CN112488854B - Personalized recommendation method for service manager and related equipment - Google Patents

Abstract

One or more embodiments of this specification provide a service manager personalized recommendation method and related equipment. The method includes: first obtaining the user's identification and location information, and the identification and location information are used to reflect different attributes of the user. Then, other users similar to the target user are filtered out through identification and location information and the similarity between them is calculated. Based on the calculated similarity, service managers suitable for the target users are selected and recommended. This method comprehensively considers user-related information and obtains service managers suitable for customers based on similarity. It solves the problem of too simple matching rules for service managers and unbalanced allocation of service managers to users, thereby allowing service managers to give full play to their experience and provide suitable service managers for users. Customer service realizes the function of effectively guiding users and recommending service managers to users in a personalized manner.

Description

Personalized recommendation methods and related equipment for service managers

Technical field

One or more embodiments of this specification relate to the field of computer technology, and in particular, to a service manager personalized recommendation method and related equipment.

Background technique

At present, the research and development of mobile online products in the insurance field mostly focuses on the sales of insurance and financial management products, the expansion of life service functions and the promotion of new activities. In terms of solving customers' consultation and service needs, they often rely on functions such as "intelligent assistant robots". However, in the current insurance industry, such functions can only understand simple question semantics and cannot provide customers with satisfactory solutions to complex problems. answer. Service managers are essentially real-person service managers and agents for insurance products. Users can bind their own service managers through the mobile app and raise various questions and consultations with the service managers online.

At present, most of the service manager recommendation methods in the insurance industry only rely on geographical location and some simple recommendation rules, such as confirming the municipal administrative area where the user is located through the geographical location, and selecting several service managers with better sales in this large area. Make recommendations, or make recommendations based on some simple rules, such as the service manager's activity in using the software, etc. This type of recommendation method will cause the system to always recommend a fixed number of the most active service managers, which will not only increase the pressure on the service managers, but also reduce the customer's personal service experience. At the same time, it is also a waste of resources for marketers who are also excellent in other sales situations and do not become service managers. If the method of randomly recommending service managers is used, although the chance of the service manager being bound is fairer, this will lose the meaning of personalized services.

Based on this, a personalized recommendation solution is needed that can achieve effective guidance and improve the efficiency of matching customers and service managers.

Contents of the invention

In view of this, the purpose of one or more embodiments of this specification is to propose a personalized recommendation method and related equipment for service managers to solve the above problems.

Based on the above purpose, one or more embodiments of this specification provide a personalized recommendation method for service managers, including:

Obtain the target user's identification and location information through the terminal device;

Retrieve the personal information and business tags of the target user from the business database according to the identification;

According to the location information, the personal information and the business tag of the target user, retrieve the identifiers and business tags of analog users similar to the target user from the business database according to predetermined filtering conditions;

Calculate the similarity between each analog user and the target user according to a predetermined similarity algorithm according to the retrieved business tags of the target user and the business tags of the analog users;

Rank each of the analog users in descending order of the similarity, and determine a predetermined number of top-ranked analog users;

According to the respective identifiers of the predetermined number of analog users, retrieve the employee identifiers and service information of the service managers of the predetermined number of analog users from the business database;

According to the retrieved employee identification and service information of the service manager, the information of the service manager recommended for the target user is pushed to the terminal device.

Based on the same inventive concept, one or more embodiments of this specification also provide a personalized recommendation device for service managers, including:

The acquisition module is configured to obtain the identification and location information of the target user through the terminal device;

The first retrieval module is configured to retrieve the personal information and business tags of the target user from the business database according to the identification;

The second retrieval module is configured to retrieve the identifiers and identifiers of analog users similar to the target user from the business database according to predetermined filtering conditions according to the location information, the personal information and the business tag of the target user. business tag;

The calculation module is configured to calculate the similarity between each of the analog users and the target user according to a predetermined similarity algorithm based on the retrieved business tags of the target user and the business tags of the analog users;

A ranking module configured to rank each of the analog users in descending order of the similarity, and determine a predetermined number of top-ranked analog users;

The third retrieval module is configured to retrieve the employee identifications and service information of the service managers of the predetermined number of analog users from the business database based on the respective identifications of the predetermined number of analog users;

The push module is configured to push the information of the service manager recommended for the target user to the terminal device based on the retrieved employee identification and service information of the service manager.

Based on the same inventive concept, one or more embodiments of this specification also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program. When implementing any of the above methods.

Based on the same inventive concept, one or more embodiments of this specification also provide a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the The computer performs any of the methods described above.

As can be seen from the above, a service manager personalized recommendation method and related equipment provided by one or more embodiments of this specification comprehensively consider the user's geographical location, gender, age and business tags, and obtain a suitable recommendation through similarity calculation. The ranking information of the customer's service manager allows the service manager to give full play to his experience and serve the right customers, allowing more service managers to get the opportunity to serve customers, thereby achieving effective guidance for users and improving the business conversion rate.

Description of the drawings

In order to more clearly illustrate one or more embodiments of this specification or technical solutions in the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, in the following description The drawings are only one or more embodiments of this specification. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of a service manager personalized recommendation method according to one or more embodiments of this specification;

Figure 2 is a schematic structural diagram of a service manager personalized recommendation device according to one or more embodiments of this specification;

Figure 3 is a schematic structural diagram of an electronic device according to one or more embodiments of this specification.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present disclosure more clear, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of this specification should have the usual meanings understood by those with ordinary skills in the field to which this disclosure belongs. The "first", "second" and similar words used in one or more embodiments of this specification do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "include" or "comprising" mean that the elements or things appearing before the word include the elements or things listed after the word and their equivalents, without excluding other elements or things.

One or more embodiments of this specification provide a personalized recommendation solution for service managers. Specifically, the user's identification and location information are first obtained, and the identification and location information are used to reflect different attributes of the user. Then, other users similar to the target user are filtered out through identification and location information and the similarity between them is calculated. Based on the calculated similarity, service managers suitable for the target users are selected and recommended.

It can be seen that one or more embodiments of this specification provide a personalized recommendation solution for service managers that comprehensively considers user-related information and obtains a service manager suitable for the customer through similarity calculation, thereby allowing the service manager to give full play to his experience and provide the appropriate service manager. Customer service, thereby achieving effective guidance for users and improving business conversion rate.

Below, the technical solutions of one or more embodiments of this specification will be described in detail through specific examples.

Referring to Figure 1, a personalized recommendation method for service managers according to one embodiment of this specification includes the following steps:

Step S101: Obtain the target user's identification and location information through the terminal device.

Step S102: Retrieve the target user's personal information and business tags from the business database according to the identification.

This location and personal information can be used to profile a user through several factors. Specifically, it can indicate the user's natural or social attributes, such as gender, age, occupation, geographical location, etc.; according to the actual business situation, the age group can be divided; the actual business situation here, for example, users between 28 and 32 years old have stronger The possibility of purchasing a certain type of product can be divided into an age group from 28 to 32 years old. The specific division needs to be based on the actual business situation of the insurance company.

Business tags can be user tags generated based on user browsing behavior and purchasing behavior; according to the actual insurance product sales of insurance companies, they can be divided into multiple tags, such as pension, accidental injury, children, dividends, etc., without user corresponding When the user behaves, the corresponding tags of the user are set to the default value 0; for example, if a user has browsed children's insurance products and purchased dividend insurance, his business tags "Children" and "Dividends" will be assigned a value of 1, and other business tags will be assigned a value of 1. The business label is the default value 0.

Step S103: According to the location information, the personal information and the business tag of the target user, retrieve the identifiers and business tags of analog users similar to the target user from the business database according to predetermined filtering conditions.

In this embodiment, analog users are screened out from the database based on geographical location, gender, age group and business tags. Specifically, for example, there are a total of 10 tags A, B, C, D, E, F, G, H, I, and J (for example, A replaces dividends and E replaces children). This user only has tag values of A, C, and H. is 1, then when filtering analog users in the database, find all users with the same geographical location, gender, and age group as the user, narrowing the search range. Next, in this small range, filter the "label A" that meets the judgment conditions "All users whose value is 1 or tag C value is 1 or tag H value is 1", the logical relationship needs to use "or" in order to find all similar users; the filtered analog user information needs to include each user's Label situation.

Step S104: Calculate the similarity between each analog user and the target user according to a predetermined similarity algorithm based on the retrieved business tags of the target user and the business tags of the analog users.

In this embodiment, specifically, first, a business tag matrix required for similarity calculation is constructed based on the business tags of the target user and the business tags of the analog users filtered out in the database; each row of the matrix includes the user name, business Label 1, business label 2 to business label n. Each row of records in the matrix is the target user information and all analog user information filtered out in the database. Then, the target user's business tag (tag attribute 1, tag attribute 2 to tag attribute n) constitutes the vector X, that is, the first row of the matrix except the first column constitutes the vector Tag attribute 2 to tag attribute n) constitute vectors Y ₁ , Y ₂ to Y _k , that is, the matrix except the first column and first row of other row data constitute vectors Y ₁ , Y ₂ to Y _k . The vector X and the vectors Y ₁ , Y ₂ to Y _k are respectively calculated to obtain the respective similarities between the target user and all analog users. The statistical formula used in this embodiment takes the Pearson correlation coefficient formula as an example:

_Among them _{, ρ}

As an optional embodiment, you can also use other statistical formulas or other optimization methods when using collaborative filtering. The purpose is to calculate a reasonable user similarity based on the current business scenario and data situation.

Step S105: Rank each of the analog users in descending order of similarity, and determine a predetermined number of top-ranked analog users.

In this step, the scheduled quantity can be adjusted specifically based on the company's business conditions and staffing.

Step S106: According to the respective identifiers of the predetermined number of analog users, retrieve the employee identifiers and service information of the service managers of the predetermined number of analog users from the business database.

In this embodiment, ranking high in similarity means that these service managers have served other users who are very similar to the target user and have certain experience. Specifically, the service information can be data such as the service manager's business activity, the number of active users that the service manager has been bound to, etc. Appropriate relevant evaluation indicators can be selected based on the actual business situation and statistical data of the insurance company.

Step S107: Push the information of the service manager recommended for the target user to the terminal device according to the retrieved employee identification and service information of the service manager.

In this embodiment, there is no limit on the quantity of service information. It can be based on a single indicator, or it can be based on weighted calculations of two or more indicators (for example, values such as "activity", "working years", "business situation in the last quarter", etc. The indicators are weighted and calculated, and the weights can be adjusted according to the actual situation), and then sorted and recommended. The sorting method can select any common sorting algorithm (for example, using bubble sorting, insertion sorting and other algorithms), and sort from high to low.

It can be seen that in this embodiment, user-related information is comprehensively considered, and a service manager suitable for the customer is obtained based on similarity, which solves the problem of too simple matching rules for service managers and uneven allocation of service managers to users, thereby allowing service managers to give full play to their capabilities. Experience, serving the right customers, achieving effective guidance for users, improving business conversion rate, and personalized recommendation of service managers for users.

It should be noted that the methods in one or more embodiments of this specification can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, and is completed by multiple devices cooperating with each other. In this distributed scenario, one device among the multiple devices can only perform one or more steps in the method of one or more embodiments of this specification, and the multiple devices will perform interoperability with each other. Interact to complete the method described.

It should be noted that the above has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order shown, or sequential order, to achieve desirable results. Multitasking and parallel processing are also possible or may be advantageous in certain implementations.

Based on the same inventive concept, corresponding to the methods of any of the above embodiments, one or more embodiments of this specification also provide a personalized recommendation device for service managers.

Referring to Figure 2, the service manager's personalized recommendation device includes:

The acquisition module 201 is configured to obtain the identification and location information of the target user through the terminal device;

The first retrieval module 202 is configured to retrieve the personal information and business tags of the target user from the business database according to the identification;

The second retrieval module 203 is configured to retrieve the identification of analog users similar to the target user from the business database according to predetermined filtering conditions based on the location information, the personal information and the business tag of the target user. and business tags;

The calculation module 204 is configured to calculate the similarity between each analog user and the target user according to a predetermined similarity algorithm based on the retrieved business tags of the target user and the business tags of the analog users;

The ranking module 205 is configured to rank each of the analog users in descending order of the similarity, and determine a predetermined number of top-ranked analog users;

The third retrieval module 206 is configured to retrieve the employee IDs and service information of the service managers of the predetermined number of analog users from the business database based on the respective IDs of the predetermined number of analog users;

The push module 207 is configured to push the information of the service manager recommended for the target user to the terminal device based on the retrieved employee identification and service information of the service manager.

As an optional embodiment, the identification and location information are specifically configured as the user's geographical location, the user's gender, the user's age and the user's business tag; wherein the user's business tag is configured to be based on the user's browsing behavior and purchasing behavior.

As an optional embodiment, the similarity calculation module is specifically configured to construct a business tag matrix based on the business tags of the target user and the business tags of analog users filtered out in the database; calculate according to the business tag matrix Similarity.

As an optional embodiment, the calculation module is specifically configured to construct a business tag matrix based on the business tags of the target user and the business tags of the analog users filtered out in the database; and calculate similarity based on the business tag matrix. Spend.

For the convenience of description, when describing the above device, the functions are divided into various modules and described separately. Of course, when implementing one or more embodiments of this specification, the functions of each module can be implemented in the same or multiple pieces of software and/or hardware.

The devices of the above embodiments are used to implement the corresponding service manager personalized recommendation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be described again here.

Based on the same inventive concept, corresponding to the methods of any of the above embodiments, one or more embodiments of this specification also provide an electronic device, including a memory, a processor, and a computer stored in the memory and capable of running on the processor. Program, when the processor executes the program, the service manager personalized recommendation method described in any of the above embodiments is implemented.

FIG. 3 shows a more specific hardware structure diagram of an electronic device provided by this embodiment. The device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input/output interface 1030 and the communication interface 1040 implement communication connections between each other within the device through the bus 1050.

The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute related tasks. program to implement the technical solutions provided by the embodiments of this specification.

The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store operating systems and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and called and executed by the processor 1010 .

The input/output interface 1030 is used to connect the input/output module to realize information input and output. The input/output/module can be configured in the device as a component (not shown in the figure), or can be externally connected to the device to provide corresponding functions. Input devices can include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices can include monitors, speakers, vibrators, indicator lights, etc.

The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize communication interaction between this device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

Bus 1050 includes a path that carries information between various components of the device (eg, processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).

It should be noted that although the above device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, during specific implementation, the device may also include necessary components for normal operation. Other components. In addition, those skilled in the art can understand that the above-mentioned device may only include components necessary to implement the embodiments of this specification, and does not necessarily include all components shown in the drawings.

The electronic devices of the above embodiments are used to implement the corresponding service manager personalized recommendation method in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be described again here.

Based on the same inventive concept, corresponding to any of the above embodiment methods, one or more embodiments of this specification also provide a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores computer instructions , the computer instructions are used to cause the computer to execute the service manager personalized recommendation method as described in any of the above embodiments.

The computer-readable media in this embodiment include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information may be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), and read-only memory. (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, Magnetic tape cassettes, tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium can be used to store information that can be accessed by a computing device.

The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the service manager personalized recommendation method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be described again here.

Those of ordinary skill in the art should understand that the discussion of any above embodiments is only illustrative, and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the spirit of the present disclosure, the above embodiments or Technical features in different embodiments can also be combined, steps can be implemented in any order, and there are many other variations in different aspects of one or more embodiments of this specification as described above, which are not included in the details for the sake of simplicity. supply.

Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art from the foregoing description. For example, other memory architectures such as dynamic RAM (DRAM) may use the discussed embodiments.

This description of one or more embodiments is intended to embrace all such alternatives, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the protection scope of this disclosure.

Claims (8)

1. A personalized recommendation method for service managers, which is characterized by including: Obtain the target user's identification and location information through the terminal device; Retrieve the personal information and business tags of the target user from the business database according to the identification; According to the location information, the personal information and the business tag of the target user, retrieve the identifiers and business tags of analog users similar to the target user from the business database according to predetermined filtering conditions; According to the retrieved business tags of the target user and the business tags of the analog users, the similarity between each analog user and the target user is calculated according to a predetermined similarity algorithm. The similarity is calculated based on the retrieved business tags of the target users and Analog users' business tags, and calculate the similarity between each analog user and the target user according to a predetermined similarity algorithm, including: Construct a business tag matrix according to the business tags of the target user and the business tags of the analog user; According to the business tag matrix, use a predetermined statistical formula or use a collaborative filtering method to calculate the similarity, and the predetermined statistical formula is the Pearson correlation coefficient formula; Rank each of the analog users in descending order of the similarity, and determine a predetermined number of top-ranked analog users; According to the respective identifiers of the predetermined number of analog users, retrieve the employee identifiers and service information of the service managers of the predetermined number of analog users from the business database; According to the retrieved employee identification and service information of the service manager, the information of the service manager recommended for the target user is pushed to the terminal device. 2. The method according to claim 1, characterized in that the business tag is generated according to the business web page browsing behavior and/or business purchasing behavior of the corresponding user. 3. The method according to claim 1, wherein the predetermined filtering conditions include: the analog user and the target user have the same geographical location, gender, age group and at least one business tag. 4. The method according to any one of claims 1 to 3, characterized in that, based on the retrieved employee identification and service information of the service manager, push the service manager recommended for the target user to the terminal device. information, including: Rank the service managers according to at least one service indicator in the service information; According to the ranking result, the information of a part of the service managers is pushed to the terminal device. 5. A personalized recommendation device for service managers, which is characterized by including: The acquisition module is configured to obtain the identification and location information of the target user through the terminal device; The first retrieval module is configured to retrieve the personal information and business tags of the target user from the business database according to the identification; The second retrieval module is configured to retrieve the identifiers and identifiers of analog users similar to the target user from the business database according to predetermined filtering conditions according to the location information, the personal information and the business tag of the target user. business tag; The calculation module is configured to calculate the similarity between each of the analog users and the target user according to a predetermined similarity algorithm based on the retrieved business tags of the target user and the business tags of the analog users. The business tags of the target user and the business tags of the analog users are calculated according to a predetermined similarity algorithm to calculate the similarity between each analog user and the target user, including: Construct a business tag matrix according to the business tags of the target user and the business tags of the analog user; According to the business tag matrix, use a predetermined statistical formula or use a collaborative filtering method to calculate the similarity, and the predetermined statistical formula is the Pearson correlation coefficient formula; A ranking module configured to rank each of the analog users in descending order of the similarity, and determine a predetermined number of top-ranked analog users; The third retrieval module is configured to retrieve the employee identifications and service information of the service managers of the predetermined number of analog users from the business database based on the respective identifications of the predetermined number of analog users; The push module is configured to push the information of the service manager recommended for the target user to the terminal device based on the retrieved employee identification and service information of the service manager. 6. The device according to claim 5, wherein the service tag is generated according to the service web page browsing behavior and/or service purchasing behavior of the corresponding user. 7. An electronic device, comprising a memory, a processor and a computer program stored in the memory and executable by the processor, characterized in that when the processor executes the computer program, the processor implements the method according to claims 1 to 1. The method described in any one of 4. 8. A non-transitory computer-readable storage medium, characterized in that computer instructions are stored on the storage medium, and when executed by a computer, the computer instructions cause the computer to implement any of claims 1 to 4. method described in one item.