CN112488854B - Personalized recommendation method for service manager and related equipment - Google Patents
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Abstract
One or more embodiments of the present specification provide a service manager personalized recommendation method and related devices. The method comprises the following steps: first, the identification and location information of the user is obtained, which is used to reflect different attributes of the user. Then, other users close to the target user are screened out through the identification and the position information, and the similarity between the other users is calculated. And selecting a service manager suitable for the target user according to the calculated similarity and recommending the service manager. The method comprehensively considers the relevant information of the user, obtains the service manager suitable for the client according to the similarity, solves the problem that the matching rule of the service manager is too simple, and distributes the service manager for the user so as to lead the service manager to give full play to own experience, serve the suitable client, realize effective guidance for the user and personally recommend the service manager for the user.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer technology, and in particular, to a service manager personalized recommendation method and related devices.
Background
At present, the research and development of mobile phone end online products in the insurance field mostly focuses on the sales of insurance and financial products, the expansion of life service functions and the popularization of new activities. In terms of solving the consultation and service requirements of clients, the functions such as an intelligent assistant robot are often relied on, but in the current insurance industry, the functions can only understand simple problem semantics, and for complex problems, satisfactory solutions cannot be provided for clients. The service manager is essentially a real service manager and an agent of the insurance product, and a user can bind the service manager by the APP of the mobile phone end to provide various questions and consultations for the service manager on line.
The current recommendation modes of service managers in the insurance industry mostly depend on geographic positions and some simple recommendation rules, for example, the urban administrative region where users are located is confirmed through the geographic positions, and several service managers with better sales conditions are selected to make recommendation in the large region, or the recommendation is made through some simple rules, for example, the activity of the service managers using software and the like. Such recommendation methods can cause the system to always recommend a fixed number of service managers with highest liveness, which can not only increase the pressure of the service manager, but also reduce the private service experience of the customer. Meanwhile, the marketing staff with excellent sales conditions is not a service manager and is also a resource waste. If a random recommendation service manager mode is used, although the opportunity that the service manager is bound is fairer, the meaning of personalized service is lost.
Based on this, there is a need for a personalized recommendation scheme that enables efficient guidance and improves the efficiency of customer and service manager matching.
Disclosure of Invention
In view of this, it is an object of one or more embodiments of the present specification to propose a service manager personalized recommendation method and related devices to solve the above-mentioned problems.
In view of the above objects, one or more embodiments of the present specification provide a service manager personalized recommendation method, including:
acquiring the identification and the position information of a target user through terminal equipment;
retrieving personal information and service labels of the target users from the service database according to the identification;
according to the position information, the personal information and the service label of the target user, searching the identification and the service label of the analog user similar to the target user from the service database according to a preset screening condition;
calculating the similarity between each analog user and the target user according to a preset similarity algorithm according to the searched service label of the target user and the service label of the analog user;
ranking each of the analog users in descending order of the similarity, determining a predetermined number of analog users that are top ranked;
retrieving employee identification and service information of a service manager of each of the predetermined number of analog users from the service database according to the respective identification of the predetermined number of analog users;
and pushing information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and service information of the service manager.
Based on the same inventive concept, one or more embodiments of the present specification further provide a service manager personalized recommendation device, including:
the acquisition module is configured to acquire the identification and the position information of the target user through the terminal equipment;
a first retrieval module configured to retrieve personal information and a service tag of the target user from the service database according to the identification;
a second search module configured to search out identification of an analog user and a service tag similar to the target user from the service database according to a predetermined screening condition based on the location information, the personal information and the service tag of the target user;
a calculation module configured to calculate a similarity between each analog user and the target user according to a predetermined similarity algorithm based on the retrieved service tag of the target user and the service tag of the analog user;
a ranking module configured to rank each of the analog users in descending order of the similarity, determining a predetermined number of analog users that are top ranked;
a third retrieval module configured to retrieve employee identification and service information of a respective service manager of the predetermined number of analog users from the business database based on the respective identifications of the predetermined number of analog users;
and the pushing module is configured to push the information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and the service information of the service manager.
Based on the same inventive concept, one or more embodiments of the present specification also provide an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method as described in any one of the above when executing the program.
Based on the same inventive concept, one or more embodiments of the present specification also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform any of the methods described above.
From the above, it can be seen that, according to the personalized recommendation method and related devices for a service manager provided in one or more embodiments of the present disclosure, the geographic location, sex, age and service label of a user are comprehensively considered, and ranking information of the service manager suitable for the client is obtained through similarity calculation, so that the service manager gives full play to its own experience, serves the suitable client, and allows more service managers to obtain opportunities for serving the client, thereby realizing effective guidance for the user, and improving the conversion rate of the service.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only one or more embodiments of the present description, from which other drawings can be obtained, without inventive effort, for a person skilled in the art.
FIG. 1 is a flow diagram of a personalized recommendation method for a service manager in accordance with one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a personalized recommendation device for a service manager in one or more embodiments of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
It is noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present disclosure should be taken in a general sense as understood by one of ordinary skill in the art to which the present disclosure pertains. The use of the terms "first," "second," and the like in one or more embodiments of the present description does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items.
One or more embodiments of the present disclosure provide a personalized recommendation scheme for a service manager, specifically, first obtain identification and location information of a user, where the identification and location information are used to reflect different attributes of the user. Then, other users close to the target user are screened out through the identification and the position information, and the similarity between the other users is calculated. And selecting a service manager suitable for the target user according to the calculated similarity and recommending the service manager.
According to the personalized recommendation scheme of the service manager, which is provided by one or more embodiments of the specification, the service manager suitable for the client is obtained through similarity calculation by comprehensively considering the related information of the user, so that the service manager gives full play to own experience and serves the suitable client, effective guiding of the user is achieved, and the conversion rate of the service is improved.
The technical solutions of one or more embodiments of the present specification are described in detail below by means of specific embodiments.
Referring to fig. 1, a service manager personalized recommendation method according to one embodiment of the present specification includes the steps of:
step S101, the identification and the position information of the target user are acquired through the terminal equipment.
Step S102, the personal information and the service label of the target user are retrieved from the service database according to the identification.
The location information and personal information can be used to characterize a user by several elements. In particular, natural or social attributes of the user, such as gender, age, occupation, geographic location, etc., may be indicated; age groups can be divided according to actual business conditions; the actual business situation, such as 28 to 32 years old users, has a stronger possibility of purchasing a certain product, namely, 28 to 32 years old can be divided into an age group, and how to divide the business situation into the actual insurance company business situation needs to be combined.
The business labels can be user labels generated according to user browsing behaviors and purchasing behaviors; according to the actual sale condition of insurance products of insurance companies, the insurance products can be divided into various labels, such as aged people, accidental injuries, children, red, and the like, when no corresponding behavior of the user exists, the corresponding labels of the user are all set to be a default value of 0; for example, if a user browses insurance products of the child class and purchases excessive red insurance, his business label "child" and "red" are assigned a value of 1, and the other business labels are given a default value of 0.
Step S103, according to the position information, the personal information and the service label of the target user, the identification and the service label of the analog user similar to the target user are searched from the service database according to the preset screening condition.
In this embodiment, the analog users are screened from the database according to geographic location, gender, age group and business label. Specifically, for example, a total of A, B, C, D, E, F, G, H, I, J total 10 tags (for example, a replaces the red, E replaces the young), and the user has only A, C, H tag value of 1, so that when the analog user is screened in the database, all users with the same geographic position, sex and age range as the user are found, the searching range is narrowed, and then in the small range, all users meeting the judgment condition of "tag a value of 1 or tag C value of 1 or tag H value of 1" are screened, and the logical relationship needs to use "or" so as to find all users with similarity; the filtered analog user information needs to include the label case for each user.
Step S104, calculating the similarity between each analog user and the target user according to a preset similarity algorithm according to the searched service label of the target user and the service label of the analog user.
In this embodiment, specifically, the service of the analog user is selected from the database according to the service label of the target userThe label is used for constructing a service label matrix required by similarity calculation; each row of the matrix comprises a user name, a service tag 1, a service tag 2 to a service tag n. Each row in the matrix is recorded as the target user information and all analog user information screened out in the database respectively. Then, the service labels (label attribute 1, label attribute 2 to label attribute n) of the target users form a vector X, i.e. the first row data of the first column is removed by matrix to form a vector X, and the service labels (label attribute 1, label attribute 2 to label attribute n) of other analog users form a vector Y 1 、Y 2 To Y k I.e. the matrix forms a vector Y with the other row data of the first column and the first row removed 1 、Y 2 To Y k . Vector X and vector Y 1 、Y 2 To Y k And respectively calculating the respective similarity between the target user and all the analog users. The statistical formula used in this embodiment is exemplified by the pearson correlation coefficient formula:
wherein ρ is X，Y Representing the correlation coefficient of X and Y, i.e. the similarity; cov (X, Y) represents the covariance of X and Y; sigma (sigma) X Represents the standard deviation of X; sigma (sigma) Y Represents the standard deviation of Y.
As an alternative embodiment, other statistical formulas or other optimization methods during collaborative filtering may be used, so as to calculate reasonable user similarity in combination with the current service scenario and data situation.
Step S105, ranking the analog users in descending order of the similarity, and determining a preset number of analog users ranked at the top.
In this step, the predetermined number may be specifically adjusted according to the business situation of the company and the personnel configuration.
Step S106, according to the respective identifications of the predetermined number of analog users, the employee identifications and the service information of the service manager of the predetermined number of analog users are retrieved from the service database.
In this embodiment, the similarity ranking indicates that these service managers have experienced other users with very high similarity to the target user. The service information can be specifically data such as service manager business activity, activity number of the bound users of the service manager, and the like, and proper relevant judgment indexes can be selected according to actual business conditions and statistical data of the insurance company.
Step S107, pushing information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and service information of the service manager.
In this embodiment, the service information is not limited in number, and may be based on a single index, or may be weighted by two or more indexes (for example, the numerical indexes such as "liveness", "working year", "last quarter service condition" are weighted, and the weight may be automatically adjusted according to the actual situation), and then ranked and recommended preferentially. The ranking method may select any common ranking algorithm (e.g., using bubbling ranking, insertion ranking, etc.) to rank from high to low.
In this embodiment, the relevant information of the user is comprehensively considered, so that the service manager suitable for the client is obtained according to the similarity, the problem that the matching rule of the service manager is too simple and the service manager is unevenly distributed to the user is solved, so that the service manager gives full play to own experience, serves the appropriate client, realizes effective guiding of the user, improves the conversion rate of the service, and individually recommends the service manager for the user.
It should be noted that the methods of one or more embodiments of the present description may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of one or more embodiments of the present description, the devices interacting with each other to accomplish the methods.
It should be noted that the foregoing describes specific embodiments of the present invention. Other embodiments are within the scope of the following 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 desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, one or more embodiments of the present disclosure also provide a service manager personalized recommendation device corresponding to the method of any embodiment.
Referring to fig. 2, the service manager personalizes a recommendation device, including:
an obtaining module 201 configured to obtain, by a terminal device, an identification and location information of a target user;
a first retrieving module 202 configured to retrieve the personal information and the service tag of the target user from the service database according to the identification;
a second retrieving module 203 configured to retrieve, from the service database, identification and service tag of an analog user similar to the target user according to a predetermined filtering condition based on the location information, the personal information and the service tag of the target user;
a calculation module 204 configured to calculate a similarity between each of the analog users and the target user according to a predetermined similarity algorithm based on the retrieved service tag of the target user and the service tag of the analog user;
a ranking module 205 configured to rank each of the analog users in descending order of the similarity, determining a predetermined number of analog users that are top ranked;
a third retrieval module 206 configured to retrieve employee identification and service information of a respective service manager of the predetermined number of analog users from the business database based on the respective identifications of the predetermined number of analog users;
and the pushing module 207 is configured to push information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and service information of the service manager.
As an alternative embodiment, the identification and location information is specifically configured as a geographic location of the user, a gender of the user, an age of the user, and a business label of the user; wherein the user's business label is configured to be generated based on user browsing behavior and purchasing behavior.
As an optional embodiment, the similarity calculation module is specifically configured to construct a service tag matrix according to the service tag of the target user and the service tag of the analog user screened out from the database; and calculating the similarity according to the service tag matrix.
As an optional embodiment, the computing module is specifically configured to construct a service tag matrix according to the service tag of the target user and the service tag of the analog user screened out from the database; and calculating the similarity according to the service tag matrix.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in one or more pieces of software and/or hardware when implementing one or more embodiments of the present description.
The device of the foregoing embodiment is configured to implement the personalized recommendation method of the corresponding service manager in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, corresponding to the method of any embodiment, one or more embodiments of the present disclosure further provide an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor executes the program to implement the personalized recommendation method of the service manager according to any embodiment.
Fig. 3 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the personalized recommendation method of the corresponding service manager in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, one or more embodiments of the present specification also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the service manager personalized recommendation method as described in any of the embodiments above, corresponding to the method of any of the embodiments above.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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), 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 Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the foregoing embodiments stores computer instructions for causing the computer to execute the personalized recommendation method for a service manager according to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; combinations of features of the above embodiments or in different embodiments are also possible within the spirit of the present disclosure, steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments described above which are not provided in detail for the sake of brevity.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present disclosure is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the one or more embodiments of the disclosure, are therefore intended to be included within the scope of the disclosure.
Claims (8)
1. A service manager personalized recommendation method, comprising:
acquiring the identification and the position information of a target user through terminal equipment;
retrieving personal information and service labels of the target users from the service database according to the identification;
according to the position information, the personal information and the service label of the target user, searching the identification and the service label of the analog user similar to the target user from the service database according to a preset screening condition;
calculating the similarity between each analog user and the target user according to a preset similarity algorithm according to the service label of the target user and the service label of the analog user, and calculating the similarity between each analog user and the target user according to the preset similarity algorithm according to the service label of the target user and the service label of the analog user, wherein the method comprises the following steps:
constructing a service tag matrix according to the service tag of the target user and the service tag of the analog user;
calculating the similarity by using a preset statistical formula or a collaborative filtering method according to the service tag matrix, wherein the preset statistical formula is a pearson correlation coefficient formula;
ranking each of the analog users in descending order of the similarity, determining a predetermined number of analog users that are top ranked;
retrieving employee identification and service information of a service manager of each of the predetermined number of analog users from the service database according to the respective identification of the predetermined number of analog users;
and pushing information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and service information of the service manager.
2. The method of claim 1, wherein the service tags are generated based on service web browsing behavior and/or service purchasing behavior of the respective users.
3. The method of claim 1, wherein the predetermined screening conditions comprise: the analogous user is the same as the target user in terms of geographic location, gender, age and at least one business label.
4. A method according to any one of claims 1 to 3, wherein pushing information of a service manager recommended to a target user to the terminal device according to the retrieved employee identification of the service manager and the service information comprises:
ranking the service managers according to at least one service index in the service information;
and pushing information of a part of the service managers in the service manager to the terminal equipment according to the ranking result.
5. A service manager personalized recommendation device, comprising:
the acquisition module is configured to acquire the identification and the position information of the target user through the terminal equipment;
a first retrieval module configured to retrieve personal information and a service tag of the target user from the service database according to the identification;
a second search module configured to search out identification of an analog user and a service tag similar to the target user from the service database according to a predetermined screening condition based on the location information, the personal information and the service tag of the target user;
a calculation module configured to calculate a similarity between each analog user and the target user according to a predetermined similarity algorithm based on the retrieved service tag of the target user and the service tag of the analog user, wherein calculating the similarity between each analog user and the target user according to the retrieved service tag of the target user and the service tag of the analog user according to the predetermined similarity algorithm includes:
constructing a service tag matrix according to the service tag of the target user and the service tag of the analog user;
calculating the similarity by using a preset statistical formula or a collaborative filtering method according to the service tag matrix, wherein the preset statistical formula is a pearson correlation coefficient formula;
a ranking module configured to rank each of the analog users in descending order of the similarity, determining a predetermined number of analog users that are top ranked;
a third retrieval module configured to retrieve employee identification and service information of a respective service manager of the predetermined number of analog users from the business database based on the respective identifications of the predetermined number of analog users;
and the pushing module is configured to push the information of the service manager recommended to the target user to the terminal equipment according to the retrieved employee identification and the service information of the service manager.
6. The apparatus of claim 5, wherein the service tags are generated according to service web 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 on the memory and executable by the processor, characterized in that the processor implements the method according to any one of claims 1 to 4 when executing the computer program.
8. A non-transitory computer readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to implement the method of any of claims 1 to 4.
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| CN110148032A (en) * | 2019-04-15 | 2019-08-20 | 平安普惠企业管理有限公司 | Products Show method, apparatus, storage medium and server based on geographical location |
| CN111538910A (en) * | 2020-06-23 | 2020-08-14 | 上海摩莱信息科技有限公司 | Intelligent recommendation method and device and computer storage medium |
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| CN109242612A (en) * | 2018-08-22 | 2019-01-18 | 中国平安人寿保险股份有限公司 | A kind of method and apparatus of Products Show |
| CN109242514A (en) * | 2018-08-28 | 2019-01-18 | 腾讯科技（深圳）有限公司 | Client's label recommendation method, device and system |
| CN109410064A (en) * | 2018-09-04 | 2019-03-01 | 深圳壹账通智能科技有限公司 | Sales control method, apparatus, computer equipment and the storage medium of financial product |
| CN110148032A (en) * | 2019-04-15 | 2019-08-20 | 平安普惠企业管理有限公司 | Products Show method, apparatus, storage medium and server based on geographical location |
| CN111538910A (en) * | 2020-06-23 | 2020-08-14 | 上海摩莱信息科技有限公司 | Intelligent recommendation method and device and computer storage medium |
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