CN112561709B - Product information method, device, equipment and medium - Google Patents

Product information method, device, equipment and medium Download PDF

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CN112561709B
CN112561709B CN202011372435.0A CN202011372435A CN112561709B CN 112561709 B CN112561709 B CN 112561709B CN 202011372435 A CN202011372435 A CN 202011372435A CN 112561709 B CN112561709 B CN 112561709B
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product
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CN112561709A (en
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王超
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Taikang Health Industry Investment Holdings Co ltd
Taikang Insurance Group Co Ltd
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Taikang Health Industry Investment Holdings Co ltd
Taikang Insurance Group Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06F16/90—Details of database functions independent of the retrieved data types
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    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

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Abstract

The invention discloses a method, a device, equipment and a medium for product information, which are used for solving the problem that the existing products recommended to accounts cannot be conveniently and automatically determined. Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and rapidly.

Description

Product information method, device, equipment and medium
Technical Field
The present invention relates to the field of insurance recommendation technologies, and in particular, to a product information method, apparatus, device, and medium.
Background
Along with the development of society and the change of environment, the related policies and development directions of the large health industry are continuously changed. The "health chinese 2020" strategy and "health chinese 2030" plan specifically arrange for the future from different angles and directions. Enhancing operability and guidance of the national health plan, promoting biomedical and national health, national fitness and national health, kang Yangwen travel and national health deep fusion and the like are important subjects which are required to be continuously and deeply studied in the practical process of constructing 'healthy China'.
Kang Yangwen travel and the health of the whole people are deeply integrated, the enterprise depth participation of the pension industry, the medical industry and the insurance industry is needed, the enterprise is taken as an enterprise covering pension, medical and insurance business, on the basis of the national big health strategy, the product service which should be provided for the China people in the age of longevity is provided, and the service is provided for the account in an online mode. However, in the online system, the lack of introduction and guidance of the product by the salesman results in that the user to which the account belongs does not know what product is selected, which product is more matched with the user's own situation or the situation of the user's relatives. In such a case, it is difficult for the user to obtain a better use experience. Therefore, in order to always take humanized service as a starting point, how to conveniently and automatically determine the recommendation of products to accounts to ensure the user satisfaction and acceptance of the service quality is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a medium for product information, which are used for solving the problem that the existing products recommended to accounts cannot be conveniently and automatically determined.
The embodiment of the invention provides a product information method, which comprises the following steps:
acquiring first service experience information of a target account;
acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
determining product information of the first target product, and recommending the product information of the first target product to the target account;
the training of the original prediction model based on the second service experience information includes:
Acquiring second service experience information of any sample account and a corresponding label thereof, wherein the label is used for identifying products purchased by the sample account;
acquiring a second target product corresponding to the second service experience information through the original prediction model;
and training the original prediction model according to the label and the second target product.
Further, the preprocessing the sample service experience information in the sample set includes:
if the sample service experience information comprises age information of the sample account, determining a preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account for purchasing the product, and a mean square error of the age information of each account for purchasing the product;
if the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and correlation degrees of the preconfigured type of preference and the product.
Further, the preprocessing the sample service experience information in the sample set further includes:
If the sample service experience information comprises the experience time information of the sample account, determining the reservation times of each hour and the hour number of each reservation of the sample account in the service time period according to a preset service time period and each experience time information of the sample account; determining a pre-processed reservation time value of the sample account according to the reservation times and the hours;
if the sample service experience information comprises experience evaluation information of the sample account, determining a target experience evaluation information value corresponding to the experience evaluation information of the sample account according to a corresponding relation between the pre-configured experience evaluation information and the experience evaluation information value.
Further, the experience evaluation information of the target account further comprises: corresponding to behavior evaluation information of the client in the actual experience process, such as interest degree of the experience item, purchase intention and the like.
Further, if the sample service experience information includes age information of the sample account, determining the preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account where the product is purchased, and a mean square error of the age information of each account where the product is purchased includes:
Wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product;
if the sample service experience information includes preference information of the sample account, the determining a preference value of the sample account after preprocessing of the preference based on a survey score of the sample account on the preference and a correlation degree of a preconfigured preference with the product includes:
C 3j (A i )＝λ j G j ，
wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) The value is a taste value of the sample account after pretreatment of the jth class taste; lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product;
if the sample service experience information includes experience time information of the sample account, determining a pre-processed reserved time value of the sample account according to the reserved times and the hours includes:
Wherein A is i An ith product purchased for the sample account; c (C) 4 (A i ) Is a pre-processed reservation time value for the sample account; m is the reservation times; h is the number of hours.
Further, before the obtaining the first target product corresponding to the first service experience information through the pre-trained prediction model, the method further includes:
determining a target type of each piece of information in the first service experience information;
if it is determined that the first service experience information lacks information of the necessary information type according to the pre-configured necessary information type and the target type, determining the history information of the lacking necessary information type from the pre-stored history information of each necessary information type of the target account, and adding the history information to the first service experience information.
Further, before the obtaining the first target product corresponding to the first service experience information through the pre-trained prediction model, the method further includes:
determining a target type of each piece of information in the first service experience information;
and filtering the information of which the target type is not the pre-configured necessary information type in the first service experience information.
Further, the recommending the product information of the first target product to the target account includes:
and recommending the product information of the first target product to the target account if the confirmation information of the first target product is received.
The embodiment of the invention provides a product information device, which comprises:
the acquisition unit is used for acquiring first service experience information of the target account;
the processing unit is used for acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
the recommending unit is used for determining the product information of the first target product and recommending the product information of the first target product to the target account;
Wherein the apparatus further comprises: a training unit;
the training unit is used for acquiring second service experience information of any sample account and a corresponding label thereof, and the label is used for identifying products purchased by the sample account; acquiring a second target product corresponding to the second service experience information through the original prediction model; and training the original prediction model according to the label and the second target product.
Further, the apparatus further comprises: a preprocessing unit;
the preprocessing unit is configured to determine, if the sample service experience information includes age information of the sample account, a preprocessed age value of the sample account based on the age information of the sample account, an average value of age information of each account where the product is purchased, and a mean square error of the age information of each account where the product is purchased; if the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and correlation degrees of the preconfigured type of preference and the product.
Further, the preprocessing unit is further configured to determine, if the sample service experience information includes experience time information of the sample account, a number of reservations of the sample account in each hour in a service period and a number of hours of each reservation according to a pre-configured service period and each experience time information of the sample account; determining a pre-processed reservation time value of the sample account according to the reservation times and the hours; if the sample service experience information comprises experience evaluation information of the sample account, determining a target experience evaluation information value corresponding to the experience evaluation information of the sample account according to a corresponding relation between the pre-configured experience evaluation information and the experience evaluation information value.
Further, the preprocessing unit is specifically configured to, if the sample service experience information includes age information of the sample account, base onDetermining a preprocessed age value of the sample account; wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product;
if the sample service experience information comprises preference information of the sample account, C is based on 3j (A i )＝λ j G j Determining a preference value of the sample account after preprocessing the preference; wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) The value is a taste value of the sample account after pretreatment of the jth class taste; lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product;
if the sample service experience information includes the experience time of the sample accountInformation based onWherein A is i An ith product purchased for the sample account; c (C) 4 (A i ) Is a pre-processed reservation time value for the sample account; m is the reservation times; h is the number of hours.
Further, the apparatus further comprises:
the determining unit is used for determining the target type of each piece of information in the first service experience information before acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model;
The processing unit is further configured to determine, if the information of the necessary information type is absent from the first service experience information according to the pre-configured necessary information type and the target type, from the pre-stored history information of each necessary information type of the target account, determine and add the history information of the absent necessary information type to the first service experience information.
Further, the apparatus further comprises:
the determining unit is used for determining the target type of each piece of information in the first service experience information before acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model;
the processing unit is further configured to filter information in the first service experience information, where the target type is not a pre-configured necessary information type.
Further, the recommending unit is specifically configured to recommend the product information of the first target product to the target account if the confirmation information of the first target product is received.
An embodiment of the present invention provides an electronic device, where the electronic device includes at least a processor and a memory, where the processor is configured to implement the steps of any of the product information methods described above when executing a computer program stored in the memory.
An embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of a product information method as described in any one of the above.
Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and quickly, the recommended product is determined based on the service experience information of the target account, the method is more suitable for users to a certain extent, and the matching efficiency and user experience of the product and user requirements are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a process for determining product information according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a training process of a specific prediction model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a specific process for determining product information according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for determining product information according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the attached drawings, wherein it is apparent that the embodiments described are only some, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to conveniently and automatically determine a product recommended to a target account, the embodiment of the invention provides a product information method, device, equipment and medium.
Aiming at the vigorous development of the current medical, pension and insurance industries, products are recommended to the account through a plurality of blind and improper operation means, so that the trust degree and user experience of the user to which the account belongs to an insurance service mechanism can be reduced. Therefore, in order to support the comprehensive reservation service of the super-experience service product, the super-experience product service is enriched, the flexible provision of the service is convenient, the user can reserve in time, the management capability and the user experience of the super-experience service are comprehensively improved, and a product recommendation platform of the super-experience reservation is built, so that the user can reserve various services in the product recommendation system, for example, at least one service of visiting a scenic area, visiting a nursing home, visiting an oral hospital, reserving ophthalmology, reserving physical examination, serious disease registration, community visiting, souvenir experience and the like, thereby providing the user with omnibearing and multi-level reservation experience, and the user can identify enterprise products really intended for the user in the reservation experience.
In the specific application process, the product recommendation system is divided into an A end, a B end and a C end, wherein the A end is mainly used by an enterprise operation management department and is responsible for the services of product management, channel management, reservation rule setting, reservation auditing, report management and the like, the B end is mainly used by first-line staff providing service and is responsible for the services of site resource maintenance, resource scheduling management, reservation order confirmation, reservation visit reception and the like, the C end is mainly used by a user, and the user can subscribe and place the order, enter the garden for verification, experience evaluation information, product purchase and the like. In the product recommendation system, the order of the reservation service is taken as a core, super experience type service productization is realized, for example, a user can reserve the service in a product recommendation platform like purchasing the product, the order is formed, for example, when the user reserves the service, the order is generated and visualized according to reservation information filled by the user, for example, the user and related staff can see the order generated when each user reserves the service, and the related staff can realize the management of the super experience type service of multiple inlets in the modes of resource management, rule management, reservation management, payment management, information management and the like of the order.
Example 1:
fig. 1 is a schematic diagram of a process for determining product information according to an embodiment of the present invention, where the process includes:
s101: and acquiring first service experience information of the target account.
S102: acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training the original prediction model based on second service experience information, and the second service experience information is obtained after preprocessing sample service experience information in a sample set.
The product information method provided by the embodiment of the invention is applied to electronic equipment, and the electronic equipment can be intelligent equipment such as intelligent terminals, intelligent computers, tablets and the like, and can also be a server.
In a practical application scenario, when a user wishes to purchase a certain product, but is hesitant to a specific product to be purchased, a reservation service function in the product recommendation system may be used through an account registered in the product recommendation system, i.e., a target account, thereby selecting a service that is currently desired to be reserved and inputting reservation information, such as a type of service reserved, gender information, age information, preference information, and the like. And determining first service experience information of the target account based on the acquired reservation information of the target account and experience information generated after the target account experiences reserved services, such as experience evaluation of the target account on reserved services, and the like. After the electronic equipment for determining the product information acquires the first service experience information of the target account, corresponding processing is carried out based on the reservation request, so that the product information of the first target product recommended to the target account is determined.
Among them, there are many modes of inputting reservation information by a user, and the reservation information may be input by inputting the reservation information in a corresponding text input box displayed on a display interface, or by sliding a sliding window displayed on the display interface to select the corresponding information as the input reservation information, or by inputting voice information. There are many specific ways of inputting the reservation information, and the specific implementation process can be flexibly set according to the needs, which is not limited herein.
In a specific implementation process, in order to conveniently determine the recommended products to the target account, a prediction model for predicting the recommended products is trained in advance. After the electronic equipment for product recommendation obtains the first service experience information of the target account, a first target product corresponding to the first service experience information is obtained based on the first service experience information through a pre-trained prediction model.
In order to acquire a trained predictive model, a sample set for training the predictive model is collected in advance, the sample set contains sample service experience information of an account of purchased products, and the account of purchased products is determined as a sample account. The sample service information of any sample account is determined according to reservation information input when the sample account reserves service on the product recommendation system and information generated when the reserved service is experienced. And preprocessing the sample service experience information aiming at each sample service experience information in the sample set, and determining the preprocessed sample service experience information as second service experience information. And training the original prediction model based on each piece of second service experience information, namely adjusting the parameter value of each parameter in the original prediction model to obtain a trained prediction model. And determining the recommended products to any target account based on the trained prediction model.
Wherein, since the products tend to be purchased by the target accounts of different ages are different, for example, the target accounts are older and may tend to purchase the pension type products, the target accounts are children and may tend to purchase the educational-gold type products, etc., the products tend to be purchased by the target accounts of different sexes are also different, for example, the male tends to purchase the traffic risk type products, the female tends to purchase the family financial class products, and the hobbies of the target accounts have an effect on the products tend to be purchased by the target accounts, for example, the target accounts which hobbies travel tend to purchase the travel risk type products, and the target accounts which hobbies financial tend to purchase the annual gold risk type products. Therefore, in the embodiment of the present invention, the first service experience information carries at least one of age information of the target account, gender information of the target account, and preference information of the target account.
The experience evaluation information of the target account further comprises: corresponding to behavior evaluation information of the client in the actual experience process, such as interest degree of the experience item, purchase intention and the like.
Further, the reserved service may also have an effect on the product that the target account tends to purchase, e.g., the target account experiences a certain type of service multiple times, or the target account evaluates to be very satisfied with the service experience of a certain reservation, etc. Therefore, on the basis of the above embodiment, the first service experience information may also be at least one of experience time information of the target account and experience evaluation information of the target account, that is, the first service experience information includes at least one of age information of the target account, gender information of the target account, taste information of the target account, experience time information of the target account and experience evaluation information of the target account.
S102: and determining the product information of the first target product, and recommending the product information of the first target product to the target account.
Based on the method in the above embodiment, the obtained first service experience information is processed through the pre-trained prediction model, so that a probability value of recommending each product to the target account can be obtained, and the first target product corresponding to the service experience information is determined based on each probability value.
In one possible implementation, for each product, it may be determined whether the probability value corresponding to the product is greater than a set probability threshold, and if so, the product is determined to be a candidate product. And randomly selecting a set number of candidate products from each candidate product as a first target product, or selecting a set number of candidate products with larger probability values from each candidate product as the first target product. Of course, a product with a larger probability value of a set number may be directly used as the first target product, for example, when a product has a corresponding probability valueIndicating that the product can be recommended to the target account, determining the product as a first target product; otherwise, it indicates that the product cannot be recommended. Specifically, the manner of determining the first target product may be flexibly set according to actual requirements, which is not specifically limited herein.
In the actual application scene, the general enterprise operation business covers the aged, medical and insurance industries, and the present enterprise provides a great health strategy in the age of longevity, and provides the service of reserved visit physical examination for the aged community, the memorial park, the oral cavity mechanism, the physical examination mechanism, psychological consultation and green general medical treatment for users. Therefore, aiming at the reserved visit of the pension communities, the enterprise provides the pension communities of the entity for the user, the user can reserve the pension admission service, so that the real admission experience is obtained, the personal care in the pension communities is felt, the products corresponding to the reserved service are selected according to the admission experience, and the products corresponding to the reserved service can have the pension community admission right and pension products; aiming at the reserved visit of the memorial park, the user can reserve the travel service of the memorial park, so that the user can feel the personal care provided by the memorial park in the form of travel, enjoy the service mode provided by the memorial park and pick up the scenery in the memorial park; aiming at the reserved visit of the oral mechanism, a user can visit the oral service, so that whether to purchase products corresponding to the reserved service, such as oral packages and oral products, is determined according to the visit experience; aiming at the appointment visit of a physical examination mechanism, a user can experience the actual physical examination process in a self-cutting manner, and according to the experience of comparing the appointment with the conventional physical examination, whether to purchase products corresponding to the appointment service, such as physical examination packages of different age groups and examination types and life insurance products for health prevention in advance, is determined; aiming at the reserved visit of psychological consultation, a user can improve the degree of importance of the user on psychological health and prevent psychological diseases through visiting a psychological consultation mechanism; aiming at the appointment visit of the green through medical treatment, a user can have customized services such as accompanying of a special person through the green through medical treatment, reserve convenient services of expert resources in advance, and then compare the medical treatment experience of the green through medical treatment with the ordinary medical treatment experience, so that whether to purchase products corresponding to the appointment services is determined according to comparison results. Therefore, in the embodiment of the present invention, the first target product may be an insurance type, or may be a specific insurance product.
If the first target product is an insurance type, the first target product includes at least one of accident insurance, life insurance, annuity insurance, cancer prevention insurance, commercial medical insurance, travel insurance, property insurance, health insurance, vehicle insurance, traffic insurance, and serious disease insurance. In the implementation process, after the first target product is determined, product information of each specific product of the first target product, which is configured in advance, is recommended to the target account.
If the first target product is a specific insurance product, the first target product comprises at least one of an accident insurance type product, a life insurance type product, an annual insurance type product, an cancer insurance type product, a commercial medical insurance type product, a travel insurance type product, a property insurance type product, a health insurance type product, a vehicle insurance type product, a traffic insurance type product and a heavy insurance type product. In the implementation process, after the first target product is determined, the product information of the first target product which is configured in advance is directly recommended to the target account.
The product information of the first target product can be recommended in a popup window mode in the product recommendation system, the product information of the first target product can be pushed in a display interface of the product recommendation system, and the product information of the first target product can be recommended in a short message notification mode. The specific recommendation method is not limited to the above-described recommendation method, and is not particularly limited herein.
S104: the training of the original prediction model based on the second service experience information includes:
acquiring second service experience information of any sample account and a corresponding label thereof, wherein the label is used for identifying products purchased by the sample account;
acquiring a second target product corresponding to the second service experience information through the original prediction model;
and training the original prediction model according to the label and the second target product.
In order to conveniently and automatically determine the recommended products to the target account, the original predictive model needs to be trained according to the second service experience information of each sample account. The second service experience information of any sample account is correspondingly provided with a label, and the label is used for identifying products purchased by the sample account.
The electronic device for model training may be the same as or different from the electronic device for determining product information. If the electronic equipment for model training is different from the electronic equipment for determining the product information, the electronic equipment for model training trains the original prediction model in advance according to the second service experience information and the corresponding label thereof, after acquiring the trained prediction model, the trained prediction model is stored in the electronic equipment for determining the product information, and the electronic equipment for subsequently determining the product information can determine a first target product recommended to a target account through the trained prediction model, so that the product information of the first target product is recommended to the target account.
In the embodiment of the invention, the second service experience information of any sample account is input into the original prediction model, a second target product corresponding to the input second service experience information can be obtained, and the original prediction model is trained according to the label corresponding to the second service experience information and the predicted second target product so as to adjust the parameter values of all the parameters in the original prediction model.
Wherein the predictive model may be a Radial Basis Function (RBF) neural network.
The second service experience information for the prediction model training is very much, and the above operation is performed on each second service experience information, and when the preset convergence condition is satisfied, the prediction model training is completed.
The meeting of the preset convergence condition may be that the loss value determined based on the second target product predicted by the trained prediction model to obtain each piece of second service experience information, the label corresponding to each piece of second service experience information is smaller than a preset loss threshold, or the iteration number of training the original prediction model reaches a set maximum iteration number, etc. The implementation may be flexibly set, and is not particularly limited herein.
As a possible implementation manner, when the original prediction model is trained, the second service experience information may be divided into a training sample and a test sample, the original prediction model is trained based on the training sample, and then the reliability degree of the trained prediction model is verified based on the test sample.
Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and quickly, the recommended product is determined based on the service experience information of the target account, the method is more suitable for users to a certain extent, and the matching efficiency and user experience of the product and user requirements are improved.
Example 2:
fig. 2 is a schematic diagram of a training flow of a specific prediction model provided by an embodiment of the present invention, and the following describes a method for training the prediction model provided by the embodiment of the present invention with reference to fig. 2 by using the prediction model as an RBF model:
Firstly, sample service experience information for prediction model training is collected in advance, labels corresponding to the sample service experience information are determined, and any label corresponding to the sample service experience information is used for identifying products purchased by a sample account corresponding to the sample service experience information. And determining a probability value, such as 1, corresponding to the label corresponding to each sample service experience information according to the label corresponding to each sample service experience information.
In order to facilitate the original prediction model to process sample service experience information, each sample service experience information can be cleaned, some useless or invalid information is filtered out, then the original prediction model is trained for convenience, a trained prediction model is obtained, the sample service experience information is preprocessed, the preprocessed sample service experience information is determined to be second service experience information, and then the original prediction model is trained based on the second service experience information, so that the trained prediction model is obtained.
And designing a network structure of an original prediction model, for example, determining a connection mode of the RBF model as p-K-1, namely, p input layer neurons, K hidden layer neurons and 1 output layer neurons of the RBF model, and carrying out random assignment on each parameter value in the original prediction model. Wherein neurons in the hidden layer are smaller natural numbers.
The current iteration number, i.e., the currently saved iteration number +1, is updated before the following steps are performed on the second service experience information for each sample account.
The original predictive model is trained based on the second service experience information for each sample account. Specifically, for the second service experience information of each sample account, the following steps are executed:
inputting the second service experience information into the original prediction model, for example, the second service experience information of a certain sample account input at the t moment is x 1 (t)，x 2 (t)，…，x p (t) the label corresponding to the second service experience information is y (t) =a i ，A i The product purchased by the sample account can obtain a second target product corresponding to the second service experience information input currently and a probability value recommended to the target account corresponding to the second target product through the original prediction model, wherein the second target product can be expressed as
Specifically, the second target product corresponding to the second service experience information input currently is obtained through the original prediction model, and the second target product can be obtained through the following modes:
for the probability value omega corresponding to the second target product corresponding to the second service experience information x (t) input at the t-th moment k (t) represents the connection weight of the kth neuron in the kth hidden layer and the output layer, k=1, 2, …，K；θ k (x (t)) is the output of the hidden layer kth neuron after the second service experience information x (t) is input to the original prediction model.
Wherein, after the second service experience information x (t) is input into the original prediction model, the output theta of the kth neuron of the hidden layer k The calculation formula of (x (t)) is:
μ k (t) represents the central value, σ, of the kth neuron of the hidden layer at the time t k The central width of the kth neuron of the hidden layer at the t-th moment is represented.
After the second target product is obtained, determining a loss value according to the probability value corresponding to the second target product and the second service experience information x (t) input at the t moment, wherein the loss value is determined specifically by the following method:
wherein E (t) represents a loss value corresponding to the second service experience information x (t) input at the t-th moment, y (t) represents a probability value corresponding to the label corresponding to the second service experience information x (t) input at the t-th moment, the probability value is 1,and a probability value corresponding to a second target product corresponding to the second service experience information x (t) input at the t-th moment.
Based on the determined loss values, the values of the parameters in the original prediction model are adjusted, specifically by the following formula:
Wherein W is T (t)＝[w 1 (t)，w 2 (t)，…，w K″ (t)] T The connection weights of K neurons in the hidden layer at the t time and the output layer respectively are represented, and the t time is represented; eta epsilon (0,0.1)]Representing the neural network learning rate; θ k (x (t)) is the output of the kth neuron of the hidden layer after the second service experience information x (t) is input into the original prediction model;and y (t-1) represents a probability value corresponding to a label corresponding to the second service experience information x (t) input at the t-1 time for the probability value corresponding to the second target product corresponding to the second service experience information x (t) input at the t-1 time.
Through the steps, the original prediction model is trained, whether the current trained prediction model meets a preset convergence condition is judged, and whether the trained prediction model is obtained is determined. Specifically, if the sum of the loss values corresponding to each piece of second service experience information acquired in the current iteration is smaller than a set loss value threshold, determining that a prediction model with completed training is acquired; otherwise, judging whether the current iteration number 1 reaches the maximum iteration number L, if so, stopping continuous training, and if not, continuing training the prediction model.
When the maximum iteration number is set, the maximum iteration number is generally set to be larger, for example, L e (100, 300), so that the sum of the loss values corresponding to each piece of second service experience information acquired by the current iteration is smaller than a set loss value threshold before the current iteration number reaches the maximum iteration number.
By the method, after the trained prediction model is obtained, the accuracy of the trained prediction model can be tested, and after the prediction model is determined to pass the test, the probability value of each product recommended to the target account can be determined based on the obtained first service experience information through the trained prediction model. For each product, if the probability value of the product meets the preset recommendation requirement, determining the product as a first target product and recommending the first target product to a target account; otherwise, the product is not recommended to the target account.
Example 3:
in order to facilitate the model to process the second service experience information, in the embodiment of the present invention, the preprocessing the sample service experience information in the sample set includes:
If the sample service experience information comprises age information of the sample account, determining a preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account for purchasing the product, and a mean square error of the age information of each account for purchasing the product;
if the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and correlation degrees of the preconfigured type of preference and the product.
In the embodiment of the invention, because some information inconvenient for the model to process may exist in the sample service experience information, such as non-numerical experience evaluation information, gender, hobbies and other information of an account, the sample service experience information needs to be preprocessed before being input into the original prediction model, for example, regularization processing is performed on the sample service experience information, punctuation marks in the sample service experience information and cases in unified sample service experience information are deleted, the non-numerical information in the sample service experience information is converted into corresponding numerical values and the like, the preprocessed sample service experience information updates the original sample service experience information, and training is performed on the original prediction model based on the updated sample service experience information, so that the original prediction model is convenient to further process the sample service experience information.
Specifically, for each type of service experience information that may exist in the sample service experience information, the following manner may be used for preprocessing:
in the first aspect, in an actual application scenario, the sample service experience information may include age information of a sample account, so that, in order to enable the prediction model to learn a product and a potential relationship existing between the sample service experience information and the age information of the sample account, in the embodiment of the present invention, if the sample service experience information includes the age information of the sample account, an average value and a mean square error of ages of each account where the sample is purchased in a sample set may be determined for each product. And then, aiming at each sample service experience information in the sample set, carrying out corresponding processing according to the age information of the sample account, the average value and the mean square error of the age information of each account of the product in the label corresponding to the purchase sample service experience information, determining the preprocessed age value of the sample account, and determining second service experience information based on the preprocessed age value of the sample account.
In one possible embodiment, if the sample service experience information includes age information of the sample account, the determining the preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account where the product is purchased, and a mean square error of the age information of each account where the product is purchased includes:
Wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product.
In the second mode, in an actual application scene, the sample service experience information may include gender information of the sample account, where the gender is non-numerical information, which is not beneficial to processing of the original prediction model. In order to enable the prediction model to learn the potential relationship between the product and the gender information of the account, in the embodiment of the present invention, if the sample service experience information includes the gender information of the sample account, a first value corresponding to the gender information for a male and a second value corresponding to the gender information for a female may be preset. For each sample service experience information in the sample set, if the sex information of the target account included in the sample service experience information is male, determining a preset first numerical value as a preprocessed sex value of the sample account; and if the gender information of the sample account included in the sample service experience information is female, determining a preset second value as the preprocessed gender value of the sample account.
For example,
wherein A is i Ith product, C, representing sample account purchase 2 (A i ) The method comprises the steps of purchasing a pre-processed sex value corresponding to a sample account of an ith product, determining that the pre-processed sex value of the sample account is-1 if the sex information of the sample account is male, and determining that the pre-processed sex value of the sample account is 1 if the sex information of the sample account is female.
In the third mode, in the actual application scene, the sample service experience information may include the preference information of the sample account, and the preference information is also non-numerical information, which is unfavorable for the processing of the original prediction model. In order to enable the prediction model to learn the potential relation between the product and the preference information of the account, in the embodiment of the invention, the investigation score of each sample account for each type of preference is counted in advance so as to determine the preference degree of the sample account for the type of preference through the investigation score, for example, the investigation score is lambda j =0, indicating that the sample account has no preference for the j-class hobbies; lambda (lambda) j =0.5, indicating that the sample account has a certain preference for the j-class hobbies; lambda of j =1, indicating that the sample account favorites the j types of hobbies, and preconfigured each type of hobbies and each Correlation of products to determine influence of such hobbies on products purchased in sample accounts, such as correlation G of a hobbies with a certain product j =1, which indicates that the association between the preference and the product is strong, and that the preference has a great influence on the purchase of the product in the sample account; if the correlation degree G of the preference and the product j =0.75, which means that the association between the preference and the product is strong, and that the preference has a great influence on purchasing the product in the sample account; if the correlation degree G of the preference and the product j =0.5, indicating that the preference has a certain association with the product, and that like the preference has a certain effect on the purchase of the product by the account; if the correlation degree G of the preference and the product j =0.25, which means that the association between the preference and the product is weak, and that the preference has less influence on purchasing the product in the sample account; if the correlation degree G of the preference and the product j =0, indicating that the preference has no association with the product, and that the preference has no effect on purchasing the product from the sample account.
Specifically, sample service experience information of any sample account in a sample set is obtained, and for each type of preference, a preference value of the sample account after preprocessing of the preference is determined based on investigation scores of the sample account on the preference and correlation degrees of the preconfigured preference and products purchased by the sample account.
Wherein the association degree of each type of hobbies and each product is set according to human experience.
In a possible manner, if the sample service experience information includes preference information of the sample account, the determining, based on the investigation score of the sample account on the preference and the association degree of the pre-configured preference with the product, a pre-processed preference value of the sample account on the preference includes:
C 3j (A i )＝λ j G j ，
wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) Is the sample account to the j-th hobbiesIs a pre-processed preference value of (a); lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product.
In the fourth aspect, in order to enable the prediction model to learn the potential relationship between the product and the experience time information of the account, in the embodiment of the present invention, if the experience time information of the sample account is included in the sample service experience information, the sample service experience information of each sample account may be determined according to a preset service time period, for example, 8:00-17:00 a day, and the reservation time of each reservation of the sample account, for example, the reservation times of each hour of the sample account in the service time period, for example, 14:30 a sample account has been reserved twice and 14:40 a time, the sample account is reserved for 3 times in the service time period, and the number of hours of each reservation of the sample account is determined, and the reservation time value of the sample account after the pretreatment is determined according to the reservation times of the sample account and the number of hours.
In one possible implementation manner, if the sample service experience information includes experience time information of the sample account, determining the pre-processed reserved time value of the sample account according to the reserved times and the hours includes:
wherein A is i An ith product purchased for the sample account; c (C) 4 (A i ) Is a pre-processed reservation time value for the sample account; m is the reservation times; h is the number of hours.
In the fifth mode, in the actual application scenario, the sample service experience information may include experience evaluation information of the sample account, where the experience evaluation information is also non-numeric information, which is unfavorable for the original experienceThe prediction model is processed. In order to enable the prediction model to learn the potential relationship between the product and the experience evaluation information of the account, in the embodiment of the invention, if the sample service experience information includes the experience evaluation information of the sample account, the corresponding relationship between the experience evaluation information and the experience evaluation information value may be preset. In the implementation process, for each sample service experience information, determining a target experience evaluation information value corresponding to the experience evaluation information of the sample account according to a preset corresponding relation between the experience evaluation information and the experience evaluation information value. For example, C 5 (A i ) =0, 0.5 and 1, c 5 (A i ) And for the target experience evaluation information value corresponding to the experience evaluation information of the sample account, the target experience evaluation information value is 0, which indicates that the sample account experiences dissatisfaction with the secondary service, the target experience evaluation information value is 0.5, which indicates that the sample account experiences satisfaction with the secondary service, and the target experience evaluation information value is 1, which indicates that the sample account experiences very satisfactory with the secondary service.
According to the trained prediction model, because the information of the age, sex, hobbies and the like of the users are different, each service which can be reserved has different attraction to the target accounts of different age groups, different sexes and different hobbies, and further the correlation degree between the finally purchased product of the target account and the times of experiencing the service by the target account is also different, for example, some target accounts experience a certain service for multiple times, the purchase of the product corresponding to the service is determined, some target accounts experience a certain service once, the product corresponding to the service is purchased, even some target accounts reserve a certain service, but the product corresponding to another service is purchased finally. Therefore, based on a large amount of second service experience information, the nonlinear input-output relationship can be learned to a certain extent through the prediction model, and influences on products purchased by accounts among different services, such as a user visiting a memorial park in advance, 30% of users purchase physical examination packages with different dimensions.
Example 4:
in order to accurately determine the first target product, based on the foregoing embodiments, in the embodiment of the present invention, before the obtaining, by the pre-trained prediction model, the first target product corresponding to the first service experience information, the method further includes:
determining a target type of each piece of information in the first service experience information;
if it is determined that the first service experience information lacks information of the necessary information type according to the pre-configured necessary information type and the target type, determining the history information of the lacking necessary information type from the pre-stored history information of each necessary information type of the target account, and adding the history information to the first service experience information.
In an actual application scenario, it may happen that the user does not want to fill in some information that has been filled in when the user reserved for some previous service, for example, information such as name, gender, hobbies, etc., so that there may be a situation that the acquired reserved information is missing, and further, the first service experience information determined based on the reserved information and the experience information of the target account is missing. When the condition that the first service experience information is missing is determined, the fact that the content in the first service experience information is incomplete is indicated, and the first target product cannot be accurately recommended to the target user according to the information contained in the first service experience information. Therefore, in order to ensure that the first target product recommended to the target account is accurately determined, the necessary information types are preconfigured, and for each necessary information type, the historical information of the necessary information type recorded by the target account in the historical experience service process is saved. In the implementation process, before a first target product corresponding to the first service experience information is obtained through a pre-trained prediction model, determining a target type of each piece of information in the obtained first service experience information, then matching the target type with a pre-configured necessary information type, and determining whether the first service experience information has the condition of lacking any necessary information type. If it is determined that the first service experience information lacks information of a necessary information type, the history information of the necessary information type lacking currently can be determined from the pre-stored history information of each necessary information type of the target account and added to the first service experience information, so that subsequent steps can be performed according to the added first service experience information.
If it is determined that the information of the necessary information type is not missing in the first service experience information, a subsequent step may be directly performed according to the currently acquired first service experience information.
It should be noted that, if it is determined that the history information of a certain necessary information type of the target account is not stored currently, in order to ensure that the first target product recommended to the target account is accurately determined later, in the embodiment of the present invention, a prompt message of a necessary information type that supplements the lack of history information, for example, "please input age", may be output, so that the target account further perfects the input first service experience information.
The output of the prompt message of the necessary information type for supplementing the lacking history information may be a voice broadcast of the prompt message of the audio format, for example, voice broadcast of the prompt message of the necessary information type for supplementing the lacking history information, "please input age", or display of the prompt message corresponding to the text form on the display interface, for example, display of the prompt message of the necessary information type for supplementing the lacking history information "please supplement gender". The two modes of outputting the prompt information can be combined at the same time, namely, the prompt information in the audio format is broadcast at the same time and the prompt information in the text format is displayed on the display interface.
The specific selection of which mode to output the prompt message can be preset according to the preference of the user, or can be selected according to the capabilities of the electronic devices, for example, some electronic devices do not have a display interface capable of displaying the prompt message, and when the prompt message is output, the prompt message in an audio format can be broadcasted for the electronic devices.
After the prompt information of the necessary information type for supplementing the lacking historical information is output, the information supplemented by the user can be received, the supplemented information is correspondingly stored with the information type corresponding to the information, and according to the supplemented information and the first service experience information acquired before, a first target product recommended to the target account is determined through a pre-trained prediction model.
Further, when the user reserves the service, the user may input some private information, such as an identification card number, a mobile phone number, etc., and if the private information is revealed, the user's property security may be possibly caused. Therefore, after the first service experience information is acquired, the privacy information in the first service experience information needs to be extracted, and the extracted privacy information is subjected to desensitization. Specifically, the method for desensitizing the private information belongs to the prior art, and is not specifically limited herein.
In order to further facilitate determining a product that can be recommended to the target account, in the embodiment of the present invention, before the obtaining, by the pre-trained prediction model, the first target product corresponding to the first service experience information, the method further includes:
determining a target type of each piece of information in the first service experience information;
and filtering the information of which the target type is not the pre-configured necessary information type in the first service experience information.
In the practical application process, the first service experience information input by the user may contain many other useless contents besides the information of the necessary information type, and the useless contents are generally large and redundant, so that not only are storage resources consumed, but also a large amount of data processing resources are wasted. Therefore, in the embodiment of the invention, in order to further facilitate determining the first target product that can be recommended to the target account, before the first target product corresponding to the first service experience information is obtained through the pre-trained prediction model, filtering the content included in the obtained first service experience information.
In a specific implementation process, a target type of each piece of information in the first service experience information is determined. And then respectively matching each target type with a preconfigured necessary information type, and filtering information corresponding to a target type which is not matched with any preconfigured necessary information type, namely filtering information of which the target type is not the preconfigured necessary information type in the first service experience information, so as to filter useless information in the first service experience information.
Example 5:
fig. 3 is a schematic diagram of a specific product information determining process provided by an embodiment of the present invention, and a detailed description will be given of a product information method provided by an embodiment of the present invention with reference to fig. 3:
the user makes service reservations on the product recommendation system.
Specifically, the user can carry out reservation services for visiting an old community, visiting a memorial park, visiting an oral cavity mechanism, visiting a physical examination mechanism, carrying out psychological consultation experience and carrying out green medical experience on the C-end APP of the product recommendation system. According to the service, the user can enjoy the experience in advance, so that the user experience is improved, and different products are provided for the user in a targeted manner according to the actual experience and evaluation of the user.
And acquiring sample service experience information.
In the specific implementation process, the product recommendation system mainly comprises a product center, a resource center and 3 business center stations of an order center, wherein the product center is responsible for managing products and service items sold on line and standardizing the products; the resource center is used for scheduling personnel, equipment, places and the like for providing services, and carrying out on-line management on the resources, so that on-line reservation of users is facilitated, and the utilization rate of the resources is improved; the order center stores reservation information input based on the account and service experience information determined by the experience information.
The persistent layer in the order center synchronizes the service experience information stored in the Mysql database and the mongoDB database to a distributed Search (es) data center in real time, and finally stores the service experience information in the es data center. The scheme for synchronizing the service experience information stored in the Mysql database to the es data center is as follows:
the canaal real-time synchronizer is deployed as a slave to the Mysql database, reads the log data binlog, and sends updates to the table data in the Mysql database to the kafka message center in the format of a structured json.
Because the service experience information stored in the Mysql database is stored in the es data center in real time, the data standardization requirement is high, the kafka information is directly stored in the es, the data processing difficulty is high, and the maintenance is difficult. Therefore, in the embodiment of the invention, the message data in the kafka message center is not directly stored in the es data center, but the message of the kafka message center is subscribed through a micro service for consuming the kafka message, so that the real-time performance of the message is ensured, and the service experience information can be flexibly processed. When the kafka message is consumed, the kafka message can be personalized according to different service types.
In the embodiment of the invention, a main and standby cluster mode is adopted for cluster construction of the es data center, for example, the main cluster is 3 es data center main nodes and 5 es data center data nodes. The standby cluster is also 3 es data center main nodes and 5 es data center data nodes, and when the main cluster provides different services, the standby cluster is automatically started.
After the service experience information stored in the Mysql database is stored in the es data center, a rest api interface enabling product is provided based on the capability of fast indexing of the es full text and is used as an input parameter of predictive model training.
The scheme for synchronizing the service experience information stored in the mongoDB database to the es data center is as follows:
at present, there are various ways to synchronize data stored in the mongoDB database to the es data center, and the service can be synchronized through the web server, and the service is written to the es data center and the mongoDB database, but this way has a great expenditure on the server, and once the data is stored, the data consistency is affected. Buffer inputs in logstack can also be utilized by adding mongo input and ES output plug-ins, but this approach is very dependent on Java database connectivity (JDBC). Therefore, in the embodiment of the invention, a mongo-connector mode can be adopted, the tool synchronizes data between the mongo db and the es data center, tracks the operation log data oplog of the mongo db, and can also perform real-time synchronous operation on the data stored in the es data center when the operation on the data in the mongo db is kept.
In the implementation process, firstly, starting a copy set mode of the mongoDB, setting a copy set name, and initializing and verifying the state of the copy set.
The method comprises the steps of configuring an es data center, setting the es data center of a mongoDB database connected through a mongoconnector, and specifically determining the ip and port of the mongoDB database and the information of an es data center cluster.
And verifying that the service experience information is updated in the mongoDB database, and inquiring whether corresponding data changes or not by the es data center.
And determining service experience information stored in the es data center as sample service experience information, preprocessing each sample service experience information, such as cleaning, desensitizing and the like, determining the preprocessed sample service experience information as second service experience information, training an original prediction model based on each second service experience information, and judging whether the current trained prediction model meets a preset convergence condition or not, thereby determining whether the trained prediction model is acquired.
If the current trained prediction model meets the preset convergence condition, the trained prediction model is determined to be obtained, the trained prediction model is output, and the first service experience information is obtained based on the method in the embodiment. And determining a first target product corresponding to the first service experience information based on the first service experience information through the trained prediction model, so that the product information of the first target product is recommended to a target account.
Based on age information of a target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account, analysis training is carried out by combining a product finally purchased by the target account, a neural network prediction model is established, intelligent product recommendation is carried out based on the model, namely, a first target product which is more suitable for the target account is provided for the target account in a targeted manner based on first service experience information of the target account through the prediction model which is completed through training.
In order to further improve accuracy of the first target products recommended to the target account, before recommending the product information of the first target products to the target account, each determined first target product may be sent to a relevant staff member, so that accuracy of each first target product may be screened through experience of the relevant staff member. When the relevant staff member determines that a certain first target product is suitable for being recommended to the target account, determination information of the first target product can be input. After receiving the determination information of the first target product, the electronic device for determining product information recommends the product information of the first target product to the target account. When the related staff member determines that a certain first target product is not suitable for being recommended to the target account, the determination information of the first target product can not be input, or error information for determining the first target product can be input, so that the product information of the first target product is not recommended to the target account later.
Example 6:
an embodiment of the present invention provides a product information device, and fig. 4 is a schematic structural diagram of a device for determining product information, where the device includes:
an obtaining unit 41, configured to obtain first service experience information of a target account;
the processing unit 42 is configured to obtain a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
a recommending unit 43, configured to determine product information of the first target product, and recommend the product information of the first target product to the target account;
wherein the apparatus further comprises: a training unit 44;
The training unit 44 is configured to obtain second service experience information of any sample account and a label corresponding to the second service experience information, where the label is used to identify a product purchased by the sample account; acquiring a second target product corresponding to the second service experience information through the original prediction model; and training the original prediction model according to the label and the second target product.
The apparatus further comprises: a preprocessing unit;
the preprocessing unit is configured to determine, if the sample service experience information includes age information of the sample account, a preprocessed age value of the sample account based on the age information of the sample account, an average value of age information of each account where the product is purchased, and a mean square error of the age information of each account where the product is purchased; if the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and correlation degrees of the preconfigured type of preference and the product.
Further, the preprocessing unit is further configured to determine, if the sample service experience information includes experience time information of the sample account, a number of reservations of the sample account in each hour in a service period and a number of hours of each reservation according to a pre-configured service period and each experience time information of the sample account; determining a pre-processed reservation time value of the sample account according to the reservation times and the hours; if the sample service experience information comprises experience evaluation information of the sample account, determining a target experience evaluation information value corresponding to the experience evaluation information of the sample account according to a corresponding relation between the pre-configured experience evaluation information and the experience evaluation information value.
Further, the preprocessing unit is specifically configured to determine, if the sample service experience information includes age information of the sample account, a preprocessed age value of the sample account based on the age information of the sample account, an average value of age information of each account for purchasing the product, and a mean square error of age information of each account for purchasing the product, where the preprocessing unit includes:
Wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product;
if the sample service experience information includes preference information of the sample account, the determining a preference value of the sample account after preprocessing of the preference based on a survey score of the sample account on the preference and a correlation degree of a preconfigured preference with the product includes:
C 3j (A i )＝λ j G j ，
wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) The value is a taste value of the sample account after pretreatment of the jth class taste; lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product;
if the sample service experience information includes experience time information of the sample account, determining a pre-processed reserved time value of the sample account according to the reserved times and the hours includes:
Wherein A is i An ith product purchased for the sample account; c (C) 4 (A i ) Is a pre-processed reservation time value for the sample account; m is the reservation times; h is the number of hours.
Further, the apparatus further comprises:
the determining unit is used for determining the target type of each piece of information in the first service experience information before acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model;
the processing unit 42 is further configured to determine, if it is determined that the first service experience information lacks information of the necessary information type according to the pre-configured necessary information type and the target type, from the pre-stored history information of each necessary information type of the target account, determine and add the history information of the lacking necessary information type to the first service experience information.
Further, the apparatus further comprises:
the determining unit is used for determining the target type of each piece of information in the first service experience information before acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model;
The processing unit 42 is further configured to filter information in the first service experience information, where the target type is not a pre-configured necessary information type.
Further, the recommending unit 43 is specifically configured to recommend the product information of the first target product to the target account if the confirmation information of the first target product is received.
Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and quickly, the recommended product is determined based on the service experience information of the target account, the method is more suitable for users to a certain extent, and the matching efficiency and user experience of the product and user requirements are improved.
Example 7:
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where, based on the above embodiments, the electronic device includes: the processor 51, the communication interface 52, the memory 53 and the communication bus 54, wherein the processor 51, the communication interface 52 and the memory 53 complete the communication with each other through the communication bus 54; the memory 53 stores a computer program which, when executed by the processor 51, causes the processor 51 to perform the steps of:
acquiring first service experience information of a target account;
acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
Determining product information of the first target product, and recommending the product information of the first target product to the target account;
the training of the original prediction model based on the second service experience information includes:
acquiring second service experience information of any sample account and a corresponding label thereof, wherein the label is used for identifying products purchased by the sample account;
acquiring a second target product corresponding to the second service experience information through the original prediction model;
and training the original prediction model according to the label and the second target product.
Since the principle of solving the problem of the electronic device is similar to that of the product information method, the implementation of the electronic device can refer to the implementation of the method, and the repetition is omitted.
The communication bus mentioned by the electronic device in the above embodiment may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus. The communication interface 52 is used for communication between the above-described electronic device and other devices. The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor. The processor may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; but also digital instruction processors (Digital Signal Processing, DSP), application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and quickly, the recommended product is determined based on the service experience information of the target account, the method is more suitable for users to a certain extent, and the matching efficiency and user experience of the product and user requirements are improved.
Example 8:
on the basis of the above embodiments, the embodiments of the present invention further provide a computer readable storage medium, in which a computer program executable by a processor is stored, which when executed on the processor causes the processor to implement the steps of:
Acquiring first service experience information of a target account;
acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
determining product information of the first target product, and recommending the product information of the first target product to the target account;
the training of the original prediction model based on the second service experience information includes:
acquiring second service experience information of any sample account and a corresponding label thereof, wherein the label is used for identifying products purchased by the sample account;
acquiring a second target product corresponding to the second service experience information through the original prediction model;
And training the original prediction model according to the label and the second target product.
Since the principle of solving the problem by the computer-readable storage medium is similar to that of the product information method in the above-described embodiment, the implementation of the product information method can be referred to as implementation of the product information method.
Because the first service experience information of the target account can be obtained in the process of recommending the product information, the first service experience information comprises at least one of age information of the target account, sex information of the target account, hobby information of the target account, experience time information of the target account and experience evaluation information of the target account. After the first service experience information of the target account is obtained, the first service experience information is fully utilized through a pre-trained prediction model, a first target product corresponding to the first service experience information is determined, and then the product information of the first target product is recommended to the target account, so that the product is recommended to the target account more conveniently and quickly, the recommended product is determined based on the service experience information of the target account, the method is more suitable for users to a certain extent, and the matching efficiency and user experience of the product and user requirements are improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (8)

1. A method of product information, the method comprising:
acquiring first service experience information of a target account;
acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
determining product information of the first target product, and recommending the product information of the first target product to the target account;
the training of the original prediction model based on the second service experience information includes:
acquiring second service experience information of any sample account and a corresponding label thereof, wherein the label is used for identifying products purchased by the sample account;
Acquiring a second target product corresponding to the second service experience information through the original prediction model;
training the original prediction model according to the label and the second target product;
before the obtaining the first target product corresponding to the first service experience information through the pre-trained prediction model, the method further includes:
determining a target type of each piece of information in the first service experience information;
if the information of the necessary information type is determined to be absent in the first service experience information according to the pre-configured necessary information type and the target type, determining the history information of the absent necessary information type from the pre-stored history information of each necessary information type of the target account, and adding the history information to the first service experience information;
the preprocessing of the sample service experience information in the sample set comprises the following steps:
if the sample service experience information comprises age information of the sample account, determining a preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account for purchasing the product, and a mean square error of the age information of each account for purchasing the product;
If the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and a correlation degree of the preconfigured type of preference and the product;
if the sample service experience information includes age information of the sample account, the determining the preprocessed age value of the sample account based on the age information of the sample account, an average value of the age information of each account for purchasing the product, and a mean square error of the age information of each account for purchasing the product includes:
wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product;
if the sample service experience information includes preference information of the sample account, the determining a preference value of the sample account after preprocessing of the preference based on a survey score of the sample account on the preference and a correlation degree of a preconfigured preference with the product includes:
C 3j (A i )＝λ j G j ，
Wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) The value is a taste value of the sample account after pretreatment of the jth class taste; lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product.
2. The method of claim 1, wherein preprocessing sample service experience information in a sample set further comprises:
if the sample service experience information comprises the experience time information of the sample account, determining the reservation times of each hour and the hour number of each reservation of the sample account in the service time period according to a preset service time period and each experience time information of the sample account; determining a preprocessed experience time value of the sample account according to the reservation times and the hours;
if the sample service experience information comprises experience evaluation information of the sample account, determining a target experience evaluation information value corresponding to the experience evaluation information of the sample account according to a corresponding relation between the pre-configured experience evaluation information and the experience evaluation information value.
3. The method of claim 2, wherein if the sample service experience information includes experience time information of the sample account, the determining the pre-processed appointment time value of the sample account according to the appointment number and the hours comprises:
wherein A is i An ith product purchased for the sample account; c (C) 4 (A i ) Is the sampleA pre-processed reservation time value of the account; m is the reservation times; h is the number of hours.
4. The method according to claim 1, wherein before the obtaining the first target product corresponding to the first service experience information by pre-training the completed prediction model, the method further includes:
determining a target type of each piece of information in the first service experience information;
and filtering the information of which the target type is not the pre-configured necessary information type in the first service experience information.
5. The method of claim 1, wherein the recommending product information for the first target product to the target account comprises:
and recommending the product information of the first target product to the target account if the confirmation information of the first target product is received.
6. A product information device, the device comprising:
the acquisition unit is used for acquiring first service experience information of the target account;
the processing unit is used for acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model; the first service experience information comprises at least one of age information of the target account, gender information of the target account, preference information of the target account, experience time information of the target account and experience evaluation information of the target account; the prediction model is obtained by training an original prediction model based on second service experience information, and the second service experience information is obtained by preprocessing sample service experience information in a sample set;
the recommending unit is used for determining the product information of the first target product and recommending the product information of the first target product to the target account;
wherein the apparatus further comprises: a training unit;
the training unit is used for acquiring second service experience information of any sample account and a corresponding label thereof, and the label is used for identifying products purchased by the sample account; acquiring a second target product corresponding to the second service experience information through the original prediction model; training the original prediction model according to the label and the second target product;
The apparatus further comprises:
the determining unit is used for determining the target type of each piece of information in the first service experience information before acquiring a first target product corresponding to the first service experience information through a pre-trained prediction model;
the processing unit is further configured to determine, if the information of the necessary information type is absent in the first service experience information according to the pre-configured necessary information type and the target type, from the pre-stored history information of each necessary information type of the target account, determine the history information of the absent necessary information type and add the history information to the first service experience information;
the apparatus further comprises: a preprocessing unit;
the preprocessing unit is configured to determine, if the sample service experience information includes age information of the sample account, a preprocessed age value of the sample account based on the age information of the sample account, an average value of age information of each account where the product is purchased, and a mean square error of the age information of each account where the product is purchased; if the sample service experience information comprises preference information of the sample account, determining a preprocessed preference value of the sample account for the type of preference based on investigation scores of the sample account for the type of preference and a correlation degree of the preconfigured type of preference and the product;
The preprocessing unit is specifically configured to, if the sample service experience information includes the sample accountAge information based onDetermining a preprocessed age value of the sample account; wherein A is i An ith product purchased for the sample account; c (C) 1 (A i ) Is the pre-processed age value of the sample account; age represents age information of the sample account; c age An average value of age information representing each account for purchasing the ith product; sigma (sigma) age Mean square error of age information representing each account for purchasing the ith product;
if the sample service experience information comprises preference information of the sample account, C is based on 3j (A i )＝λ j G j Determining a preference value of the sample account after preprocessing the preference; wherein A is i An ith product purchased for the sample account; c (C) 3j (A i ) The value is a taste value of the sample account after pretreatment of the jth class taste; lambda (lambda) j A survey score representing the sample account for category j hobbies; g j Representing the association degree of the preconfigured jth hobbies and the ith product.
7. An electronic device comprising at least a processor and a memory, the processor being adapted to implement the steps of the product information method according to any of claims 1-5 when executing a computer program stored in the memory.
8. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the product information method according to any one of claims 1-5.
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CN111461827A (en) * 2020-03-31 2020-07-28 中国银行股份有限公司 Product evaluation information pushing method and device
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WO2019061994A1 (en) * 2017-09-30 2019-04-04 平安科技（深圳）有限公司 Electronic device, insurance product recommendation method and system, and computer readable storage medium
CN111461827A (en) * 2020-03-31 2020-07-28 中国银行股份有限公司 Product evaluation information pushing method and device
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