Recommended method, device, simulation system, computer device and storage medium

Abstract

The present application relates to a recommendation method, device, simulation system, computer equipment and storage medium, and relates to the fields of big data and artificial intelligence. The method comprises: determining a reference employee of a target employee from a relationship network diagram, wherein the reference employee and the target employee are in the same job sequence, and the reference employee comprises a first reference employee and / or a second reference employee. For any reference employee, the similarity between the target employee and the reference employee is determined based on the first set of employees who have a job hierarchy relationship with the target employee in the similarity of the relationship network diagram, and / or the second set of employees who have a job hierarchy relationship with the reference employee in the relationship network diagram. Based on the similarity between the target employee and each reference employee, and the attention of each reference employee to the content to be recommended, the interest of the target employee in the content to be recommended is determined, and based on the interest, the content to be recommended is recommended to the target employee. The use of this method can improve the accuracy of recommendation.

Description

Technical Field

[0001] The present application relates to the fields of computer software information technology, big data, and artificial intelligence technology, and in particular to a recommendation method, apparatus, simulation system, computer equipment, and storage medium. Background Art

[0002] With the development of computer software information technology, big data and artificial intelligence technology, training and examination simulation systems have emerged, in which users can complete training and examinations online.

[0003] In some application scenarios, the content and difficulty of training and examinations are different for users of different levels or positions. Therefore, in the training-level examination simulation system, how to recommend suitable training and examination content to users in a targeted manner is a focus of attention in the industry. Summary of the invention

[0004] Based on this, it is necessary to provide a recommendation method, device, simulation system, computer equipment and storage medium that can accurately recommend training and examination content to users in response to the above technical problems.

[0005] In a first aspect, the present application provides a recommendation method, the method comprising:

[0006] Determine reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0007] For any of the reference employees, determine the similarity between the target employee and the reference employee based on a first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or a second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee;

[0008] Determining the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended;

[0009] The content to be recommended is recommended to the target employee according to the interest level.

[0010] In one of the embodiments, in the relationship network diagram, there is a directed edge between the reference employee and the target employee, the directed edge between the first reference employee and the target employee is a bidirectional edge, and the directed edge between the second reference employee and the target employee is a unidirectional edge pointing to the second reference employee, the bidirectional edge is used to represent the same job level relationship, and the unidirectional edge is used to represent the upper and lower job level relationship.

[0011] In one embodiment, determining the similarity between the target employee and the reference employee based on a first set of employees having a job hierarchical relationship with the target employee in the relationship network diagram and / or a second set of employees having a job hierarchical relationship with the reference employee in the relationship network diagram includes:

[0012] Determine a third employee set and a fourth employee set according to the first employee set and the second employee set, wherein the third employee set is the intersection of the first employee set and the second employee set, and the fourth employee set is the union of the first employee set and the second employee set;

[0013] The similarity between the target employee and the reference employee is determined according to the third employee set and the fourth employee set.

[0014] In one embodiment, the second employee set is a set of employees with the same job level as the reference employee; when the reference employee is the first reference employee, the first employee set is the set of the first reference employees; when the reference employee is the second reference employee, the first employee set is the set of the second reference employees.

[0015] In one embodiment, determining the interest of the target employee in the content to be recommended based on the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended includes:

[0016] Obtaining a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1;

[0017] determining a first interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the first reference employees, the first weight, and the attention level of each of the first reference employees for the content to be recommended;

[0018] determining a second interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the second reference employees, the second weight, and the attention level of each of the second reference employees for the content to be recommended;

[0019] The interest degree of the target employee in the content to be recommended is determined according to the first interest degree and the second interest degree.

[0020] In one embodiment, the method further comprises:

[0021] Determining whether the target employee meets the weight adjustment condition according to the business data of the target employee;

[0022] When the target employee meets the weight adjustment condition, the second weight is increased and adjusted;

[0023] Among them, the business data includes years of employment, test results and training progress.

[0024] In a second aspect, the present application further provides a recommendation device, the device comprising:

[0025] A first determination module is used to determine a reference employee of the target employee from a relationship network diagram, wherein the reference employee and the target employee are in the same job sequence, and the reference employee includes a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0026] A second determination module is used to determine the similarity between the target employee and the reference employee based on a first set of employees having a job hierarchy relationship with the target employee in the relationship network diagram, and / or a second set of employees having a job hierarchy relationship with the reference employee in the relationship network diagram;

[0027] A third determination module is used to determine the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention of each of the reference employees to the content to be recommended;

[0028] A recommendation module is used to recommend the content to be recommended to the target employee according to the interest level.

[0029] In one of the embodiments, in the relationship network diagram, there is a directed edge between the reference employee and the target employee, the directed edge between the first reference employee and the target employee is a bidirectional edge, and the directed edge between the second reference employee and the target employee is a unidirectional edge pointing to the second reference employee, the bidirectional edge is used to represent the same job level relationship, and the unidirectional edge is used to represent the upper and lower job level relationship.

[0030] In one embodiment, the second determining module is further configured to:

[0031] Determine a third employee set and a fourth employee set according to the first employee set and the second employee set, wherein the third employee set is the intersection of the first employee set and the second employee set, and the fourth employee set is the union of the first employee set and the second employee set;

[0032] The similarity between the target employee and the reference employee is determined according to the third employee set and the fourth employee set.

[0033] In one embodiment, the second employee set is a set of employees with the same job level as the reference employee; when the reference employee is the first reference employee, the first employee set is the set of the first reference employees; when the reference employee is the second reference employee, the first employee set is the set of the second reference employees.

[0034] In one embodiment, the third determination module is further used for:

[0035] Obtaining a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1;

[0036] determining a first interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the first reference employees, the first weight, and the attention level of each of the first reference employees for the content to be recommended;

[0037] determining a second interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the second reference employees, the second weight, and the attention level of each of the second reference employees for the content to be recommended;

[0038] The interest degree of the target employee in the content to be recommended is determined according to the first interest degree and the second interest degree.

[0039] In one embodiment, the recommendation device further comprises:

[0040] A fourth determination module, used to determine whether the target employee meets the weight adjustment condition according to the business data of the target employee;

[0041] An increasing module, configured to increase and adjust the second weight if the target employee meets the weight adjustment condition;

[0042] Among them, the business data includes years of employment, test results and training progress.

[0043] In a third aspect, the present application also provides a simulation system, the system comprising a management end and a student end; wherein:

[0044] The management terminal is used to store training content and examination content;

[0045] The trainee terminal is used to determine the reference employees of the target employee from the relationship network diagram, determine the similarity between the target employee and the reference employees, and determine the interest of the target employee in the content to be recommended based on the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended, wherein the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0046] The trainee terminal is also used to recommend the content to be recommended to the target employee according to the interest level, and the content to be recommended is the training content and the test content stored in the management terminal.

[0047] In a fourth aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0048] Determine reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0049] For any of the reference employees, determine the similarity between the target employee and the reference employee based on a first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or a second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee;

[0050] Determining the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended;

[0051] The content to be recommended is recommended to the target employee according to the interest level.

[0052] In a fifth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0053] Determine reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0054] For any of the reference employees, determine the similarity between the target employee and the reference employee based on a first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or a second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee;

[0055] Determining the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended;

[0056] The content to be recommended is recommended to the target employee according to the interest level.

[0057] In a sixth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Determine reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0059] For any of the reference employees, determine the similarity between the target employee and the reference employee based on a first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or a second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee;

[0060] Determining the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended;

[0061] The content to be recommended is recommended to the target employee according to the interest level.

[0062] The recommendation method, device, simulation system, computer equipment and storage medium described in the embodiment of the present application, the method includes: determining the reference employee of the target employee from the relationship network diagram, the reference employee and the target employee are in the same job sequence, the reference employee includes a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is the upper level of the job level of the target employee. And for any of the reference employees, according to the first set of employees who have a job level relationship with the target employee in the similarity of the relationship network diagram, and / or the second set of employees who have a job level relationship with the reference employee in the relationship network diagram, determine the similarity between the target employee and the reference employee. According to the similarity between the target employee and each of the reference employees, and the attention of each of the reference employees to the content to be recommended, determine the interest of the target employee in the content to be recommended, and recommend the content to be recommended to the target employee based on the interest. Based on the recommendation method, apparatus, simulation system, computer equipment and storage medium provided in the embodiments of the present application, the interest of the target employee in the content to be recommended can be determined through the similarity between the target employee and a first reference employee in the same job sequence and the same job level, and a second reference employee in the same job sequence and one level above the target employee's job level, and the attention paid by the first reference employee and the second reference employee to the content to be recommended, respectively, so as to recommend the content to be recommended to the target employee based on the interest level. That is, when recommending content to the target employee, both the first reference employee and the second reference employee are referred to at the same time, so that the obtained interest level is more accurate, so that the content recommended to the target employee based on the interest level is more in line with the job requirements of the target user, thereby improving the recommendation accuracy of the content to be recommended. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flowchart of a recommended method in one embodiment;

[0064] Figure 2 is a schematic diagram of a relationship network diagram in one embodiment;

[0065] Figure 3 A schematic flow chart of a recommended method in another embodiment;

[0066] Figure 4A schematic flow chart of a recommended method in another embodiment;

[0067] Figure 5 A schematic flow chart of a recommended method in another embodiment;

[0068] Figure 6 is a structural block diagram of a recommended device in an embodiment;

[0069] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0071] In one embodiment, Figure 1 As shown, a recommended method is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0072] Step 102, determine the reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee.

[0073] In the embodiment of the present application, the relationship network diagram can represent the business relationship between various employees. The relationship network diagram includes company employees and the job sequence and job level of the employees. The job sequence represents the department or position of the employee. Employees in different job sequences are responsible for different work businesses. For example, employees in job sequence A are responsible for handling the customer's storage business, and employees in job sequence B are responsible for handling the customer's loan business. The job level represents the level of the employee's position, and employees in different job levels of the same job sequence have a superior-subordinate relationship. For example, among the employees in job sequence A, the manager level employee is the upper level of the ordinary employee, and the manager level employee is the upper level of the manager level employee.

[0074] It should be noted that the relationship network diagram in the embodiment of the present application can be a relationship network diagram representing the employees of the entire company, or it can be a relationship network diagram representing employees in a certain job sequence. The scope represented by the relationship network diagram in the embodiment of the present application is not specifically limited.

[0075] In an embodiment of the present application, the reference employees of the target employee can be determined from the relationship network diagram. The target employee is the user or object to be recommended content, and the reference employee is an employee who is in the same job sequence as the target employee and whose job level is the same level or the upper level of the job level of the target employee, that is, the reference employee includes a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is the upper level of the job level of the target employee.

[0076] It should be noted that when there is no employee with the same position level as the target employee in the position sequence, the reference employees only include the second reference employee; when there is no employee with a position level one level above the target employee in the position sequence, the reference employees only include the first reference employee.

[0077] For example, in job sequence A there are employees A, B, C, 1 and 2, among which, the job levels of employees A, B and C are all ordinary employees, and the job levels of employees 1 and 2 are managers. When employee A is the target employee, the reference employees include the first reference employees: employees B and C, and the second reference employees: employees 1 and 2.

[0078] Step 104, for any reference employee, determine the similarity between the target employee and the reference employee based on the first set of employees having a job hierarchy relationship with the target employee in the relationship network diagram, and / or the second set of employees having a job hierarchy relationship with the reference employee in the relationship network diagram.

[0079] In the embodiment of the present application, the similarity is used to characterize the overlap between the target employee's work and the reference employee's work. The higher the similarity, the higher the overlap between the target employee's work and the reference employee's work, that is, the closer the work content is, and thus the training content and the examination content should be closer.

[0080] In this embodiment, the similarity between the target employee and the reference employee is determined based on the first set of employees who have a job hierarchy relationship with the target employee in the relationship network diagram, and / or the second set of employees who have a job hierarchy relationship with the reference employee in the relationship network diagram, wherein, when the reference employee is the first reference employee, the first set of employees who have a job hierarchy relationship with the target employee includes: employees whose job hierarchy is the same as that of the target employee; when the reference employee is the second reference employee, the first set of employees who have a job hierarchy relationship with the target employee includes: employees whose job hierarchy is one level above that of the target employee; and having a job hierarchy relationship with the reference employee only means that the job hierarchy is the same as that of the reference employee.

[0081] Still taking the above example, assuming that the target employee is employee A, for employee B, employee B is the first reference employee, then the first set of employees having a job hierarchy relationship with employee A is {employee B, employee C}, and the second set of employees having a job hierarchy relationship with employee B is {employee A, employee C}; for employee 1, employee 1 is the second reference employee, then the first set of employees having a job hierarchy relationship with employee A is {employee 1, employee 2}, and the second set of employees having a job hierarchy relationship with employee 1 is {employee 2}.

[0082] After obtaining the first employee set and the second employee set, since the first employee set is a set of users having a rank relationship with the target employee, and the second employee set is a set of users having a rank relationship with the reference user, the similarity between the target user and the reference user can be obtained according to the degree of overlap between the users in the first employee set and the second employee set. The higher the similarity, the higher the business overlap and the higher the similarity of the focused content.

[0083] Step 106 , determining the interest of the target employee in the content to be recommended based on the similarity between the target employee and each reference employee, and the attention paid by each reference employee to the content to be recommended.

[0084] In the embodiment of the present application, the reference employee's attention to the content to be recommended can be used to characterize the reference employee's attention or interest in the recommended content, which can be determined based on the reference employee's browsing frequency of the recommended content. Exemplarily, the reference employee's attention to the content to be recommended is proportional to the reference employee's browsing frequency of the recommended content. For example, if the employee browses the content to be recommended 5 times / month, the employee's attention to the content to be recommended is 25%, and if the employee browses the content to be recommended 10 times / month, the employee's attention to the content to be recommended is 50%.

[0085] It should be noted that the above-mentioned determination of the reference employee's attention to the content to be recommended based on browsing frequency is an example of the present application. In the embodiments of the present application, the basis for determining the reference employee's attention to the content to be recommended is not specifically limited. For example, it can also be determined based on the reference employee's browsing time for the content to be recommended.

[0086] After obtaining the similarity between the target employee and each reference employee, and the attention paid by each reference employee to the content to be recommended, a fusion process can be performed based on the similarity between the target employee and each reference employee, and the attention paid by each reference employee to the content to be recommended to obtain the similarity of the target employee to the content to be recommended.

[0087] Step 108: recommend the content to be recommended to the target employee based on the interest level.

[0088] In the embodiment of the present application, the interest level is the predicted interest level of the target employee in the recommended content. According to the target employee's interest level in the recommended content, the recommended content is recommended to the target employee, wherein the recommended content can be all the business content of the target employee's position sequence, or all the business content that the target employee and the reference employee have browsed in history.

[0089] It should be noted that the business content included in the above-mentioned content to be recommended is an example of this embodiment. In actual application, corresponding adjustments can be made according to the career development of the company's employees. The embodiment of this application does not specifically limit the business content included in the content to be recommended.

[0090] In the embodiment of the present application, different methods can be used to recommend the content to be recommended to the target employee based on the interest level, for example: setting an interest level threshold, when the target user's interest level in the content to be recommended exceeds the interest level threshold, recommend the content to be recommended to the target user; or, based on the target user's interest level in all the content to be recommended, sorting the content from large to small according to the interest level to form a list, and recommending the first preset number of content to be recommended in the list to the target user. It should be noted that the above method of recommending the content to be recommended to the target employee based on the interest level is only an example of the present embodiment, and the present embodiment of the application does not specifically limit how to recommend the content to be recommended to the target employee based on the interest level.

[0091] In the above recommendation method, the target employee's interest in the content to be recommended can be determined through the similarity between the target employee and a first reference employee in the same job sequence and the same job level, and a second reference employee in the same job sequence and one level above the target employee's job level, and the attention paid by the first reference employee and the second reference employee to the content to be recommended, respectively, so as to recommend the content to be recommended to the target employee based on the interest level. That is, when recommending content to the target employee, both the first reference employee and the second reference employee are referred to at the same time, so that the obtained interest level is more accurate, so that the content recommended to the target employee based on the interest level is more in line with the target user's job requirements, thereby improving the recommendation accuracy of the content to be recommended.

[0092] In one embodiment, Figure 2 As shown, in the relationship network diagram, there is a directed edge between the reference employee and the target employee, the directed edge between the first reference employee and the target employee is a bidirectional edge, and the directed edge between the second reference employee and the target employee is a unidirectional edge pointing to the second reference employee. The bidirectional edge is used to represent the same job level relationship, and the unidirectional edge is used to represent the upper and lower job level relationship.

[0093] In an embodiment of the present application, a directed edge between each reference employee and a target employee is used in a relationship network diagram to represent the relationship between each reference employee and the target employee, and the reference employees include a first reference employee and a second reference employee. The directed edge between the first reference employee and the target employee is a bidirectional edge, which indicates that the first reference employee and the target employee have the same job level. The directed edge between the second reference employee and the target employee is a unidirectional edge, which points to the second reference employee, indicating that the job level of the second reference employee is one level above the job level of the target employee.

[0094] Still taking the above example, the job level of employee A, employee B and employee C is ordinary employee, then there is a bidirectional edge between every two employees among employee A, employee B and employee C, and the job level of employee 1 and employee 2 is manager level. Taking employee A as an example, there is a unidirectional edge from employee A to employee 1 between employee A and employee 1, and there is a unidirectional edge from employee A to employee 2 between employee A and employee 2.

[0095] In this embodiment, in the relationship network diagram, directed edges are used to represent the job hierarchy relationships between employees, which is clearer and more concise than other diagram methods.

[0096] In one embodiment, referring to Figure 3 As shown, in step 104, according to the first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or the second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee, the similarity between the target employee and the reference employee is determined, including:

[0097] Step 302: Determine a third employee set and a fourth employee set according to the first employee set and the second employee set, wherein the third employee set is the intersection of the first employee set and the second employee set, and the fourth employee set is the union of the first employee set and the second employee set.

[0098] In the embodiment of the present application, based on the first employee set and the second employee set, employees who have a job hierarchy relationship with both the target employee and the reference employee, as well as employees who have a job hierarchy relationship with the target employee and a job hierarchy relationship with the reference employee can be obtained.

[0099] The similarity between the target employee and the reference employee is directly proportional to the number of employees who have a job hierarchy relationship with both the target employee and the reference employee, and is inversely proportional to the number of employees who have a job hierarchy relationship with both the target employee and the reference employee.

[0100] In an embodiment of the present application, the third employee set is the intersection of the first employee set and the second employee set, that is, the third employee set is a set of employees who have a job hierarchy relationship with both the target employee and the reference employee, and the fourth employee set is the union of the first employee set and the second employee set, that is, the fourth employee set is a set of employees who have a job hierarchy relationship with the target employee and employees who have a job hierarchy relationship with the reference employee.

[0101] Still taking the above example, for employee A, employee B and employee C, taking employee A as the target employee as an example, for reference employee B, the first employee set is {employee B, employee C}, the second employee set is {employee A, employee C}, then the third employee set is {employee C}, employee C has a job hierarchy relationship with both employee A and employee B, the fourth employee set is {employee A, employee B, employee C}, employee B, employee C have a job hierarchy relationship with employee A, and employee A, employee C have a job hierarchy relationship with employee B.

[0102] Step 304: Determine the similarity between the target employee and the reference employee based on the third employee set and the fourth employee set.

[0103] In the embodiment of the present application, the ratio of the number of employees in the third employee set to the number of employees in the fourth employee set is the similarity between the target employee and the reference employee, wherein the similarity is a form of expression of the repetition between the business that the target employee is responsible for and the business that the reference employee is responsible for. For a reference employee, the proportion of employees who have a job hierarchy relationship with the target employee and the reference employee to the employees who have a job hierarchy relationship with the target employee or have a job hierarchy relationship with the reference employee can represent the similarity between the target employee and the reference employee, that is, the repetition of business between the target employee and the reference employee.

[0104] In this embodiment, the second employee set is a set of employees who have a job hierarchy relationship with the reference employees, and the reference employees include the first reference employee and the second reference employee. Therefore, the similarity includes the similarity between the target employee and the first reference employee, and the similarity between the target employee and the second reference employee. When subsequently determining the target employee's interest in the content to be recommended, it is also necessary to refer to the similarity between the target employee and the first reference employee and the similarity between the target employee and the second reference employee at the same time, so that the obtained interest is more accurate, so that the content recommended to the target employee based on the interest is more in line with the job requirements of the target user, thereby improving the recommendation accuracy of the content to be recommended.

[0105] In one embodiment, the second employee set is a set of employees with the same job level as the reference employee; when the reference employee is the first reference employee, the first employee set is the set of the first reference employee; when the reference employee is the second reference employee, the first employee set is the set of the second reference employee.

[0106] In an embodiment of the present application, the second employee set is a set of employees having a job hierarchy relationship with the reference employee, and having a job hierarchy relationship with the reference employee only means that the job hierarchy is the same as that of the reference employee. Therefore, the second employee set is a set of employees having the same job hierarchy as that of the reference employee.

[0107] The first employee set is a set of employees that have a job hierarchy relationship with the target employee, and having a job hierarchy relationship with the target employee means that the job hierarchy is the same as the target employee, or the job hierarchy is one level above the job hierarchy of the target employee. When the reference employee is the first reference employee, the first employee set is the set of the first reference employee, and when the reference employee is the second reference employee, the first employee set is the set of the second reference employee.

[0108] Exemplarily, determining the similarity (S(v u ,v i The process of ), Similarity) can refer to the following formula (1).

[0109]

[0110] Among them, v u represents the target employee, v i represents the i-th first reference employee, Dio(v u ) represents the set of employees with the same job level as the target employee, Dio(v i ) represents the set of employees with the same job level as the first reference employee, and V represents the set of target employees and reference employees.

[0111] Determine the similarity between the target employee and the second reference employee (S(v u ,vj The process of ), Similarity) can refer to the following formula (II).

[0112]

[0113] Among them, v u represents the target employee, v j represents the jth second reference employee, Sout(v u ) represents the set of employees whose job level is one level above the target employee’s job level. Dio(v j ) represents the set of employees with the same job level as the second reference employee, and V represents the set of target employees and reference employees.

[0114] In this embodiment, in the process of determining the similarity between the target employee and the reference employee, when the reference employees are the first reference employee and the second reference employee respectively, the specific content represented by the first employee set is different. Therefore, two different similarity results can be obtained. When subsequently determining the target employee's interest in the content to be recommended, it is also necessary to refer to the similarity between the target employee and the first reference employee and the similarity between the target employee and the second reference employee at the same time, so that the obtained interest is more accurate, so that the content recommended to the target employee based on the interest is more in line with the job requirements of the target user, thereby improving the recommendation accuracy of the content to be recommended.

[0115] In one embodiment, referring to Figure 4 As shown, in step 106, the interest of the target employee in the content to be recommended is determined based on the similarity between the target employee and each reference employee, and the attention paid by each reference employee to the content to be recommended, including:

[0116] Step 402: Obtain a first weight of the target employee relative to the first reference employee and a second weight of the target employee relative to the second reference employee, wherein the sum of the first weight and the second weight is 1.

[0117] In the embodiment of the present application, a target employee may have at least one reference employee. When determining the interest of the target employee in the content to be recommended, each first reference employee and each second reference employee will have an impact on the interest. The first weight represents the influence of the first reference employee on the interest, the second weight represents the influence of the second reference employee on the interest, and the sum of the first weight and the second weight is 1.

[0118] Step 404 , determining the first interest level of the target employee for the content to be recommended based on the similarity between the target employee and each first reference employee, the first weight, and the attention level of each first reference employee for the content to be recommended.

[0119] In the embodiment of the present application, the first interest level indicates the interest level of the target employee in the content to be recommended under the influence of each first reference employee. The first interest level of the target employee in the content to be recommended can be determined based on the similarity between the target employee and each first reference employee, the first weight, and the attention level of each first reference employee in the content to be recommended.

[0120] Step 406 , determining the second interest level of the target employee for the content to be recommended based on the similarity between the target employee and each second reference employee, the second weight, and the attention level of each second reference employee for the content to be recommended.

[0121] In the embodiment of the present application, the second interest level represents the interest level of the target employee in the content to be recommended under the influence of each second reference employee. The second interest level of the target employee in the content to be recommended is determined based on the similarity between the target employee and each second reference employee, the second weight, and the attention level of each second reference employee in the content to be recommended.

[0122] Step 408: Determine the interest level of the target employee for the content to be recommended based on the first interest level and the second interest level.

[0123] In the embodiment of the present application, the interest level of the target employee in the content to be recommended is positively correlated with the first interest level and the second interest level, respectively.

[0124] Exemplarily, the process of determining the interest of the target employee in the content to be recommended may refer to the following formula (III).

[0125]

[0126] Among them, P represents the interest of the target employee in the content to be recommended, c i Indicates the content to be recommended, Intrest(v i ,c i ) represents the attention of the first reference employee to the content to be recommended, Intrest(v j ,c i ) represents the attention of the second reference employee to the content to be recommended, α represents the first weight, β represents the second weight, Indicates the first level of interest, Indicates the second level of interest.

[0127] In this embodiment, the interest of the target employee in the content to be recommended is determined based on the similarities between the target employee and the first reference employee and the second reference employee, and the attention paid by the first reference employee and the second reference employee to the content to be recommended, so as to determine the content to be recommended to the target employee. When determining the interest of the target employee in the content to be recommended, the first reference employee and the second reference employee are simultaneously referred to, so that the obtained interest is more accurate, thereby achieving the purpose of improving the accuracy of recommendation to the target employee.

[0128] In one embodiment, Figure 5 As shown, the recommended method also includes:

[0129] Step 502: Determine whether the target employee meets the weight adjustment condition based on the target employee's business data.

[0130] Step 504: if the target employee meets the weight adjustment condition, the second weight is increased and adjusted;

[0131] Among them, business data includes years of employment, test results and training progress.

[0132] In an embodiment of the present application, whether the target employee meets the weight adjustment conditions is determined based on the target employee's business data, wherein the business data includes years of employment, test results, and training progress, etc. When the target employee meets the weight adjustment conditions, it indicates that the target employee can handle more business, for example: he is about to face a promotion, and is about to face the business involved in the previous job level. Therefore, at this time, the second weight can be increased and adjusted to increase the influence of the second reference employee on the interest level.

[0133] It should be noted that the basis for determining whether the target employee meets the weight adjustment conditions can be a single business data or a comprehensive consideration of multiple business data. The embodiment of the present application does not specifically limit the basis for determining whether the weight adjustment conditions are met.

[0134] For example, let the business data be the years of employment, employee A's years of employment are 5 years, and the weight adjustment condition is that the years of employment are 5 years or more. At this time, employee A meets the weight adjustment condition, and the second weight is increased from 0.4 to 0.6.

[0135] In this embodiment, it is possible to determine whether the target employee meets the weight adjustment conditions based on the target employee's business data, and then increase the second weight when the weight adjustment conditions are met. That is, the influence of the second reference employee on the target employee's interest in the content to be recommended can be adjusted according to the employee's work situation, and then more appropriate content to be recommended can be recommended to the employee more specifically based on the employee's work situation. The target employee's business data and weight adjustment conditions can be adjusted according to different company situations, so that the recommendation method is accurately applicable to multiple companies.

[0136] In a specific embodiment, a bank has a relationship network diagram, which includes employee A, employee B, employee C, employee 1, and employee 2. The job sequences of the above five employees are all loan business, the job levels of employee A, employee B, and employee C are all ordinary employees, the job levels of employee 1 and employee 2 are both manager level one, employee A is the target employee, employee B and employee C are the first reference employees, and employee 1 and employee 2 are the second reference employees.

[0137] For employee B, the first employee set is the set of the first reference employees {employee B, employee C}, the second employee set is the set of employees with the same job level as employee B {employee A, employee C}, so the third employee set is {employee C}, and the fourth employee set is {employee A, employee B, employee C}. The similarity between employee A and employee B is 1 / 3.

[0138] For employee 1, the first employee set is the set of the second reference employees {employee 1, employee 2}, the second employee set is the set of employees with the same job level as employee 1 {employee 2}, so the third employee set is {employee 2}, the fourth employee set is {employee 1, employee 2}, and the similarity between employee A and employee 1 is 1 / 2.

[0139] According to the above method, the similarity between employee A and other reference employees is determined in turn.

[0140] In addition, the business data of employee A is the length of service, and the length of service of employee A is 5 years. The weight adjustment condition is that the length of service is 5 years. At this time, employee A meets the weight adjustment condition, and the second weight is increased from 0.4 to 0.6. The first weight 0.4 and the second weight 0.6 are obtained, and the attention of each reference employee to the recommended content is obtained respectively.

[0141] Multiply the similarity between employee A and employee B, employee B's attention to the content to be recommended, and the first weight 0.6 to obtain the first interest level based on employee B; multiply the similarity between employee A and employee C, employee C's attention to the content to be recommended, and the first weight 0.6 to obtain the first interest level based on employee C. Add the first interest level based on employee B and the first interest level based on employee C to obtain employee A's first interest level for the content to be recommended. Use the same method to determine employee A's second interest level for the content to be recommended, and add the first interest level to the second interest level to determine employee A's interest level for the content to be recommended. When the interest level is greater than 0.6, the content to be recommended is recommended to employee A.

[0142] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a recommendation device for implementing the recommendation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more recommendation device embodiments provided below can refer to the limitations on the recommendation method above, and will not be repeated here.

[0144] In one embodiment, Figure 6 As shown, a recommendation device is provided, including: a first determination module 602, a second determination module 604, a third determination module 606 and a recommendation module 608, wherein:

[0145] A first determination module 602 is used to determine a reference employee of the target employee from the relationship network diagram, the reference employee and the target employee are in the same job sequence, the reference employee includes a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0146] A second determination module 604 is used to determine the similarity between the target employee and the reference employee based on the first set of employees having a job hierarchical relationship with the target employee in the relationship network diagram and / or the second set of employees having a job hierarchical relationship with the reference employee in the relationship network diagram;

[0147] The third determination module 606 is used to determine the interest of the target employee in the content to be recommended based on the similarity between the target employee and each reference employee and the attention of each reference employee to the content to be recommended;

[0148] The recommendation module 608 is used to recommend the content to be recommended to the target employee according to the interest level.

[0149] In an embodiment of the present application, a first reference employee who is in the same job sequence and the same job level as the target employee, and a second reference employee who is in the same job sequence as the target employee and one level above the target employee's job level are determined, and based on the similarity between the target employee and the first reference employee and the second reference employee, and the attention paid by the first reference employee and the second reference employee to the content to be recommended, the interest of the target employee in the content to be recommended is determined to determine the content to be recommended to the target employee. When determining the interest of the target employee in the content to be recommended, both the first reference employee and the second reference employee are referenced to make the obtained interest more accurate, so that the content recommended to the target employee based on the interest is more in line with the job requirements of the target user, thereby improving the recommendation accuracy of the content to be recommended.

[0150] In one embodiment, in a relationship network diagram, there is a directed edge between the reference employee and the target employee, the directed edge between the first reference employee and the target employee is a bidirectional edge, and the directed edge between the second reference employee and the target employee is a unidirectional edge pointing to the second reference employee. The bidirectional edge is used to represent the same job level relationship, and the unidirectional edge is used to represent the upper and lower job level relationships.

[0151] In one embodiment, the second determining module 604 is further configured to:

[0152] According to the first employee set and the second employee set, determine a third employee set and a fourth employee set, the third employee set is the intersection of the first employee set and the second employee set, and the fourth employee set is the union of the first employee set and the second employee set;

[0153] The similarity between the target employee and the reference employee is determined based on the third employee set and the fourth employee set.

[0154] In one embodiment, the second employee set is a set of employees with the same job level as the reference employee; when the reference employee is the first reference employee, the first employee set is the set of the first reference employee; when the reference employee is the second reference employee, the first employee set is the set of the second reference employee.

[0155] In one embodiment, the third determining module 606 is further configured to:

[0156] Obtaining a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1;

[0157] Determining a first interest level of the target employee for the content to be recommended based on the similarity between the target employee and each first reference employee, the first weight, and the attention level of each first reference employee for the content to be recommended;

[0158] Determining a second interest level of the target employee for the content to be recommended based on the similarity between the target employee and each second reference employee, the second weight, and the attention level of each second reference employee for the content to be recommended;

[0159] The interest degree of the target employee with respect to the content to be recommended is determined according to the first interest degree and the second interest degree.

[0160] In one embodiment, the recommendation device further comprises:

[0161] A fourth determination module is used to determine whether the target employee meets the weight adjustment condition according to the business data of the target employee;

[0162] An increasing module, used for increasing and adjusting the second weight when the target employee meets the weight adjustment condition;

[0163] Among them, business data includes years of employment, test results and training progress.

[0164] Each module in the above recommendation device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0165] In one embodiment, a simulation system is provided, the system comprising a management end and a trainee end; wherein:

[0166] The management end is used to store training content and examination content;

[0167] The trainee terminal is used to determine the reference employees of the target employee from the relationship network diagram, determine the similarity between the target employee and the reference employees, and determine the interest of the target employee in the content to be recommended based on the similarity between the target employee and each reference employee and the attention paid by each reference employee to the content to be recommended, wherein the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee;

[0168] The trainee end is also used to recommend content to target employees based on their interest. The content to be recommended is the training content and examination content stored in the management end.

[0169] In one embodiment, the management terminal further includes an information collection module, a data processing module, a data generation module and a data display module; wherein,

[0170] The information collection module is used to input training resources and transmit the training resources to the data processing module and the data display module respectively;

[0171] The data processing module is used to receive training resources, perform data processing on the training resources, and save the training resources in a database;

[0172] The data generation module is used to read the training resources in the database according to the training rules, and generate the training content according to the training rules;

[0173] The data generation module is also used to read the training resources in the database according to the examination rules and generate the examination content according to the examination rules;

[0174] The data display module is used to receive the training resources transmitted by the information acquisition module and the training contents and examination contents transmitted by the data generation module, and to display the training resources, training contents and examination contents.

[0175] In one embodiment, the information collection module includes: a scene information input unit, an AR interaction information input unit and a rule data input unit; wherein,

[0176] The scene information input unit is used to input the operation step information of the counter business;

[0177] The AR interactive information input unit is connected to the scene information input unit and is used to input the customer demand video of the counter business recorded by the administrator;

[0178] The rule data input unit is used to input the input information rules of the counter business.

[0179] In the embodiment of the present application, the operation step information is the operation steps that a counter business employee needs to perform to complete the operation, and the input information rules are the rules that the input information should meet in each step of the counter business.

[0180] In one embodiment, the information processing module includes: a scene information processing unit, an AR interaction information processing unit and a rule data processing unit; wherein,

[0181] The scene processing unit is used to process the operation step information of the counter business and store the operation step information of the counter business in the database;

[0182] The AR interactive information processing unit is used to store customer demand videos of counter services into a database;

[0183] The rule data processing unit is used to store the input information rules of the counter business in the database as unstructured data.

[0184] In one embodiment, the data generation module includes: a data generation rule input unit and a data generation unit; wherein,

[0185] The administrator inputs the training rules and the examination rules into the management terminal through the data generation rule input unit;

[0186] The data generation unit is used to read the counter business in the database according to the training rules, and generate training content according to the training rules;

[0187] The data generating unit is used to read the counter services in the database according to the examination rules, and to generate the examination contents according to the examination rules.

[0188] In one embodiment, the trainee terminal includes a trainee training terminal module, a trainee simulation operation module, a trainee simulation operation monitoring module, a big data analysis platform and an AR-assisted training module, wherein:

[0189] The academy training terminal module is used to play or display training resources for employees to browse and learn;

[0190] Employees perform simulated operations on business through the trainee simulated operation module, and the trainee simulated operation monitoring module is used to monitor the simulated operations of employees and determine whether the simulated operations are correct;

[0191] The AR-assisted training module is used to simulate virtual scenarios of employees' counter business, providing employees with more realistic training and examination scenarios.

[0192] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a recommendation method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0193] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0194] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0195] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0196] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0197] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0198] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0199] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A recommendation method, characterized in that: The method comprises: Determine reference employees of the target employee from the relationship network diagram, the reference employees and the target employee are in the same job sequence, the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee; For any of the reference employees, determine the similarity between the target employee and the reference employee based on a first set of employees in the relationship network diagram that have a job hierarchical relationship with the target employee, and / or a second set of employees in the relationship network diagram that have a job hierarchical relationship with the reference employee; Determining the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended; Recommending the content to be recommended to the target employee according to the interest level; The step of determining the interest of the target employee in the content to be recommended based on the similarity between the target employee and each of the reference employees and the attention paid by each of the reference employees to the content to be recommended includes: Obtaining a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1; determining a first interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the first reference employees, the first weight, and the attention level of each of the first reference employees for the content to be recommended; determining a second interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the second reference employees, the second weight, and the attention level of each of the second reference employees for the content to be recommended; The interest degree of the target employee in the content to be recommended is determined according to the first interest degree and the second interest degree.

2. The method according to claim 1, characterized in that In the relationship network diagram, there is a directed edge between the reference employee and the target employee, the directed edge between the first reference employee and the target employee is a bidirectional edge, and the directed edge between the second reference employee and the target employee is a unidirectional edge pointing to the second reference employee, the bidirectional edge is used to represent the same job level relationship, and the unidirectional edge is used to represent the upper and lower job level relationship.

3. The method according to claim 1 or 2, characterized in that: The determining the similarity between the target employee and the reference employee according to the first set of employees having a job hierarchical relationship with the target employee in the relationship network diagram and / or the second set of employees having a job hierarchical relationship with the reference employee in the relationship network diagram includes: Determine a third employee set and a fourth employee set according to the first employee set and the second employee set, wherein the third employee set is the intersection of the first employee set and the second employee set, and the fourth employee set is the union of the first employee set and the second employee set; The similarity between the target employee and the reference employee is determined according to the third employee set and the fourth employee set.

4. The method according to claim 1 or 2, characterized in that: The second employee set is a set of employees with the same job level as the reference employee; when the reference employee is the first reference employee, the first employee set is the set of the first reference employees; when the reference employee is the second reference employee, the first employee set is the set of the second reference employees.

5. The method according to claim 1, characterized in that The method further comprises: Determining whether the target employee meets the weight adjustment condition according to the business data of the target employee; When the target employee meets the weight adjustment condition, the second weight is increased and adjusted; Among them, the business data includes years of employment, test results and training progress.

6. A recommendation device, characterized in that: The device comprises: A first determination module is used to determine a reference employee of the target employee from a relationship network diagram, wherein the reference employee and the target employee are in the same job sequence, and the reference employee includes a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee; A second determination module is used to determine the similarity between the target employee and the reference employee based on a first set of employees having a job hierarchy relationship with the target employee in the relationship network diagram, and / or a second set of employees having a job hierarchy relationship with the reference employee in the relationship network diagram; A third determination module is used to determine the interest of the target employee in the content to be recommended according to the similarity between the target employee and each of the reference employees and the attention of each of the reference employees to the content to be recommended; A recommendation module, configured to recommend the content to be recommended to the target employee according to the interest level; Wherein, the third determination module is further used for: Obtaining a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1; determining a first interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the first reference employees, the first weight, and the attention level of each of the first reference employees for the content to be recommended; determining a second interest level of the target employee for the content to be recommended according to the similarity between the target employee and each of the second reference employees, the second weight, and the attention level of each of the second reference employees for the content to be recommended; The interest degree of the target employee in the content to be recommended is determined according to the first interest degree and the second interest degree.

7. A simulation system, characterized in that: The system includes a management end and a student end; wherein, The management terminal is used to store training content and examination content; The trainee terminal is used to determine the reference employees of the target employee from the relationship network diagram, determine the similarity between the target employee and the reference employees, and determine the interest of the target employee in the content to be recommended based on the similarity between the target employee and each of the reference employees, and the attention of each of the reference employees to the content to be recommended, wherein the reference employees include a first reference employee and / or a second reference employee, the job level of the first reference employee is the same as the job level of the target employee, and the job level of the second reference employee is one level above the job level of the target employee; wherein, determining the interest of the target employee in the content to be recommended based on the similarity between the target employee and each of the reference employees, and the attention of each of the reference employees to the content to be recommended, includes : Obtain a first weight of the target employee with respect to the first reference employee and a second weight of the target employee with respect to the second reference employee, wherein the sum of the first weight and the second weight is 1; determine a first interest level of the target employee with respect to the content to be recommended according to the similarity between the target employee and each of the first reference employees, the first weight, and the attention level of each of the first reference employees with respect to the content to be recommended; determine a second interest level of the target employee with respect to the content to be recommended according to the similarity between the target employee and each of the second reference employees, the second weight, and the attention level of each of the second reference employees with respect to the content to be recommended; determine the interest level of the target employee with respect to the content to be recommended according to the first interest level and the second interest level; The trainee terminal is further used to recommend the content to be recommended to the target employee according to the interest level, and the content to be recommended is the training content and the test content stored in the management terminal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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