Information recommendation method and device, storage medium and electronic equipment

Abstract

The invention discloses an information recommendation method and device, a storage medium and electronic equipment. The method relates to the field of financial science and technology, and comprises the steps of determining a first feature set corresponding to a target object based on character test data in the presence of the character test data of the target object, the target object being an object of a to-be-transacted financial service; determining a second feature set corresponding to each customer manager according to the character test data of the plurality of customer managers; calculating the first feature set and the second feature set to obtain a matching value of each customer manager and the target object; and according to the matching value, determining information of a to-be-recommended customer manager from the information of the plurality of customer managers, and pushing the information of the to-be-recommended customer manager to the target object. Through the method and the device, the problem of relatively low recommendation accuracy caused by recommendation of information of customer managers for handling services to customers according to own experience in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and specifically, to an information recommendation method and device, a storage medium, and an electronic device. Background Art

[0002] With the continuous development of financial services, customers are the core assets of enterprises, and customer relationships are an effective means for enterprises to improve their core competitiveness in customer management. How to increase customer stickiness and improve customer satisfaction has become an important topic. Among them, account managers are important entities that provide services to customers in the banking system. Account managers discover customer needs and solve customer problems in a timely manner. Since the needs of each customer are different, the service capabilities of account managers are also different. When the fit between account managers and customers is poor, account managers cannot provide users with better services, and customer satisfaction with banking services will also decrease. At present, managers generally assign account managers to connect with customers based on their own experience, but this method has the problem of low accuracy.

[0003] Currently, no effective solution has been proposed to the problem that in related technologies, the accuracy of recommendations of account managers who recommend business to customers based on their own experience is relatively low. Summary of the invention

[0004] The main purpose of this application is to provide an information recommendation method and device, storage medium and electronic device to solve the problem in the related art that the information of the account manager who recommends business to the customer based on his own experience leads to a relatively low recommendation accuracy.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for recommending information is provided. The method comprises: in the case of personality test data of a target object, based on the personality test data, determining a first feature set corresponding to the target object, the target object being the object of a to-be-processed financial business; determining a second feature set corresponding to each account manager based on the personality test data of multiple account managers; calculating the first feature set and the second feature set to obtain a matching value between each account manager and the target object; determining the information of the account manager to be recommended from the information of the multiple account managers based on the matching value, and pushing the information of the account manager to be recommended to the target object.

[0006] Furthermore, based on the personality test data, determining the first feature set corresponding to the target object includes: determining the score values ​​of the target object in multiple personality dimensions based on the personality test data; obtaining attribute information of the target object, wherein the attribute information at least includes the age information of the target object; performing numerical conversion on the attribute information to obtain a target value; and determining the first feature set based on the score value and the target value.

[0007] Furthermore, calculating the first feature set and the second feature set to obtain a matching value between each account manager and the target object includes: for a target account manager, calculating the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers to obtain a first prior probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; determining a second prior probability of the first feature set corresponding to the target object; and calculating based on the first prior probability and the second prior probability to obtain the matching value.

[0008] Further, the matching value is obtained by performing calculations based on the first prior probability and the second prior probability, including: obtaining a first historical feature set of multiple historical objects, and obtaining a second historical feature set of historical account managers matching each historical object; based on the first historical feature set and the second historical feature set, calculating the conditional probability that the historical object possesses the first feature set when the account manager possesses the second feature set, to obtain the conditional probability; based on the conditional probability, the first prior probability and the second prior probability, calculating the posterior probability that the account manager possesses the second feature set when the historical object possesses the first feature set, to obtain the matching value.

[0009] Furthermore, determining the second prior probability of the first feature set corresponding to the target object includes: obtaining a first historical feature set of multiple historical objects; for any feature value in the first feature set, calculating the proportion of the feature value in the first historical feature set to obtain a proportion value; and calculating based on the proportion value to obtain the second prior probability.

[0010] Furthermore, based on the matching value, determining the information of the account manager to be recommended from the information of multiple account managers includes: sorting the information of the multiple account managers based on the matching value to obtain a sorting result; determining the information of candidate account managers from the information of the multiple account managers based on the sorting result; obtaining the target financial business information to be handled by the target object, and determining the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information.

[0011] Furthermore, determining the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information includes: obtaining historical financial business information that the candidate account manager has handled; and determining the information of the account manager to be recommended from the information of the candidate account managers based on the historical financial business information and the target financial business information.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for recommending information is provided. The device comprises: a first determination unit, which is used to determine a first feature set corresponding to the target object based on the personality test data when there is personality test data of the target object, wherein the target object is the object to be processed with financial business; a second determination unit, which is used to determine a second feature set corresponding to each account manager based on the personality test data of multiple account managers; a calculation unit, which is used to calculate the first feature set and the second feature set to obtain a matching value between each account manager and the target object; and a third determination unit, which is used to determine the information of the account manager to be recommended from the information of the multiple account managers based on the matching value, and push the information of the account manager to be recommended to the target object.

[0013] Furthermore, the first determination unit includes: a first determination module, used to determine the score value of the target object in multiple personality dimensions based on the personality test data; a first acquisition module, used to obtain attribute information of the target object, wherein the attribute information at least includes the age information of the target object; a conversion module, used to perform numerical conversion on the attribute information to obtain a target value; and a second determination module, used to determine the first feature set based on the score value and the target value.

[0014] Furthermore, the calculation unit includes: a first calculation module, used to calculate the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers, and obtain a first prior probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; a third determination module, used to determine the second prior probability of the first feature set corresponding to the target object; and a second calculation module, used to perform calculations based on the first prior probability and the second prior probability to obtain the matching value.

[0015] Furthermore, the second calculation module includes: a second acquisition module, used to obtain a first historical feature set of multiple historical objects, and obtain a second historical feature set of a historical account manager matching each historical object; a third calculation module, used to calculate the conditional probability that the historical object has the first feature set when the account manager has the second feature set based on the first historical feature set and the second historical feature set, to obtain the conditional probability; a fourth calculation module, used to calculate the posterior probability that the account manager has the second feature set when the historical object has the first feature set based on the conditional probability, the first prior probability and the second prior probability, to obtain the matching value.

[0016] Furthermore, the third determination module includes: a first acquisition submodule, used to acquire a first historical feature set of multiple historical objects; a first calculation submodule, used to calculate the proportion of any feature value in the first historical feature set to obtain a proportion value; and a second calculation submodule, used to calculate based on the proportion value to obtain the second prior probability.

[0017] Furthermore, the third determination unit includes: a processing module, used to sort the information of the multiple account managers according to the matching value to obtain a sorting result; a fourth determination module, used to determine the information of candidate account managers from the information of the multiple account managers according to the sorting result; a third acquisition module, used to obtain the target financial business information to be handled by the target object, and determine the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information.

[0018] Furthermore, the third acquisition module includes: a second acquisition sub-module, used to obtain the historical financial business information handled by the candidate account manager; and a determination sub-module, used to determine the information of the account manager to be recommended from the information of the candidate account manager based on the historical financial business information and the target financial business information.

[0019] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is also provided, wherein the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the steps of any one of the above-mentioned method embodiments.

[0020] In order to achieve the above-mentioned purpose, according to another aspect of the present application, there is further provided an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above-mentioned method embodiments.

[0021] In the embodiment of the present application, in the case of personality test data of the target object, based on the personality test data, the first feature set corresponding to the target object is determined, and the target object is the object of the financial business to be handled; according to the personality test data of multiple account managers, the second feature set corresponding to each account manager is determined; the first feature set and the second feature set are calculated to obtain the matching value between each account manager and the target object; according to the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object, which solves the technical problem that the related technology recommends the information of the account manager for handling business to the customer based on its own experience, resulting in a relatively low recommendation accuracy. In this scheme, the first feature set of the target object and the second feature set of the account manager are obtained by effectively quantifying the personal personality factors through the personality test data, and then the matching value of the account manager and the target object is calculated according to the first feature set and the second feature set, and finally, according to the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object, and the first feature set and the second feature set can more accurately describe the characteristics of the account manager and the user, avoiding the judgment bias problem caused by subjective experience, thereby achieving the technical effect of improving the recommendation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A hardware structure block diagram of a computer terminal for implementing an information recommendation method is shown;

[0024] Figure 2 This is the process of the recommended method based on the information provided in the embodiment of this application Figure 1 ;

[0025] Figure 3This is the process of the recommended method based on the information provided in the embodiment of this application Figure 2 ;

[0026] Figure 4 is a schematic diagram of a recommendation device according to information provided by an embodiment of the present application;

[0027] Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0031] Example 1

[0032] According to an embodiment of the present application, a method embodiment of recommending information is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing an information recommendation method. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0034] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the information recommendation method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned information recommendation method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] The display may be a touch screen type liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0038] Under the above operating environment, this application provides Figure 2 The recommended method for displaying the information. Figure 2 This is the process of the recommended method based on the information of Example 1 of this application Figure 1 The recommended approach includes:

[0039] Step S201, in the case where personality test data of a target object exists, a first feature set corresponding to the target object is determined based on the personality test data, wherein the target object is a subject for which financial business is to be processed.

[0040] Optionally, when a user (i.e., the target object mentioned above) conducts financial business, it is determined whether the personality test data of the user (i.e., the target object mentioned above) can be used. If the personality test data can be used, the personality test data of the user is obtained, and then the first feature set corresponding to the target object is determined through the personality test data.

[0041] In an optional embodiment, the user's personality test data can be obtained through the test questions corresponding to MBTI (Myers-Briggs Type Indicator, MBTI), and each test question in MBTI involves one of the four pairs of dimensions of MBTI. Each question will have two or more options to describe different personality traits. For example, for the dimensions of "extroversion (E)" and "introversion (I)", the question may be: In social activities, do you prefer to talk loudly or keep a low profile and listen? . The tester answers each question and selects the option they think is most in line with their behavior and preferences. The user's personality test data can be obtained from the test questions in MBTI and the user's answer options.

[0042] In an optional embodiment, after obtaining the user's personality test data, its encoding features can be extracted through a deep learning model to obtain a corresponding first feature set.

[0043] Step S202, determining a second feature set corresponding to each account manager based on the personality test data of multiple account managers.

[0044] Optionally, the personality test data of multiple account managers are obtained in the same manner, and the second feature set corresponding to each account manager is obtained based on the personality test data of the account managers.

[0045] Step S203, calculating the first feature set and the second feature set to obtain a matching value between each account manager and the target object.

[0046] Optionally, the first feature set and the second feature set are calculated to obtain the matching value between each account manager and the target object. In an optional embodiment, the first feature set and the second feature set can be calculated to obtain multiple similarities, and the matching value between each account manager and the target object can be determined based on the similarities.

[0047] Step S204, determining the information of the account manager to be recommended from the information of the multiple account managers according to the matching value, and pushing the information of the account manager to be recommended to the target object.

[0048] Optionally, the information of the account manager to be recommended is determined from the information of multiple account managers based on the matching value. For example, the account manager with the largest matching value is selected as the account manager to be recommended, and the information of the account manager to be recommended is pushed to the target object so that financial services can be handled for the target object through the account manager to be recommended.

[0049] To summarize, the personal personality factors are effectively quantified through personality test data to obtain the first feature set of the target object and the second feature set of the account manager, and then the matching value between the account manager and the target object is calculated based on the first feature set and the second feature set. Finally, based on the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object. The first feature set and the second feature set can more accurately describe the characteristics of the account manager and the user, avoiding the judgment bias caused by subjective experience, thereby achieving the technical effect of improving the recommendation accuracy.

[0050] Optionally, in the information recommendation method provided in Example 1 of the present application, determining the first feature set corresponding to the target object based on the personality test data includes: determining the score value of the target object in multiple personality dimensions based on the personality test data; obtaining attribute information of the target object, wherein the attribute information includes at least the age information of the target object; performing numerical conversion on the attribute information to obtain a target value; and determining the first feature set based on the score value and the target value.

[0051] In an optional embodiment, the first feature set described above is obtained by the following steps: Determine the score values ​​of the target object in multiple personality dimensions based on the personality test data. For example, the tester's answer to each personality test question will be assigned a corresponding score. All scores of the tester in each dimension are weighted and summed to obtain a score of one dimension. For example, the scores in the "extroversion" and "introversion" dimensions are weighted and summarized. For example, the user's evaluation score in the "extroversion-introversion" dimension, the evaluation score in the "feeling-intuition" dimension, the tester's evaluation score in the "thinking-emotion" dimension, and the evaluation score in the "judgment-perception" dimension are used as the above-mentioned scoring values ​​in multiple personality dimensions.

[0052] Then, the user's attribute information is obtained, for example, the attribute information may include the user's age information and gender information, etc. The attribute information is converted into a target value, and finally, the first feature set is determined according to the score value and the target value.

[0053] The scoring values ​​of multiple personality dimensions and the user's attribute information can more accurately characterize the user's characteristics, and thus more accurately match the user with a corresponding account manager.

[0054] Optionally, in the information recommendation method provided in Example 1 of the present application, the first feature set and the second feature set are calculated to obtain the matching value of each account manager and the target object, including: for the target account manager, the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers is calculated to obtain the first prior probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; determining the second prior probability of the first feature set corresponding to the target object; and calculating based on the first prior probability and the second prior probability to obtain the matching value.

[0055] In an optional embodiment, the following steps may be used to obtain the matching value between each account manager and the target object: for any account manager, the first prior probability of the second feature set corresponding to the account manager may be determined based on the proportion of the second feature set corresponding to the account manager in the second feature sets corresponding to multiple account managers. In order to avoid a too low proportion, the proportion corresponding to the second feature set may be obtained based on the proportion of each feature value in the second feature set.

[0056] Then, a corresponding method can be used to obtain the second prior probability of the first feature set corresponding to the target object, and finally the matching value can be obtained by calculation based on the first prior probability and the second prior probability. For example, the first prior probability and the second prior probability and the first feature set and the second feature set are processed by a deep learning model, and the matching value between each account manager and the target object is output by the deep learning model.

[0057] The matching value between each account manager and the target object can be calculated more accurately through the first prior probability corresponding to the account manager and the second prior probability of the user, thereby improving the accuracy of subsequent recommendations.

[0058] Optionally, in the information recommendation method provided in Example 1 of the present application, calculation is performed based on the first prior probability and the second prior probability to obtain a matching value, including: obtaining a first historical feature set of multiple historical objects, and obtaining a second historical feature set of historical account managers matching each historical object; based on the first historical feature set and the second historical feature set, calculating the conditional probability that the historical object has the first feature set when the account manager has the second feature set to obtain the conditional probability; based on the conditional probability, the first prior probability and the second prior probability, calculating the posterior probability that the account manager has the second feature set when the historical object has the first feature set to obtain a matching value.

[0059] In an optional embodiment, the matching value can be calculated by the Naive Bayes theorem:

[0060] Naive Bayes Theorem: Where P(X|H) is the posterior probability of H under condition X; P(H) is the prior probability of H; P(X|H) is the posterior probability of X under condition H; P(X) is the prior probability of X.

[0061] For example, X is used to represent the first feature set of the user, H is used to represent the second feature set of the account manager, P(X) is the second prior probability, P(H) is the first prior probability, P(X|H) is the conditional probability that the historical object has the first feature set when the account manager has the second feature set, and P(X|H) is the posterior probability that the account manager has the second feature set when the historical object has the first feature set.

[0062] The conditional probability that a historical object has a first feature set when a customer manager has a second feature set can be obtained by analyzing and calculating the first historical feature sets of multiple historical objects and the second historical feature sets of historical customer managers matching each historical object.

[0063] It should be noted that for the above conditional probabilities, in order to reduce the cost of calculating P(X|H), we can make a naive assumption of class conditional independence. The probabilities P(x1|H), P(x2|H), ..., P(xn|H) can be calculated from historical data, where x1, x2, ..., xn represent the feature values ​​in the first feature set.

[0064] Finally, the posterior probability that the account manager has the second feature set when the historical object has the first feature set is calculated based on the first prior probability, the second prior probability and the conditional probability, and the above matching value is obtained based on the posterior probability.

[0065] The matching degree between each account manager and the target object can be more accurately evaluated by using the posterior probability that the account manager has the second feature set when the historical object has the first feature set.

[0066] Optionally, in the information recommendation method provided in Example 1 of the present application, determining the second prior probability of the first feature set corresponding to the target object includes: obtaining a first historical feature set of multiple historical objects; for any feature value in the first feature set, calculating the proportion of the feature value in the first historical feature set to obtain a proportion value; and calculating based on the proportion value to obtain a second prior probability.

[0067] In an optional embodiment, the second prior probability can be calculated by the following steps: obtaining a first historical feature set of multiple historical objects that have conducted business in a financial institution, and then for any feature value in the first feature set, calculating the proportion of each feature value in the first historical feature set to obtain a proportion value, and accumulating the obtained proportion values ​​to obtain the second prior probability.

[0068] By calculating the proportion of each feature value in the first feature set in the first historical feature set, the prior probability of the first feature set can be calculated more accurately, thereby improving the accuracy of subsequent matching.

[0069] Optionally, in the information recommendation method provided in Example 1 of the present application, determining the information of the account manager to be recommended from the information of multiple account managers based on the matching value includes: sorting the information of the multiple account managers based on the matching value to obtain a sorting result; determining the information of the candidate account managers from the information of the multiple account managers based on the sorting result; obtaining the target financial business information to be handled by the target object, and determining the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information.

[0070] In an optional embodiment, determining the information of the account manager to be recommended from the information of multiple account managers includes the following steps: firstly sorting the information of multiple account managers according to the matching value, and then determining the information of the candidate account manager from the sorting result by setting the corresponding matching threshold. Then obtaining the target financial business information to be handled by the target object, and determining the information of the account manager to be recommended from the information of the candidate account manager according to the target financial business information. For example, the information of the account manager to be recommended is determined according to the historical evaluation of the candidate account manager in handling the target financial business information.

[0071] After obtaining the candidate account manager information based on the matching value, a secondary screening is performed based on the target financial business information, which can further improve the accuracy of recommending account managers.

[0072] Optionally, in the information recommendation method provided in Example 1 of the present application, determining the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information includes: obtaining historical financial business information that the candidate account manager has handled; and determining the information of the account manager to be recommended from the information of the candidate account managers based on the historical financial business information and the target financial business information.

[0073] In an optional embodiment, determining the information of the account manager to be recommended from the information of the candidate account managers according to the target financial business information includes the following steps: obtaining the historical financial business information handled by the candidate account manager, and then determining the information of the account manager to be recommended from the information of the candidate account manager according to the historical financial business information and the target financial business information. For example, the information of the account manager to be recommended is determined from the information of the candidate account manager according to the degree of matching between the historical financial business information and the target financial business information.

[0074] Historical financial business information and target financial business information can further improve the matching degree between account managers and customers and improve the accuracy of recommended account manager information.

[0075] In an optional embodiment, the Figure 3 The flowchart shown realizes the recommendation of customer manager information: obtaining the customer's evaluation score in the "extroversion-introversion" dimension, the evaluation score in the "sensing-intuition" dimension, the tester's evaluation score in the "thinking-feeling" dimension, and the evaluation score in the "judgment-perception" dimension, as shown in Table 1:

[0076] variable X1 X2 X3 X4 X5 value a b c d e

[0077] X1 represents the test-taker's evaluation score a in the dimension of "extraversion E-introversion I"; X2 represents the test-taker's evaluation score b in the dimension of "sensation S-intuition N"; X3 represents the test-taker's evaluation score c in the dimension of "thinking T-feeling F"; X4 represents the test-taker's evaluation score d in the dimension of "judgment J-perception P". Set variable X5 as the test-taker's age variable, and e as its variable value. According to the above variable values, the customer's feature set and the account manager's feature set are obtained. Finally, according to the naive Bayes theorem, the probability of the account manager and the customer's fit is calculated based on the customer's feature set and the account manager's feature set. Finally, the account manager to be recommended is determined based on the fit probability.

[0078] The information recommendation method provided in the embodiment of the present application determines the first feature set corresponding to the target object based on the personality test data when there is personality test data of the target object, and the target object is the object to be handled by the financial business; determines the second feature set corresponding to each account manager according to the personality test data of multiple account managers; calculates the first feature set and the second feature set to obtain the matching value between each account manager and the target object; determines the information of the account manager to be recommended from the information of multiple account managers according to the matching value, and pushes the information of the account manager to be recommended to the target object, which solves the technical problem that the related technology recommends the information of the account manager to handle the business for the customer based on its own experience, resulting in a relatively low recommendation accuracy. In this scheme, the first feature set of the target object and the second feature set of the account manager are obtained by effectively quantifying the personal personality factors through the personality test data, and then the matching value between the account manager and the target object is calculated according to the first feature set and the second feature set, and finally, according to the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object, and the first feature set and the second feature set can more accurately describe the characteristics of the account manager and the user, avoiding the judgment bias problem caused by subjective experience, thereby achieving the technical effect of improving the recommendation accuracy.

[0079] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0080] Example 2

[0081] The present application also provides an information recommendation device. It should be noted that the information recommendation device of the present application can be used to execute the information recommendation method provided by the present application. The information recommendation device provided by the present application is introduced below.

[0082] According to an embodiment of the present application, a device for implementing the above-mentioned information recommendation method is also provided, such as Figure 4 As shown, the device includes: a first determining unit 401, a second determining unit 402, a calculating unit 403 and a third determining unit 404.

[0083] A first determining unit 401 is used to determine a first feature set corresponding to the target object based on the personality test data when personality test data of the target object exists, where the target object is a subject for which financial business is to be processed;

[0084] A second determining unit 402 is used to determine a second feature set corresponding to each account manager based on the personality test data of the multiple account managers;

[0085] A calculation unit 403 is used to calculate the first feature set and the second feature set to obtain a matching value between each account manager and the target object;

[0086] The third determining unit 404 is used to determine the information of the account manager to be recommended from the information of the multiple account managers according to the matching value, and push the information of the account manager to be recommended to the target object.

[0087] The information recommendation device provided in the embodiment of the present application determines, through a first determination unit 401, a first feature set corresponding to the target object based on the personality test data when there is personality test data of the target object, and the target object is the object of the financial business to be processed; a second determination unit 402 determines a second feature set corresponding to each account manager based on the personality test data of multiple account managers; a calculation unit 403 calculates the first feature set and the second feature set to obtain a matching value between each account manager and the target object; a third determination unit 404 determines the information of the account manager to be recommended from the information of the multiple account managers based on the matching value, and pushes the information of the account manager to be recommended to the target object, thereby solving the technical problem that the related technology recommends the information of the account manager for handling business to the customer based on its own experience, resulting in a relatively low recommendation accuracy. In this solution, personal personality factors are effectively quantified through personality test data to obtain a first feature set of the target object and a second feature set of the account manager, and then the matching value between the account manager and the target object is calculated based on the first feature set and the second feature set. Finally, based on the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object. The first feature set and the second feature set can more accurately describe the characteristics of the account manager and the user, avoiding the problem of judgment bias caused by subjective experience, thereby achieving the technical effect of improving the accuracy of recommendations.

[0088] Optionally, in the information recommendation device provided in Example 2 of the present application, the first determination unit includes: a first determination module, used to determine the score value of the target object in multiple personality dimensions based on the personality test data; a first acquisition module, used to obtain attribute information of the target object, wherein the attribute information at least includes the age information of the target object; a conversion module, used to perform numerical conversion on the attribute information to obtain a target value; and a second determination module, used to determine the first feature set based on the score value and the target value.

[0089] Optionally, in the information recommendation device provided in Example 2 of the present application, the calculation unit includes: a first calculation module, used to calculate, for a target account manager, the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers, to obtain a first prior probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; a third determination module, used to determine the second prior probability of the first feature set corresponding to the target object; and a second calculation module, used to perform calculations based on the first prior probability and the second prior probability to obtain a matching value.

[0090] Optionally, in the information recommendation device provided in Example 2 of the present application, the second calculation module includes: a second acquisition module, used to obtain a first historical feature set of multiple historical objects, and obtain a second historical feature set of historical account managers matching each historical object; a third calculation module, used to calculate the conditional probability that the historical object has the first feature set when the account manager has the second feature set based on the first historical feature set and the second historical feature set, to obtain the conditional probability; and a fourth calculation module, used to calculate the posterior probability that the account manager has the second feature set when the historical object has the first feature set based on the conditional probability, the first prior probability and the second prior probability, to obtain a matching value.

[0091] Optionally, in the information recommendation device provided in Example 2 of the present application, the third determination module includes: a first acquisition submodule, used to acquire a first historical feature set of multiple historical objects; a first calculation submodule, used to calculate the proportion of the feature value in the first historical feature set for any feature value in the first feature set to obtain a proportion value; and a second calculation submodule, used to calculate based on the proportion value to obtain a second prior probability.

[0092] Optionally, in the information recommendation device provided in Example 2 of the present application, the third determination unit includes: a processing module, used to sort the information of multiple account managers according to the matching value to obtain a sorting result; a fourth determination module, used to determine the information of candidate account managers from the information of multiple account managers based on the sorting result; a third acquisition module, used to obtain the target financial business information to be handled by the target object, and determine the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information.

[0093] Optionally, in the information recommendation device provided in Example 2 of the present application, the third acquisition module includes: a second acquisition sub-module, used to obtain historical financial business information handled by the candidate account manager; and a determination sub-module, used to determine the information of the account manager to be recommended from the information of the candidate account managers based on the historical financial business information and the target financial business information.

[0094] It should be noted that the first determination unit 401, the second determination unit 402, the calculation unit 403 and the third determination unit 404 correspond to steps S201 to S204 in the first embodiment, and the four units and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in the first embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above modules can also be part of the device and can be run in the computer terminal 10 provided in the first embodiment.

[0095] Example 3

[0096] An embodiment of the present application may provide an electronic device, Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (only one is shown) processor 502, memory 504, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0097] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0098] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: in the case where there is personality test data of the target object, determine the first feature set corresponding to the target object based on the personality test data, and the target object is the object of the financial business to be processed; based on the personality test data of multiple account managers, determine the second feature set corresponding to each account manager; calculate the first feature set and the second feature set to obtain the matching value between each account manager and the target object; based on the matching value, determine the information of the account manager to be recommended from the information of the multiple account managers, and push the information of the account manager to be recommended to the target object.

[0099] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the personality test data, determine the first feature set corresponding to the target object, including: based on the personality test data, determine the score value of the target object in multiple personality dimensions; obtain the attribute information of the target object, wherein the attribute information at least includes the age information of the target object; perform numerical conversion on the attribute information to obtain a target value; and determine the first feature set based on the score value and the target value.

[0100] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculate the first feature set and the second feature set to obtain the matching value between each account manager and the target object, including: for the target account manager, calculate the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers to obtain the first prior probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; determine the second prior probability of the first feature set corresponding to the target object; calculate based on the first prior probability and the second prior probability to obtain the matching value.

[0101] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: calculate based on the first prior probability and the second prior probability to obtain a matching value including: obtaining a first historical feature set of multiple historical objects, and obtaining a second historical feature set of historical account managers matching each historical object; based on the first historical feature set and the second historical feature set, calculate the conditional probability that the historical object has the first feature set when the account manager has the second feature set to obtain the conditional probability; based on the conditional probability, the first prior probability and the second prior probability, calculate the posterior probability that the account manager has the second feature set when the historical object has the first feature set to obtain the matching value.

[0102] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: determining the second prior probability of the first feature set corresponding to the target object includes: obtaining the first historical feature set of multiple historical objects; for any feature value in the first feature set, calculating the proportion of the feature value in the first historical feature set to obtain the proportion value; calculating based on the proportion value to obtain the second prior probability.

[0103] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the matching value, determine the information of the account manager to be recommended from the information of multiple account managers, including: sorting the information of multiple account managers based on the matching value to obtain the sorting result; based on the sorting result, determine the information of candidate account managers from the information of multiple account managers; obtain the target financial business information to be handled by the target object, and determine the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information.

[0104] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determining the information of the account manager to be recommended from the information of the candidate account managers based on the target financial business information, including: obtaining the historical financial business information handled by the candidate account manager; determining the information of the account manager to be recommended from the information of the candidate account managers based on the historical financial business information and the target financial business information.

[0105] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, and other terminal devices. Figure 5 The structure of the electronic device is not limited. Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations are shown.

[0106] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0107] Example 4

[0108] The embodiment of the present application further provides a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the information recommendation method provided in the first embodiment.

[0109] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0110] The present application also provides a computer program product which, when executed on a data processing device, is suitable for executing the steps of the recommended method of information.

[0111] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0112] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0117] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for recommending information, characterized in that: include: In the case where personality test data of a target object exists, determining a first feature set corresponding to the target object based on the personality test data, wherein the target object is a subject for which financial business is to be processed; Determine a second feature set corresponding to each account manager based on personality test data of the plurality of account managers; Calculating the first feature set and the second feature set to obtain a matching value between each account manager and the target object; According to the matching value, the information of the account manager to be recommended is determined from the information of multiple account managers, and the information of the account manager to be recommended is pushed to the target object.

2. The method according to claim 1, characterized in that Determining, based on the personality test data, a first feature set corresponding to the target object includes: Determining the score values ​​of the target object in multiple personality dimensions based on the personality test data; Acquire attribute information of the target object, wherein the attribute information at least includes age information of the target object; Performing numerical conversion on the attribute information to obtain a target numerical value; The first feature set is determined according to the score value and the target value.

3. The method according to claim 1, characterized in that: The first feature set and the second feature set are calculated to obtain a matching value between each account manager and the target object, including: For a target account manager, the proportion of the second feature set corresponding to the target account manager in the second feature sets corresponding to multiple account managers is calculated to obtain a first priori probability of the second feature set corresponding to the target account manager, wherein the target account manager is any one of the multiple account managers; Determine a second prior probability of the first feature set corresponding to the target object; The matching value is obtained by performing calculation according to the first priori probability and the second priori probability.

4. The method according to claim 3, characterized in that Calculating according to the first prior probability and the second prior probability to obtain the matching value includes: Obtain a first historical feature set of multiple historical objects, and obtain a second historical feature set of a historical account manager matching each historical object; According to the first historical feature set and the second historical feature set, a conditional probability that the historical object has the first feature set when the account manager has the second feature set is calculated to obtain a conditional probability; Based on the conditional probability, the first prior probability and the second prior probability, the posterior probability that the account manager has the second feature set when the historical object has the first feature set is calculated to obtain the matching value.

5. The method according to claim 3, characterized in that: Determining a second priori probability of the first feature set corresponding to the target object includes: Obtaining a first history feature set of a plurality of history objects; For any feature value in the first feature set, calculate the proportion of the feature value in the first historical feature set to obtain a proportion value; The second priori probability is obtained by performing calculation according to the ratio value.

6. The method according to claim 1, characterized in that Determining the information of the account manager to be recommended from the information of the plurality of account managers according to the matching value includes: Sorting the information of the plurality of account managers according to the matching values ​​to obtain a sorting result; Determining information of candidate account managers from information of the multiple account managers according to the ranking result; The target financial business information to be handled by the target object is obtained, and the information of the account manager to be recommended is determined from the information of the candidate account managers according to the target financial business information.

7. The method according to claim 6, characterized in that Determining the information of the account manager to be recommended from the information of the candidate account managers according to the target financial business information includes: Obtaining historical financial business information handled by the candidate account manager; The information of the account manager to be recommended is determined from the information of the candidate account managers based on the historical financial business information and the target financial business information.

8. An information recommendation device, characterized in that: include: A first determining unit is used to determine, if there is personality test data of a target object, a first feature set corresponding to the target object based on the personality test data, wherein the target object is a subject for which financial business is to be processed; A second determining unit, configured to determine a second feature set corresponding to each account manager based on the personality test data of the plurality of account managers; A calculation unit, used to calculate the first feature set and the second feature set to obtain a matching value between each account manager and the target object; The third determining unit is used to determine the information of the account manager to be recommended from the information of multiple account managers according to the matching value, and push the information of the account manager to be recommended to the target object.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the computer-readable storage medium is located is controlled to execute the information recommendation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program, when running, executes the information recommendation method described in any one of claims 1 to 7.

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