CN117436974A - Product recommendation method, device, computer equipment and storage medium - Google Patents

Product recommendation method, device, computer equipment and storage medium Download PDF

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CN117436974A
CN117436974A CN202311253681.8A CN202311253681A CN117436974A CN 117436974 A CN117436974 A CN 117436974A CN 202311253681 A CN202311253681 A CN 202311253681A CN 117436974 A CN117436974 A CN 117436974A
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financial product
information
product
financial
bipartite graph
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苑倩倩
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Bank of China Ltd
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Bank of China Ltd
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    • G06F16/9035—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
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    • G—PHYSICS
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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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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The present application relates to a product recommendation method, apparatus, computer device, storage medium and computer program product, which can be used in the field of artificial intelligence technology, and also in the field of finance or other related fields. The method and the device can improve product recommendation efficiency and accuracy. The method comprises the following steps: acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product; processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product; inputting the two graphs into a correlation prediction model to obtain the correlation between the object and the financial product; according to the correlation, determining a target financial product in the financial products; the target financial product is a financial product recommended to the subject.

Description

Product recommendation method, device, computer equipment and storage medium
Technical Field
The present application relates to the field of artificial intelligence technology, and in particular, to a product recommendation method, apparatus, computer device, storage medium, and computer program product.
Background
With the development of computer technology, users can select the products of the cardiology instrument according to the own demands. However, due to the rapid increase in the number of products, it is often time consuming for the user to find a suitable product from a vast amount of information. Therefore, how to efficiently recommend products has become an important research direction.
The traditional technology generally analyzes the data of the user manually to determine the recommended products for the user; however, this technique requires a lot of manual processing time, resulting in low product recommendation efficiency.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a product recommendation method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve product recommendation efficiency.
In a first aspect, the present application provides a product recommendation method. The method comprises the following steps:
acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product;
processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In one embodiment, the processing the operation information and the attribute information to obtain a bipartite graph includes:
processing the operation information and the attribute information to obtain the node of the object, the node of the financial product and the connection edge between the node of the object and the node of the financial product;
and constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges.
In one embodiment, the inputting the bipartite graph into a relevance prediction model to obtain the relevance between the object and the financial product includes:
inputting the bipartite graph into a relevance prediction model, and simulating the random movement process of the object in the bipartite graph through the relevance prediction model to obtain the interest degree of the object on the financial product;
and determining the correlation degree between the object and the financial product according to the interest degree.
In one embodiment, the acquiring the operation information of the object for the financial product includes:
acquiring historical browsing information, historical acquisition information and historical evaluation information of an object aiming at a financial product;
and combining the history browsing information, the history acquisition information and the history evaluation information to obtain the operation information of the object aiming at the financial product.
In one embodiment, the acquiring attribute information of the financial product includes:
acquiring type information, deadline information, risk information and resource gain information of the financial product;
and combining the type information, the deadline information, the risk information and the resource gain information to obtain the attribute information of the financial product.
In one embodiment, after determining the target financial product in the financial products according to the correlation, the method further includes:
sorting the target financial products according to the relativity of the target financial products to obtain a financial product recommendation list;
and recommending the target financial product to the object according to the financial product recommendation list.
In a second aspect, the present application further provides a product recommendation device. The device comprises:
the information acquisition module is used for acquiring operation information of an object aiming at a financial product and acquiring attribute information of the financial product;
the information processing module is used for processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
the information input module is used for inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
the product determining module is used for determining a target financial product from the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product;
processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product;
processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product;
processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
The product recommendation method, the device, the computer equipment, the storage medium and the computer program product acquire the operation information of an object aiming at a financial product and acquire the attribute information of the financial product; processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product; inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product; determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject. According to the scheme, the two graphs are constructed by acquiring the operation information of the object and the attribute information of the financial product, and the correlation of the object to the financial product is predicted by utilizing the correlation prediction model, so that the target financial product is determined in the financial product according to the correlation, the recommendation of the financial product aiming at the object is realized, and the product recommendation efficiency and the product recommendation accuracy are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a flow chart of a product recommendation method according to an embodiment;
FIG. 2 is a flow chart illustrating steps for constructing a bipartite graph according to one embodiment;
FIG. 3 is a flowchart illustrating steps for determining relevance in one embodiment;
FIG. 4 is a block diagram of a product recommendation device in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
In one embodiment, as shown in fig. 1, a product recommendation method is provided, and the method is applied to a terminal for illustration in this embodiment; it will be appreciated that the method may also be applied to a server, and may also be applied to a system comprising a terminal and a server, and implemented by interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers and the like; the server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. In this embodiment, the method includes the steps of:
step S101, acquiring operation information of an object for a financial product, and acquiring attribute information of the financial product.
In this step, the object may be a user; the operation information may include history browsing information, history acquisition information, and history evaluation information of the object to the financial product, for example, the operation information may be history browsing record, history clicking record, history acquisition record, history evaluation record, or the like of the object to the financial product; the attribute information of the financial product may include a product type, a term (e.g., short term, medium term, long term, etc.), a risk level, etc., for describing characteristics of the financial product.
Specifically, the terminal acquires operation information of an object for a financial product and attribute information of the financial product from a financial system.
Step S102, processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used to represent the relationship between an object and a financial product.
In this step, the bipartite graph may be a graph consisting of two node sets, where each edge connects one node from the first node set and one node from the second node set.
Specifically, the terminal processes the operation information and the attribute information, and can respectively use the object and the financial product as two node sets of the bipartite graph, and establish an edge between the two node sets according to the operation information and the attribute information (for example, the edge can be established between the object and the financial product browsed or acquired by the object according to the browsing record and the acquiring record of the object and the financial product, and simultaneously, the edge can be established between the financial products with similar attributes according to the attribute information of the financial product), so as to obtain the bipartite graph.
Step S103, inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product.
In the step, a relevance prediction model can adopt a machine learning algorithm, such as collaborative filtering, a content-based recommendation algorithm and the like, and can also be a model constructed based on a random walk algorithm, and the relevance prediction model can be used for predicting the interest degree or the relevance degree of an object to a financial product according to the operation information of the object and the attribute information of the financial product; the relevance may refer to a degree of relevance or a measure of relevance between an object and a financial product, and in a recommendation system, the degree of interest or matching of the object to the financial product may be measured by using the relevance, so as to make a recommendation, where the relevance is generally represented by a numerical value, and a higher numerical value indicates a higher relevance.
Specifically, the terminal takes the built bipartite graph as input data of a correlation prediction model, inputs the bipartite graph into the correlation prediction model, predicts the correlation between the object and the financial product through the correlation prediction model, and obtains the correlation between the object and the financial product.
Step S104, determining a target financial product from the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In this step, the target financial product may be one or more of the financial products, and the target financial product may be a target financial product recommended to the subject.
Specifically, the terminal sorts the financial products according to the predicted relevance value, selects the financial products with higher relevance as target financial products, for example, the financial products can be arranged in descending order according to the relevance value, and selects the first financial products with higher relevance as target financial products.
In the product recommendation method, the operation information of the object for the financial product is obtained, and the attribute information of the financial product is obtained; processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product; inputting the two graphs into a correlation prediction model to obtain the correlation between the object and the financial product; according to the correlation, determining a target financial product in the financial products; the target financial product is a financial product recommended to the subject. According to the scheme, the two graphs are constructed by acquiring the operation information of the object and the attribute information of the financial product, and the correlation of the object to the financial product is predicted by utilizing the correlation prediction model, so that the target financial product is determined in the financial product according to the correlation, the recommendation of the financial product aiming at the object is realized, and the product recommendation efficiency and the product recommendation accuracy are improved.
In one embodiment, as shown in fig. 2, in step S102, the operation information and the attribute information are processed to obtain a bipartite graph, which specifically includes the following contents:
step S201, processing the operation information and the attribute information to obtain nodes of the object, nodes of the financial product and connected edges between the nodes of the object and the nodes of the financial product;
step S202, constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges.
In this embodiment, the node of the object and the node of the financial product respectively belong to two node sets, and the connected edge represents the relationship between the node of the object and the node of the financial product; a node may be an element representing an entity or object in a bipartite graph, in which embodiment the node may represent an object or financial product; the connected edges may be lines in the bipartite graph representing a relationship or connection between two nodes, in which embodiment the connected edges may represent a relationship or connection between a node of the object and a node of the financial product.
Specifically, the terminal can identify all related objects and all financial products according to the operation information, and take the objects and the financial products as nodes of the bipartite graph to obtain the nodes of the objects and the nodes of the financial products, and simultaneously, according to the attribute information, edges can be established between the financial products with similar attributes to obtain the connected edges between the nodes of the financial products; and establishing edges between the nodes of the object and the nodes of the financial product according to the operation information and the attribute information, obtaining the connected edges between the nodes of the object and the nodes of the financial product, and constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges.
According to the technical scheme provided by the embodiment, the bipartite graph is constructed according to the nodes of the object, the nodes and the connected edges of the financial product, so that the bipartite graph can be obtained more efficiently and accurately, and the product recommendation efficiency and accuracy can be improved.
In one embodiment, as shown in fig. 3, in step S103, the two-part graph is input into a relevance prediction model to obtain a relevance between the object and the financial product, which specifically includes the following contents:
step S301, inputting the bipartite graph into a correlation prediction model, and simulating a random movement process of the object in the bipartite graph through the correlation prediction model to obtain the interest degree of the object on the financial product;
step S302, determining the correlation degree between the object and the financial product according to the interest degree.
In this embodiment, the interestingness may refer to the preference degree or matching degree of the object to the financial product, and in this embodiment, the interestingness may be obtained by simulating a random moving process of the object in the bipartite graph by using the relevance prediction model, where a higher value indicates a higher interestingness of the object to the financial product.
Specifically, the terminal inputs the constructed bipartite graph into a correlation prediction model; the random moving process of the object in the two-part graph can be simulated through a correlation prediction model, wherein the process can determine the next moving of the object in the two-part graph through the correlation between the node of the object and the connected edge, and specifically, a neighboring node with higher correlation can be selected as the next position of the object according to the correlation value between the current node of the object and the connected edge; through simulating the random moving process of the object, the interest degree of the object on the financial product can be obtained, wherein the interest degree can represent the preference degree or the matching degree of the object on the financial product; according to the obtained interestingness, the relativity between the object and the financial product can be determined, and in general, the higher the interestingness value is, the larger the relativity between the object and the financial product is, or the interestingness can be taken as the relativity between the object and the financial product.
According to the technical scheme provided by the embodiment, the degree of interest is calculated, so that the degree of correlation between the object and the financial product can be determined more efficiently and accurately, and the product recommendation efficiency and accuracy are improved.
In one embodiment, in step S101, operation information of an object for a financial product is acquired, which specifically includes the following contents: acquiring historical browsing information, historical acquisition information and historical evaluation information of an object aiming at a financial product; and combining the historical browsing information, the historical acquisition information and the historical evaluation information to obtain the operation information of the object aiming at the financial product.
In this embodiment, the history browsing information may refer to records of browsing the financial product by the object in the past, and these records may include detailed information, browsing time, browsing times, etc. of the financial product that the object has viewed; the history acquisition information may refer to records of the object's acquisition or application of the financial product in the past, and these records may include the type of the financial product acquired by the object, the acquisition time, the acquisition number, etc.; the historical evaluation information may refer to records of evaluation or feedback of the financial product by the subject in the past, and the records may include satisfaction degree score, evaluation content, evaluation time and the like of the financial product by the subject; the combined processing may refer to the process of integrating and processing multiple data sources or data types, and in this embodiment, the combined processing of historical browsing information, historical acquisition information, and historical evaluation information may include data cleansing, feature extraction, data conversion, and the like, to obtain more meaningful and useful operational information.
Specifically, the terminal collects historical browsing information of the object on the financial product (the information can comprise detailed information, browsing time, browsing times and the like of the financial product which the object looks for) by acquiring the browsing record of the object on the platform or the application program on the financial product; recording historical acquisitions or acquisition records of the object for the financial products (such information may include type of financial product acquired by the object, acquisition time, number of acquisitions, etc.); collecting historical evaluation or feedback information of the object on the financial product (the information can comprise satisfaction degree score, evaluation content, evaluation time and the like of the object on the financial product); and combining the acquired history browsing information, history acquisition information and history evaluation information to obtain the operation information of the object aiming at the financial product.
According to the technical scheme provided by the embodiment, through collecting the historical browsing information, the historical acquisition information and the historical evaluation information of the object aiming at the financial product, the behavior and the preference of the object on the financial product can be obtained, so that the interest and the demand of the object on the financial product can be better known, and the product recommendation efficiency and the product recommendation accuracy can be improved.
In one embodiment, in step S101, attribute information of a financial product is acquired, which specifically includes the following: acquiring type information, deadline information, risk information and resource gain information of a financial product; and combining the type information, the deadline information, the risk information and the resource gain information to obtain attribute information of the financial product.
In this embodiment, the type information may refer to a type or a kind of a financial product; the term information may refer to a term of the financial product, such as short term, medium term, long term, etc.; the risk information may refer to a risk level of the financial product, such as low risk, medium risk, high risk, etc.; the resource gain information may refer to a resource gain condition of the financial product, such as a rate of return or a rate of increase.
Specifically, the terminal acquires type information, deadline information, risk information and resource gain information of the financial product; and combining the acquired type information, deadline information, risk information and resource gain information of the financial product to obtain attribute information of the financial product (for example, the type information, deadline information, risk information and resource gain information can be weighted to obtain a comprehensive attribute score so as to reflect the comprehensive characteristics of the financial product).
According to the technical scheme provided by the embodiment, the type information, the term information, the risk information and the resource gain information of the financial products are obtained, and the information is combined, so that more accurate attribute information of the financial products can be obtained, and the product recommendation accuracy can be improved.
In one embodiment, the step S104 further includes the step of recommending the target financial product after determining the target financial product according to the correlation, and the step specifically includes the following steps: sorting the target financial products according to the correlation degree of the target financial products to obtain a financial product recommendation list; and recommending the target financial product to the object according to the financial product recommendation list.
In this embodiment, the financial product recommendation list may refer to a financial product list obtained by sorting according to the relevance of the target financial product, for example, the financial products in the financial product recommendation list are sorted from high to low according to the relevance, and the financial product with the highest recommendation is sorted in front of the financial product recommendation list.
Specifically, the terminal sorts the target financial products according to the correlation degree of the target financial products; according to the sorting result, a financial product recommendation list is obtained; and recommending the target financial products to the target objects according to the financial product recommendation list, wherein the recommendation can be performed in different modes, such as pushing notification, displaying in a website or an application program.
According to the technical scheme provided by the embodiment, the recommendation of the financial products is realized by combining the correlation calculation and the ordering algorithm according to the attribute information of the financial products and the requirements of the objects, so that the recommendation accuracy and the feedback timeliness of the products are improved, and the recommendation effect of the products is optimized.
The following describes a product recommendation method provided by the present application in an embodiment, where the method is applied to a terminal to illustrate, and the main steps include:
the method comprises the steps that a terminal obtains historical browsing information, historical obtaining information and historical evaluating information of an object aiming at a financial product; and combining the historical browsing information, the historical acquisition information and the historical evaluation information to obtain the operation information of the object aiming at the financial product.
The second step, the terminal obtains the type information, the deadline information, the risk information and the resource gain information of the financial products; and combining the type information, the deadline information, the risk information and the resource gain information to obtain attribute information of the financial product.
Thirdly, the terminal processes the operation information and the attribute information to obtain nodes of the object, nodes of the financial product and connected edges between the nodes of the object and the nodes of the financial product; constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges; the bipartite graph is used to represent the relationship between an object and a financial product.
Inputting the two graphs into a correlation prediction model by the terminal, and simulating the random movement process of the object in the two graphs through the correlation prediction model to obtain the interest degree of the object on the financial product; and determining the correlation degree between the object and the financial product according to the interest degree.
And fifthly, determining a target financial product from the financial products by the terminal according to the correlation.
Sixth, the terminal sorts the target financial products according to the correlation degree of the target financial products to obtain a financial product recommendation list; and recommending the target financial product to the object according to the financial product recommendation list.
According to the technical scheme provided by the embodiment, the two-part graph is constructed by acquiring the operation information of the object and the attribute information of the financial product, and the correlation degree of the object to the financial product is predicted by utilizing the correlation degree prediction model, so that the target financial product is determined in the financial product according to the correlation degree, the recommendation of the financial product aiming at the object is realized, and the product recommendation efficiency and the product recommendation accuracy are improved.
The product recommendation method provided by the application is described below by using an application example, and the application example is applied to a terminal by using the method for illustration, and the main steps include:
first, the terminal builds a database table for storing data of the object for the product.
Specifically, the terminal creates a table for storing behavior data generated by the object for the product.
And secondly, the terminal converts behavior data of the object on the product into a two-part graph (or a two-part network).
Thirdly, the terminal calculates the correlation between the object and the product by applying a random walk algorithm aiming at a bipartite graph formed between the object and the product, and finally generates a recommendation list according to the correlation.
The terminal can calculate the similarity between the objects through the calculation model, collect and calculate the similarity between the products for the attributes such as the functions of the products, and recommend corresponding commodities for the similar objects.
According to the technical scheme provided by the application example, the recommendation of different products to different objects is calculated by using a random walk algorithm, so that more adaptive products are recommended to the objects, and the pertinence of the recommended products can be improved; the method comprises the steps of obtaining information such as browsing products by an object, converting the information into two graphs, and then applying a random walk algorithm to a matrix corresponding to the graph to obtain goods recommended to the object, so that more suitable and richer products are provided for the object; and the product recommendation efficiency and accuracy are improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a product recommendation device for realizing the above related product recommendation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation of one or more embodiments of the product recommendation device provided below may refer to the limitation of the product recommendation method hereinabove, and will not be repeated here.
In one embodiment, as shown in FIG. 4, a product recommendation device is provided, the device 400 may include:
an information acquisition module 401, configured to acquire operation information of an object for a financial product, and acquire attribute information of the financial product;
an information processing module 402, configured to process the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
the information input module 403 is configured to input the bipartite graph to a relevance prediction model, so as to obtain a relevance between the object and the financial product;
the product determining module 404 is configured to determine a target financial product from the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
In one embodiment, the information processing module 402 is further configured to process the operation information and the attribute information to obtain a node of the object, a node of the financial product, and a connection edge between the node of the object and the node of the financial product; and constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges.
In one embodiment, the information input module 403 is further configured to input the bipartite graph to a relevance prediction model, and simulate a random moving process of the object in the bipartite graph through the relevance prediction model, so as to obtain the interest degree of the object in the financial product; and determining the correlation degree between the object and the financial product according to the interest degree.
In one embodiment, the information obtaining module 401 is further configured to obtain historical browsing information, historical obtaining information and historical evaluation information of the object for the financial product; and combining the historical browsing information, the historical acquisition information and the historical evaluation information to obtain the operation information of the object aiming at the financial product.
In one embodiment, the information obtaining module 401 is further configured to obtain type information, deadline information, risk information, and resource gain information of the financial product; and combining the type information, the deadline information, the risk information and the resource gain information to obtain attribute information of the financial product.
In one embodiment, the apparatus 400 further comprises: the product ordering module is used for ordering the target financial products according to the correlation of the target financial products to obtain a financial product recommendation list; and recommending the target financial product to the object according to the financial product recommendation list.
The respective modules in the above-described product recommendation apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
It should be noted that the method and the device for product recommendation provided by the present application may be used in the application field related to product recommendation in the financial field, and may also be used in the processing related to product recommendation in any field other than the financial field, where the application field of the method and the device for product recommendation provided by the present application is not limited.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a product recommendation method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims (10)

1. A method of product recommendation, the method comprising:
acquiring operation information of an object aiming at a financial product, and acquiring attribute information of the financial product;
processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
determining a target financial product in the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
2. The method according to claim 1, wherein the processing the operation information and the attribute information to obtain a bipartite graph includes:
processing the operation information and the attribute information to obtain the node of the object, the node of the financial product and the connection edge between the node of the object and the node of the financial product;
and constructing a bipartite graph according to the nodes of the object, the nodes of the financial product and the connected edges.
3. The method of claim 1, wherein inputting the bipartite graph into a relevance prediction model yields a relevance between the object and the financial product, comprising:
inputting the bipartite graph into a relevance prediction model, and simulating the random movement process of the object in the bipartite graph through the relevance prediction model to obtain the interest degree of the object on the financial product;
and determining the correlation degree between the object and the financial product according to the interest degree.
4. The method of claim 1, wherein the obtaining operation information of the object for the financial product comprises:
acquiring historical browsing information, historical acquisition information and historical evaluation information of an object aiming at a financial product;
and combining the history browsing information, the history acquisition information and the history evaluation information to obtain the operation information of the object aiming at the financial product.
5. The method of claim 1, wherein the obtaining attribute information of the financial product comprises:
acquiring type information, deadline information, risk information and resource gain information of the financial product;
and combining the type information, the deadline information, the risk information and the resource gain information to obtain the attribute information of the financial product.
6. The method according to any one of claims 1 to 5, further comprising, after determining a target financial product among the financial products according to the correlation degree:
sorting the target financial products according to the relativity of the target financial products to obtain a financial product recommendation list;
and recommending the target financial product to the object according to the financial product recommendation list.
7. A product recommendation device, the device comprising:
the information acquisition module is used for acquiring operation information of an object aiming at a financial product and acquiring attribute information of the financial product;
the information processing module is used for processing the operation information and the attribute information to obtain a bipartite graph; the bipartite graph is used for representing the relation between the object and the financial product;
the information input module is used for inputting the bipartite graph into a correlation prediction model to obtain the correlation between the object and the financial product;
the product determining module is used for determining a target financial product from the financial products according to the correlation; the target financial product is a financial product recommended to the subject.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
CN202311253681.8A 2023-09-26 2023-09-26 Product recommendation method, device, computer equipment and storage medium Pending CN117436974A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311253681.8A CN117436974A (en) 2023-09-26 2023-09-26 Product recommendation method, device, computer equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311253681.8A CN117436974A (en) 2023-09-26 2023-09-26 Product recommendation method, device, computer equipment and storage medium

Publications (1)

Publication Number Publication Date
CN117436974A true CN117436974A (en) 2024-01-23

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Country Link
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