model adopted is the user profile analysis model, then the corresponding recommendation rules are obtained as user profile construction rules. Furthermore, the indicator data of the first type of user group are obtained. Figure 9 This application provides a schematic diagram of a process for obtaining indicator data using user profile construction rules, as shown in the embodiments of this application. Figure 9 As shown, student profiles are created through three dimensions: information profile, behavior profile, and group profile. The corresponding information profile data, behavior profile data, and group profile data are obtained by analyzing the student profiles. The above data may contain different data labels. Specifically, the data required for each data label is statistically analyzed, which is the target data.

[0111] If the data tag corresponding to the information profile data includes competition results, then the information profile data includes: preliminary score, final score, score improvement coefficient, and ranking improvement coefficient. Let the student's preliminary score be M, the final score be N, and the improvement coefficient be P, then the score improvement coefficient is P = ((NM)*100%) / M; let the student's preliminary ranking be m, the final ranking be n, and the improvement coefficient be p, then the ranking improvement coefficient is p = ((nm)*100%) / m.

[0112] The behavioral profile data corresponds to data tags with tag comments, so the behavioral profile data includes: praise tags, tags for improvement, classroom performance, homework completion, etc.; the group profile data corresponds to data tags with student personality and mentor-mentee relationship, so the group profile data includes: whether the person is a mentor / group leader, the number of mentees, etc.

[0113] If the target recommendation model adopted is the analytic hierarchy process (AHP), then the corresponding recommendation rules are hierarchical model construction rules. Further, the weight of the indicator data for the first user group is obtained. This weight is the importance of the indicator data obtained based on the scaling method for selecting the final candidate users, corresponding to the ratio between the scales corresponding to the two indicator data. The scaling method is a predefined scale, and each scale corresponds to a specific weight, as shown in Table 1.

[0114] Table 1

[0115]

[0116]

[0117] Optionally, the hierarchical model construction rules can be user profile construction rules based on the analytic hierarchy process, or user profile construction rules based on entropy, factor analysis, etc. This application embodiment does not specifically limit them.

[0118] Therefore, the embodiments of this application can provide multiple methods for recommendation, which are highly flexible, offer many choices, and can meet different application scenarios.

[0119] Optionally, determining candidate users from the first user group based on the recommendation rules and the target data includes:

[0120] When the recommendation rule is determined to be a ranking rule and the target data is the test scores of the first type of user group, the test scores are ranked according to a specific order algorithm to obtain the test scores in the top N positions, and the first type of users corresponding to the test scores in the top N positions are determined as candidate users; where N is a positive integer greater than or equal to 1.

[0121] When the recommendation rule is determined to be a user profile construction rule and the target data is the indicator data of the first type of user group, a predefined algorithm is used to calculate the score value of each first type of user corresponding to the indicator data, and candidate users are determined from the first type of user group based on the score value.

[0122] When the recommendation rule is determined to be a hierarchical model construction rule, and the target data is the proportion of indicator data of the first type of user group, then for each first type of user, the proportion corresponding to the first type of user is divided into multiple dimensions based on the identifier of the award to be recommended, a judgment matrix is ​​constructed based on the multiple dimensions of proportion, and the weight vector of the first type of user is calculated using the hierarchical analysis method and the judgment matrix. Based on the calculated weight vector corresponding to the first type of user group, candidate users are determined from the first type of user group.

[0123] In this application embodiment, the specific order algorithm may refer to an algorithm that sorts data from largest to smallest, smallest to largest, or by subtracting the first two data. This application embodiment does not specifically limit the specific algorithm, but it can be used for sorting, and the candidate users can be determined based on the sorting.

[0124] The predefined algorithm can refer to a predefined algorithm used to calculate the rating of the first type of user. It can be an average algorithm, a weighted average algorithm, a summation algorithm, etc. This application does not specifically limit this. Taking the average algorithm as an example, the system calculates the profile score of each user proportionally based on the obtained indicator data, and sorts them from high to low according to the profile score to obtain candidate users.

[0125] For example, when establishing rules for building a hierarchical model, the following three issues need to be considered:

[0126] 1. What is the target award you want to recommend (the award to be recommended)? (Target level)

[0127] 2. What are the recommended criteria or indicators? What are the criteria for evaluating student performance? (Criteria level)

[0128] 3. What are the possible solutions for obtaining candidate users corresponding to the target award? (Solution layer)

[0129] Furthermore, hierarchical model construction rules are built based on the target layer, criterion layer, and solution layer. Figure 10 This is a schematic diagram of a hierarchical model construction rule provided in an embodiment of this application, such as... Figure 10 As shown, taking the best-performing student this week as an example, the target layer is: selecting recommended award candidates; the criteria layer is: competition results, final results, praise tag score, need-to-improve tag score, and whether they are mentors / group leaders; the solution layer is: student 1, student 2, and student 3. Furthermore, the weight vector of the second layer (criteria layer) to the first layer (target layer) and the weight vector of the third layer (solution layer) to each element (criteria) of the second layer are calculated using a vector algorithm. Based on the weight vector, the candidate user is determined, and then the hierarchical model construction rules are constructed.

[0130] In this step, a judgment matrix is ​​constructed based on the weight values ​​of multiple dimensions, including the following steps: n factors (indicators) are determined for the criterion layer, C = {c1, c2, c3, ..., cn}, such as preliminary scores, final scores, and commendation label scores. Further, the influence of these factors on the target layer is compared to determine their weight value relative to a specific criterion within that layer, using a... ij The judgment matrix represents the comparison result of factor ci relative to factor cj.

[0131]

[0132] By decomposing the hierarchical indicators, the constructed judgment matrices are paired up to form quantitative weights between indicators, as shown in Table 2. Table 2 shows the data corresponding to the judgment matrices of the completed criterion layer.

[0133] Table 2

[0134]

[0135] It should be noted that, since it is difficult to compare all factors, we first compare the indicators in pairs. The data (weight values) in Table 2 are for illustrative purposes only. This application does not impose any specific limitations on this, and the importance of each pair of indicators can be modified.

[0136] Therefore, the embodiments of this application can be applied to a variety of scenarios and offer diverse recommendation modes. In particular, under the analytic hierarchy process (AHP) mode, the system can create a profile of each student based on various indicators such as their competition results, mentor-mentee relationship within the group, and the addition or subtraction of points by comment tags. Based on the hierarchical model, the system constructs rules to obtain a judgment matrix of candidate user weights and calculates the weight vector of the candidate users according to the judgment matrix, thereby determining the candidate users. This approach can comprehensively consider multiple aspects of the user's performance, making the recommendation of award-winning users more equitable.

[0137] Optionally, the analytic hierarchy process (AHP) includes a hierarchical single ranking method and a hierarchical overall ranking method; the weight vector includes a first weight vector and a second weight vector; the weight vector of the first type of user is calculated using the AHP and the judgment matrix, and candidate users are determined from the first type of user group based on the calculated weight vector corresponding to the first type of user group, including:

[0138] The first weight vector for each dimension is calculated using the hierarchical single sorting method and the judgment matrix.

[0139] Based on the proportion of the indicator data in the first type of users, the dimensions are divided to obtain multiple judgment matrices, and the hierarchical total ranking method and the multiple judgment matrices are used to calculate the second weight vector of the first type of users.

[0140] Calculate the product of the first weight vector and the second weight vector corresponding to each user of the first type to obtain the weight value of the first user group, and determine candidate users from the first user group based on the weight value.

[0141] In this embodiment, the hierarchical single-sorting method refers to a method that compares all elements in the current layer pairwise for a certain element in the previous layer and performs hierarchical sorting, that is, it calculates the weight of each indicator based on the constructed judgment matrix. The specific steps are as follows:

[0142] (1) Normalize the column vectors of each column of the judgment matrix.

[0143] For example, take the judgment matrix A as an example. A is a third-order matrix. Perform column vector normalization, that is, divide the number of each column by the column sum, such as 1÷(1+1 / 2+1 / 6) to get 0.6.

[0144] (2) Sum the elements of the normalized column vector by row, i.e. perform row sum normalization.

[0145] The row sum normalization involves adding the numbers in each row and dividing by the number of rows. For the first row, this is the sum of 0.6 + 0.615 + 0.545 divided by the number of rows (3), resulting in 0.587. This process is repeated to obtain the weight vector ω. See the following formula:

[0146]

[0147] (3) Standardize the summation result.

[0148] Specifically, according to the maximum eigenvalue method, let the eigenvalue of matrix A be λ, and let Aω = λω, where ω is the eigenvector corresponding to λ.

[0149] The ω calculated using step (2) is (0.587, 0.324, 0.089). T Multiplying by a third-order matrix A, the resulting products [1.769, 0.974, 0.268] are divided by the order of the matrix, such as by 3, to obtain the value of λ. See the following formula:

[0150]

[0151] The hierarchical overall ranking method refers to a method that, in order to obtain the combined weights of elements at a certain level in a hierarchical structure with respect to the overall objective and their mutual influence with elements at the upper level, calculates the combined weights of the elements at that level using the results obtained from the hierarchical single ranking method.

[0152] In this step, the first weight vector for each dimension is calculated using the hierarchical single sorting method and the judgment matrix corresponding to Table 2: ω=(2.112, 2.787, 1.021, 0.66, 0.252). T The largest eigenvalue λ = 5.183.

[0153] Understandably, after calculating the first weight vector of the second layer (criteria layer) to the first layer (target layer), what is needed next is to calculate the second weight vector of the third layer (scheme layer) to each element (criteria) of the second layer.

[0154] Specifically, based on the proportion of indicator data among the first type of users, multiple judgment matrices are obtained by dividing the data into dimensions. Figure 10 For example, if there are three schemes P1, P2, and P3, then based on the preliminary scores, final scores, praise tags, improvement tags, and mentor-mentee identities of the students in the three schemes, five third-order matrices can be formed.

[0155] The first 3x3 matrix is ​​formed by comparing the preliminary scores of P1 with those of P2 and P3, as shown in Table 3:

[0156] Table 3

[0157] Preliminary results Student 1 Student 2 Student 3 Student 1 1 1 / 3 1 / 5 Student 2 3 1 1 / 3 Student 3 5 3 1

[0158] Similarly, using the final results, praise tags, improvement tags, and mentor-mentee relationships, the remaining four third-order matrices are constructed, as shown in Tables 4-7 respectively:

[0159] Table 4

[0160] Final Results Student 1 Student 2 Student 3 Student 1 1 1 / 3 1 / 5 Student 2 3 1 1 / 3 Student 3 5 3 1

[0161] Table 5

[0162] Praise label score Student 1 Student 2 Student 3 Student 1 1 3 7 Student 2 1 / 3 1 3 Student 3 1 / 7 1 / 3 1

[0163] Table 6

[0164] Tag scores to be improved Student 1 Student 2 Student 3 Student 1 1 2 2 Student 2 1 / 2 1 1 Student 3 1 / 2 3 1

[0165] Table 7

[0166] Is he a master or team leader? Student 1 Student 2 Student 3 Student 1 1 1 / 2 1 / 2 Student 2 2 1 1 Student 3 2 1 1

[0167] The five judgment matrices generated by Tables 3-7 are used to calculate the second weight vector of the third layer (scheme layer) for each element of the second layer (criteria layer) using the hierarchical single sorting method. From this, the maximum eigenvalue and the corresponding eigenvector of each attribute can be obtained.

[0168] The results corresponding to the first weight vector and the second weight vector are shown in Table 8 and Table 9, respectively:

[0169] Table 8

[0170]

[0171]

[0172] Table 9

[0173]

[0174] Based on the results in Tables 8 and 9, the weight value of P1 to the overall goal can be calculated. That is, by multiplying the preliminary and final scores, praise labels, improvement labels, and mentor-apprentice identity weight values ​​of P1, P2, and P3 by the weight vector of the fifth-order matrix A, we can obtain the weight value at the target layer.

[0175] For example, the weight of student A in P1 is: 0.105×2.112+0.669×1.021+0.5×0.66+0.105×2.787+0.2×0.252=1.5778.

[0176] Similarly, the weights of students P2 and P3 can be calculated to be 1.7777 and 3.4762, respectively. By comparison, 1.5778 < 1.7777 < 3.4762, therefore student 3 should be recommended as the best performing student this week.

[0177] It should be noted that in the embodiments of this application, the analytic hierarchy process (AHP) mode uses student user profiling and the AHP. The AHP is a systematic and hierarchical analysis method that combines qualitative and quantitative approaches. It can treat a complex goal-oriented decision problem, such as selecting the best-performing student of the week, as a system, decompose the goal into multiple goals or criteria, and perform relevant calculations through qualitative indicators to serve as a systematic method for optimizing decision-making based on multiple options. In other words, it is a decision-making method that decomposes elements always related to decision-making into levels such as goals, criteria, and options, and then performs qualitative and quantitative analysis on this basis.

[0178] Therefore, the embodiments of this application obtain the recommendation weight of each first-class user through hierarchical single ranking method and hierarchical total ranking method, clearly present the relationship between each layer, each criterion and each element, simplify the evaluation procedure, the calculation process is simple and easy to understand, easy to program, and can achieve fair and reasonable recommendation of candidate users.

[0179] Optionally, the method further includes:

[0180] Perform a consistency check on the judgment matrix;

[0181] If the judgment matrix fails the consistency test, the weight values ​​in the judgment matrix are adjusted until the judgment matrix passes the consistency test.

[0182] In this embodiment of the application, due to the complexity of objective things and the ambiguity when judging and comparing things, it is difficult to construct a completely consistent comparison matrix. Therefore, it is necessary to perform a consistency check on the judgment matrix. The consistency check means that the judgment matrix is ​​allowed to have a certain range of inconsistency.

[0183] Specifically, the consistency check of the judgment matrix includes the following steps:

[0184] (1) Calculate the consistency index CI of the judgment matrix according to the predefined formula. The formula for the consistency index is as follows:

[0185]

[0186] When CI = 0, it indicates that the judgment matrix has complete consistency; when CI is close to 0, it indicates that the judgment matrix has satisfactory consistency; the larger CI is, the more serious the inconsistency of the judgment matrix.

[0187] (2) When checking whether the judgment matrix has satisfactory consistency, it is also necessary to compare CI and the random consistency index RI to obtain the test coefficient CR. The formula for calculating CR is as follows:

[0188]

[0189] The random one-time index RI can be found in Table 10. If CR < 0.1, it means that the judgment matrix has passed the consistency test; otherwise, it does not have satisfactory consistency, i.e., it has not passed the consistency test.

[0190] Table 10

[0191] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

[0192] For example, taking the judgment matrix corresponding to Table 2 as an example, CI = (5.183-5) / (5-1) = 0.045, RI = 1.12, then CR = 0.045 / 1.12 = 0.040 < 0.1, indicating that the judgment matrix passes the consistency test.

[0193] Similarly, the summary results of the scheme layer judgment matrix are shown in Table 11:

[0194] Table 11

[0195]

[0196]

[0197] For example, when constructing the analytic hierarchy process, a consistency check is required. Figure 11 A flowchart illustrating an application of the Analytic Hierarchy Process (AHP) pattern is provided for an embodiment of this application; as shown below. Figure 11 As shown, it includes the following steps:

[0198] Step A: Determine the indicator system and construct the judgment matrix, that is, construct the judgment matrix using the proportion of indicator data of the first type of user group, and then proceed to Step B.

[0199] Step B: Calculate the first weight vector using the hierarchical single sorting method and the judgment matrix, and perform a consistency check on the judgment matrix. If the consistency check is satisfied, proceed to step C; otherwise, proceed to step D.

[0200] Step C: Based on the proportion of the indicator data in the first type of users, divide the dimensions to obtain multiple judgment matrices, and use the hierarchical overall ranking method and multiple judgment matrices to calculate the second weight vector. Then, perform a consistency check on the multiple judgment matrices. If the consistency check is satisfied, the analysis result is obtained; otherwise, proceed to step D.

[0201] Step D: Adjust the judgment matrix, that is, modify the weight values ​​in the judgment matrix. The specific values ​​to be modified are not limited in this embodiment of the application, but are modified according to the application scenario.

[0202] Therefore, the embodiments of this application use consistency checks to ensure the accuracy of the weight vector, thereby improving the rationality of constructing the judgment matrix.

[0203] It should be noted that the data in the above tables, such as the data in Tables 1-11, are only illustrative examples. The embodiments of this application do not impose specific limitations on them, and they can be changed according to different application scenarios.

[0204] Optionally, the method further includes:

[0205] In response to the touch operation of the second type of user, the award status of the candidate user is displayed, and a share link is generated based on the award status;

[0206] Obtain the contact information of the candidate user, and send the sharing link to the terminal device corresponding to the candidate user based on the contact information.

[0207] In this embodiment of the application, the terminal device corresponding to the candidate user may refer to the candidate user's own terminal device or the terminal device of a user associated with the candidate user, such as a student's or parent's mobile phone. This embodiment of the application does not make specific limitations in this regard.

[0208] For example, with Figure 8 For example, after generating the test schedule review posters for each group, a sharing link for the poster is generated and sent to the terminal devices corresponding to the members in each group. The poster includes the award status and ranking of the candidate users, etc. This application embodiment does not specifically limit the content contained in the poster.

[0209] Therefore, the embodiments of this application can make it easier for more users to know about the award status of the first type of users in a timely manner, thereby increasing the enthusiasm of the first type of users.

[0210] In conjunction with the above embodiments, Figure 12 A flowchart illustrating a specific candidate user recommendation method provided in this application embodiment; as follows: Figure 12 As shown, taking a teaching scenario as an example, the specific execution steps of the candidate user recommendation method are as follows:

[0211] Step a: Open the pop-up window for setting special rewards and punishments, which displays the following awards to be recommended: Award 1: Distinguished (the best performer this week), Award 2: Never-ending (the most improved person), Award 3: Disappointing (the person who did very poorly on the test). The following example is Distinguished (the best performer this week).

[0212] Step b: Select the method for recommending candidate students. Teachers can choose a specific recommendation mode for award-winning students, or the system can automatically determine the recommendation mode. Figure 2 The illustrated embodiment.

[0213] Step c: If the subjective recommendation mode is adopted, the system will sort the list of candidate award-winning students from high to low according to the competition results, and the teacher can subjectively select the award-winning students according to the sorting.

[0214] Step d: If the profiling analysis method is adopted, the system calculates the order of candidate award-winning students based on the student profile results. That is, the system analyzes student profiles through three dimensions based on historical data: information profile, behavior profile, and group profile. Furthermore, the following key tags are obtained through the student profiles: preliminary score, final score, praise tag score, need-to-improve tag score, and whether the student is a mentor / group leader. Based on the key tags obtained from the student profiles, the system calculates the profile score for each student in proportion. The system sorts the profiles from high to low according to the profile scores to obtain the list of candidate award-winning students. Teachers then select from the list of recommended candidate award-winning students.

[0215] Step e: If the profiling analysis + hierarchical analysis method is adopted, the system will derive the order of recommended students based on the student profile and hierarchical analysis method. That is, based on the student profile tags obtained in step d, the system uses hierarchical analysis method to decompose the elements always related to decision-making into levels such as goals, criteria, and solutions. On this basis, qualitative and quantitative analysis is carried out to provide teachers with a list of candidates for special awards. Teachers will then select from the list of recommended candidates for awards.

[0216] Therefore, this application provides three recommendation modes for determining candidate users, which greatly improves the flexibility of the application. In teaching scenarios, it makes it easier for teachers to give students corresponding awards more quickly and reasonably during the teaching process, and at the same time, the appropriate recommendation method can be selected according to changes in the teaching scenario.

[0217] In the foregoing embodiments, the candidate user recommendation method provided by the embodiments of this application has been described. To implement the functions of the methods provided by the embodiments of this application, the electronic device serving as the execution subject may include hardware structures and / or software modules, implementing the above functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular function is executed in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0218] For example, Figure 13 This is a schematic diagram of a candidate user recommendation device provided in an embodiment of this application. The device includes: a receiving module 1301, a first determining module 1302, and a second determining module 1303. The receiving module 1301 is used to receive a processing request for an award to be recommended. The processing request for the award to be recommended includes: the type to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected.

[0219] The first determining module 1302 is used to determine the target scene corresponding to the tag of the target scene according to the processing request of the award to be recommended, and to obtain the target recommendation mode matching the target scene;

[0220] The second determining module 1303 is used to adopt the target recommendation mode, obtain recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, obtain target data corresponding to the first type of user group based on the recommendation rules, and determine candidate users from the first type of user group according to the recommendation rules and the target data.

[0221] Optionally, before receiving a processing request for an award to be recommended, the device further includes a tag selection module, the tag selection module being used to:

[0222] Receive a display tag request, and display tags for multiple application scenarios according to the display tag request;

[0223] In response to the selection operation of the second type of user, a processing request is generated containing tags of the target scenario to be applied; the selection operation is used to determine the tag of the target scenario to be applied from the tags of the multiple application scenarios.

[0224] Optionally, the target recommendation mode includes: subjective recommendation mode, profile analysis mode, and hierarchical analysis mode; the second determining module 1303 includes an acquisition unit and a determining unit, the acquisition unit being used for:

[0225] If the target recommendation mode adopted is a subjective recommendation mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the ranking rule, and the target data obtained based on the ranking rule is the test score of the first type of user group.

[0226] If the target recommendation mode adopted is the profile analysis method, then the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are the user profile construction rules, and the target data obtained based on the user profile construction rules are the indicator data of the first type of user group; the indicator data includes information profile data, behavioral profile data and group profile data; the information profile data is data describing the learning ability of the first type of user, the behavioral profile data is data describing the daily behavior of the first type of user, and the group profile data is data describing the corresponding identity of the first type of user;

[0227] If the target recommendation mode adopted is the analytic hierarchy process (AHP) mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the hierarchical model construction rule, and the target data obtained based on the hierarchical model construction rule is the proportion of the indicator data of the first user group; the hierarchical model construction rule is the user profile construction rule based on the analytic hierarchy process.

[0228] Optionally, the determining unit is used for:

[0229] When the recommendation rule is determined to be a ranking rule and the target data is the test scores of the first type of user group, the test scores are ranked according to a specific order algorithm to obtain the test scores in the top N positions, and the first type of users corresponding to the test scores in the top N positions are determined as candidate users; where N is a positive integer greater than or equal to 1.

[0230] When the recommendation rule is determined to be a user profile construction rule and the target data is the indicator data of the first type of user group, a predefined algorithm is used to calculate the score value of each first type of user corresponding to the indicator data, and candidate users are determined from the first type of user group based on the score value.

[0231] When the recommendation rule is determined to be a hierarchical model construction rule, and the target data is the proportion of indicator data of the first type of user group, then for each first type of user, the proportion corresponding to the first type of user is divided into multiple dimensions based on the identifier of the award to be recommended, a judgment matrix is ​​constructed based on the multiple dimensions of proportion, and the weight vector of the first type of user is calculated using the hierarchical analysis method and the judgment matrix. Based on the calculated weight vector corresponding to the first type of user group, candidate users are determined from the first type of user group.

[0232] Optionally, the analytic hierarchy process includes hierarchical single ranking and hierarchical overall ranking; the weight vector includes a first weight vector and a second weight vector; the determining unit is specifically used for:

[0233] The first weight vector for each dimension is calculated using the hierarchical single sorting method and the judgment matrix.

[0234] Based on the proportion of the indicator data in the first type of users, the dimensions are divided to obtain multiple judgment matrices, and the hierarchical total ranking method and the multiple judgment matrices are used to calculate the second weight vector of the first type of users.

[0235] Calculate the product of the first weight vector and the second weight vector corresponding to each user of the first type to obtain the weight value of the first user group, and determine candidate users from the first user group based on the weight value.

[0236] Optionally, the device further includes an inspection module, the inspection module being used for:

[0237] Perform a consistency check on the judgment matrix;

[0238] If the judgment matrix fails the consistency test, the weight values ​​in the judgment matrix are adjusted until the judgment matrix passes the consistency test.

[0239] Optionally, the device further includes a sharing module, the sharing module being used for:

[0240] In response to the touch operation of the second type of user, the award status of the candidate user is displayed, and a share link is generated based on the award status;

[0241] Obtain the contact information of the candidate user, and send the sharing link to the terminal device corresponding to the candidate user based on the contact information.

[0242] The specific implementation principle and effects of the candidate user recommendation device provided in this application embodiment can be found in the relevant descriptions and effects of the above embodiments, and will not be elaborated further here.

[0243] This application also provides a schematic diagram of the structure of an electronic device. Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 14 As shown, the electronic device may include: a processor 1401 and a memory 1402 communicatively connected to the processor; the memory 1402 stores computer execution instructions; the processor 1401 executes the computer execution instructions stored in the memory 1402, causing the processor 1401 to perform the method described in any of the above embodiments.

[0244] The memory 1402 and the processor 1401 can be connected via bus 1403.

[0245] This application also provides a computer-readable storage medium storing computer program execution instructions, which, when executed by a processor, are used to implement the methods described in any of the foregoing embodiments of this application.

[0246] This application also provides a chip for executing instructions, which is used to perform the methods described in any of the foregoing embodiments executed by an electronic device as described in any of the foregoing embodiments of this application.

[0247] This application also provides a computer program product, which includes a computer program that, when executed by a processor, can implement the methods described in any of the foregoing embodiments executed by an electronic device as described in any of the foregoing embodiments of this application.

[0248] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0249] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0250] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0251] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0252] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0253] The memory may include high-speed random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0254] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0255] The aforementioned storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0256] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0257] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. A candidate user recommendation method, characterized in that, The method includes: The system receives a processing request for an award to be recommended. The processing request for the award to be recommended includes: the type of award to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected. Based on the processing request of the award to be recommended, determine the target scene corresponding to the tag of the target scene, and obtain the target recommendation mode that matches the target scene; Using the target recommendation mode, the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are obtained, and the target data corresponding to the first type of user group is obtained based on the recommendation rules. Candidate users are determined from the first type of user group according to the recommendation rules and the target data. The target recommendation mode includes: subjective recommendation mode, profile analysis mode, and hierarchical analysis mode; obtaining recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, and obtaining target data corresponding to the required first type of user group based on the recommendation rules, including: If the target recommendation mode adopted is a subjective recommendation mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the ranking rule, and the target data obtained based on the ranking rule is the test score of the first type of user group. If the target recommendation mode adopted is the profile analysis method, then the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are the user profile construction rules, and the target data obtained based on the user profile construction rules are the indicator data of the first type of user group; the indicator data includes information profile data, behavioral profile data and group profile data; the information profile data is data describing the learning ability of the first type of user, the behavioral profile data is data describing the daily behavior of the first type of user, and the group profile data is data describing the corresponding identity of the first type of user; If the target recommendation mode adopted is the analytic hierarchy process (AHP) mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the hierarchical model construction rule, and the target data obtained based on the hierarchical model construction rule is the proportion of the indicator data of the first user group; the hierarchical model construction rule is the user profile construction rule based on the analytic hierarchy process.

2. The method according to claim 1, characterized in that, Before receiving a processing request for an award to be recommended, the method further includes: Receive a display tag request, and display tags for multiple application scenarios according to the display tag request; In response to the selection operation of the second type of user, a processing request is generated containing tags of the target scenario to be applied; the selection operation is used to determine the tag of the target scenario to be applied from the tags of the multiple application scenarios.

3. The method according to claim 1, characterized in that, Based on the recommendation rules and the target data, candidate users are determined from the first user group, including: When the recommendation rule is determined to be a ranking rule and the target data is the test scores of the first type of user group, the test scores are ranked according to a specific order algorithm to obtain the test scores in the top N positions, and the first type of users corresponding to the test scores in the top N positions are determined as candidate users; where N is a positive integer greater than or equal to 1. When the recommendation rule is determined to be a user profile construction rule and the target data is the indicator data of the first type of user group, a predefined algorithm is used to calculate the score value of each first type of user corresponding to the indicator data, and candidate users are determined from the first type of user group based on the score value. When the recommendation rule is determined to be a hierarchical model construction rule, and the target data is the proportion of indicator data of the first type of user group, then for each first type of user, the proportion corresponding to the first type of user is divided into multiple dimensions based on the identifier of the award to be recommended, a judgment matrix is ​​constructed based on the multiple dimensions of proportion, and the weight vector of the first type of user is calculated using the hierarchical analysis method and the judgment matrix. Based on the calculated weight vector corresponding to the first type of user group, candidate users are determined from the first type of user group.

4. The method according to claim 3, characterized in that, The analytic hierarchy process (AHP) includes a hierarchical single ranking method and a hierarchical overall ranking method; the weight vector includes a first weight vector and a second weight vector; the weight vector of the first type of user is calculated using the AHP and the judgment matrix, and candidate users are determined from the first type of user group based on the calculated weight vector, including: The first weight vector for each dimension is calculated using the hierarchical single sorting method and the judgment matrix. Based on the proportion of the indicator data in the first type of users, the dimensions are divided to obtain multiple judgment matrices, and the hierarchical total ranking method and the multiple judgment matrices are used to calculate the second weight vector of the first type of users. Calculate the product of the first weight vector and the second weight vector corresponding to each user of the first type to obtain the weight value of the first user group, and determine candidate users from the first user group based on the weight value.

5. The method according to claim 4, characterized in that, The method further includes: Perform a consistency check on the judgment matrix; If the judgment matrix fails the consistency test, the weight values ​​in the judgment matrix are adjusted until the judgment matrix passes the consistency test.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: In response to the touch operation of the second type of user, the award status of the candidate user is displayed, and a share link is generated based on the award status; Obtain the contact information of the candidate user, and send the sharing link to the terminal device corresponding to the candidate user based on the contact information.

7. A candidate user recommendation device, characterized in that, The device includes: The receiving module is used to receive processing requests for awards to be recommended. The processing requests for awards to be recommended include: the type to be recommended, the identifier of the award to be recommended, the tag of the target scenario to be applied, and the first type of user group to be selected. The first determining module is used to determine the target scene corresponding to the tag of the target scene according to the processing request of the award to be recommended, and to obtain the target recommendation mode matching the target scene; The second determining module is used to adopt the target recommendation mode, obtain recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended, obtain target data corresponding to the first type of user group based on the recommendation rules, and determine candidate users from the first type of user group according to the recommendation rules and the target data. The target recommendation mode includes: subjective recommendation mode, profile analysis mode, and hierarchical analysis mode; the second determining module includes an acquisition unit, the acquisition unit being used for: If the target recommendation mode adopted is a subjective recommendation mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the ranking rule, and the target data obtained based on the ranking rule is the test score of the first type of user group. If the target recommendation mode adopted is the profile analysis method, then the recommendation rules corresponding to the identifier of the award to be recommended and the type to be recommended are the user profile construction rules, and the target data obtained based on the user profile construction rules are the indicator data of the first type of user group; the indicator data includes information profile data, behavioral profile data and group profile data; the information profile data is data describing the learning ability of the first type of user, the behavioral profile data is data describing the daily behavior of the first type of user, and the group profile data is data describing the corresponding identity of the first type of user; If the target recommendation mode adopted is the analytic hierarchy process (AHP) mode, then the recommendation rule corresponding to the identifier of the award to be recommended and the type to be recommended is the hierarchical model construction rule, and the target data obtained based on the hierarchical model construction rule is the proportion of the indicator data of the first user group; the hierarchical model construction rule is the user profile construction rule based on the analytic hierarchy process.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

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