Product recommendation method based on meta universe

Abstract

The invention provides a meta-universe-based product recommendation method, which can be applied to the fields of meta universe and artificial intelligence. The method comprises the following steps: in response to interaction between a user and a meta universe platform, collecting interaction data of the user; processing the interaction data based on a decision unit to obtain an n-dimensional weight coefficient of the user, n being an integer greater than 1, and the weight coefficient representing a consumption tendency of the user; obtaining a plurality of user-oriented products to be recommended and n-dimensional product parameters of each product to be recommended, and establishing an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficients; and according to the n-dimensional model, determining a target product matched with the user from the plurality of recommended products. The invention further provides a product recommendation device and equipment based on the meta universe, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of metaverse and artificial intelligence, and in particular to a product recommendation method, device, equipment, medium and program product based on metaverse. Background Art

[0002] The Metaverse is the next generation of the Internet that uses Virtual Reality (VR) as a medium. The changes it will bring to human society will exceed the changes brought about by mobile Internet in the past decade.

[0003] Currently, product recommendations in the financial industry are based on "one-dimensional" data such as risk level and behavioral preferences. This has the disadvantages of poor recommendation results, lack of targeting, and impact on customer experience. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a product recommendation method, apparatus, device, medium and program product based on the metaverse.

[0005] According to the first aspect of the present disclosure, a product recommendation method based on the Metaverse is provided, including: in response to the user interacting with the Metaverse platform, collecting the user's interaction data; processing the interaction data based on a decision unit to obtain the user's n-dimensional weight coefficient, wherein n is an integer greater than 1, and the weight coefficient represents the user's consumption tendency; obtaining multiple products to be recommended to the user and n-dimensional product parameters of each product to be recommended, and establishing an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficient; and determining the target product matching the user from the multiple recommended products according to the n-dimensional model.

[0006] According to an embodiment of the present disclosure, there are multiple Metaverse platforms; collecting user interaction data includes: collecting partial interaction data of users on each Metaverse platform, wherein the partial interaction data is multimodal data; integrating multiple partial interaction data of multiple Metaverse platforms to obtain user interaction data.

[0007] According to an embodiment of the present disclosure, the interactive data is processed based on the decision unit to obtain the user's n-dimensional weight coefficient, including performing the following operations for any target dimension of the n dimensions: on the target dimension, multiple keywords and the weight components corresponding to each keyword are pre-set, wherein each keyword represents a decision basis for the user's consumption tendency in the target dimension, and the sum of the multiple weight components of the multiple keywords is 1; for each keyword in the multiple keywords, the keyword is used as a decision unit, and the positive frequency frontier and the negative frequency frontier of the decision unit are determined according to the interactive data; based on the positive frequency frontier, the negative frequency frontier and the weight component, the weight coefficient of the user in the target dimension is determined.

[0008] According to an embodiment of the present disclosure, a positive frequency frontier and a negative frequency frontier of a decision unit are determined based on interactive data, including: based on the interactive data, counting the number of occurrences of keywords in the interactive data within a preset time period; and determining the positive frequency frontier and the negative frequency frontier of the decision unit based on the number of occurrences and the preset time period.

[0009] According to an embodiment of the present disclosure, the weight coefficient of the user in the target dimension is determined based on the positive frequency frontier, the negative frequency frontier and the weight component, including: calculating the difference between the positive frequency frontier and the negative frequency frontier of each decision unit; weightedly summing up multiple differences of multiple decision units with the corresponding weight components, to obtain the weight coefficient of the user in the target dimension.

[0010] According to an embodiment of the present disclosure, the n-dimensional model displays n-dimensional product parameters and n-dimensional weight coefficients through n axes, wherein: the position of the user's n-dimensional weight coefficient on each axis is connected by a broken line to form a closed polygon, recorded as the first polygon; the position of the n-dimensional product parameter of each product to be recommended on each axis is connected by a broken line to form a closed polygon, recorded as the second polygon.

[0011] According to an embodiment of the present disclosure, a target product matching a user is determined from multiple recommended products based on an n-dimensional model, including: for any product to be recommended among multiple recommended products, calculating the degree of overlap between a first polygon and a second polygon of the product to be recommended; when the degree of overlap is greater than a preset degree of overlap threshold, determining the product to be recommended as the target product matching the user.

[0012] The second aspect of the present disclosure provides a product recommendation device based on the Metaverse, including: an interactive data collection module, which is used to collect user interaction data in response to the user interacting with the Metaverse platform; a weight coefficient determination module, which is used to process the interactive data based on the decision unit to obtain the user's n-dimensional weight coefficient, wherein n is an integer greater than 1, and the weight coefficient represents the user's consumption tendency; an n-dimensional model establishment module, which is used to obtain multiple products to be recommended to the user and n-dimensional product parameters of each product to be recommended, and establish an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficient; a target product determination module, which is used to determine the target product matching the user from multiple recommended products according to the n-dimensional model.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 A diagram schematically illustrates an application scenario of a product recommendation method, apparatus, device, medium, and program product based on the Metaverse according to an embodiment of the present disclosure;

[0018] Figure 2 A flowchart of a product recommendation method based on the metaverse according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 3 Schematically shows a flow chart of determining n-dimensional weight coefficients according to an embodiment of the present disclosure;

[0020] Figure 4 The structure diagram of the n-dimensional model according to the embodiment of the present disclosure is schematically shown;

[0021] Figure 5 A flowchart of target product determination according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 6 A structural block diagram of a product recommendation device based on the metaverse according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 7 A block diagram of an electronic device suitable for implementing a product recommendation method based on the metaverse according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0025] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0026] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0027] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0028] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0029] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0030] The embodiment of the present disclosure provides a product recommendation method based on the Metaverse, which can be applied to the fields of Metaverse and artificial intelligence. The method includes: in response to the user interacting with the Metaverse platform, collecting the user's interaction data; processing the interaction data based on the decision unit to obtain the user's n-dimensional weight coefficient, where n is an integer greater than 1, and the weight coefficient represents the user's consumption tendency; obtaining multiple products to be recommended for the user and n-dimensional product parameters of each product to be recommended, and establishing an n-dimensional model based on the n-dimensional product parameters and the n-dimensional weight coefficient; according to the n-dimensional model, determining the target product that matches the user from multiple recommended products.

[0031] Figure 1 The application scenario diagram of the metaverse-based product recommendation method, apparatus, device, medium, and program product according to an embodiment of the present disclosure is schematically shown.

[0032] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0033] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0034] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0035] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0036] It should be noted that the product recommendation method based on the Metaverse provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the product recommendation device based on the Metaverse provided in the embodiment of the present disclosure can generally be set in the server 105. The product recommendation method based on the Metaverse provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the product recommendation device based on the Metaverse provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0037] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0038] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The product recommendation method based on the metaverse in an embodiment of the present disclosure is described in detail.

[0039] Figure 2 The flowchart of the product recommendation method based on the metaverse according to an embodiment of the present disclosure is schematically shown.

[0040] like Figure 2 As shown, the product recommendation method based on the metaverse of this embodiment includes operations S210 to S240, and the product recommendation method based on the metaverse can be executed by a server.

[0041] In operation S210 , in response to a user interacting with the Metaverse platform, user interaction data is collected.

[0042] In an embodiment of the present disclosure, before collecting the user's interactive data, the user's consent or authorization may be obtained. For example, before operation S210, a request to collect the user's interactive data may be issued. When the user agrees or authorizes that the interactive data may be collected, operation S210 is performed.

[0043] In operation S220, the interactive data is processed based on the decision unit to obtain an n-dimensional weight coefficient of the user, where n is an integer greater than 1, and the weight coefficient represents the consumption tendency of the user.

[0044] In the embodiments of the present disclosure, a corresponding operation portal may be provided for the user to choose to agree or reject the automated processing result. That is, before the interactive data is processed based on the decision unit, an instruction to agree or reject the processing input by the user through the corresponding operation portal may be obtained. If the user agrees to the processing, the interactive data is processed based on the decision unit, that is, step S220 is executed. If the user rejects the processing, the expert processing process is entered.

[0045] For example, consumption propensity may be a user's asset rating, financial needs, consumption channel preferences, transaction preferences, product preferences, investment preferences, etc.

[0046] In operation S230, a plurality of products to be recommended to the user and n-dimensional product parameters of each product to be recommended are obtained, and an n-dimensional model is established according to the n-dimensional product parameters and the n-dimensional weight coefficients.

[0047] For example, the n-dimensional product parameters of each product to be recommended can be set independently by the product designer.

[0048] In operation S240 , a target product matched with the user is determined from a plurality of recommended products according to the n-dimensional model.

[0049] For example, the product to be recommended may be a physical product or a virtual product, and the present disclosure does not impose any specific restrictions on the type of the product to be recommended.

[0050] Through the embodiments of the present disclosure, after the Metaverse technology becomes popular, the interaction between users and the Metaverse can be utilized to obtain more accurate and detailed interaction data. After the interaction data is processed based on the decision-making unit, a multi-dimensional weight coefficient of the user is formed. After matching it with the multi-dimensional product parameters of the financial product, target products with a higher adaptation rate can be recommended to customers, thereby improving the product recommendation effect and accuracy and increasing the marketing success rate.

[0051] In this embodiment, there are multiple Metaverse platforms; the above operation S210 of collecting user interaction data includes: collecting partial interaction data of the user on each Metaverse platform, wherein the partial interaction data is multimodal data; integrating multiple partial interaction data of multiple Metaverse platforms to obtain user interaction data.

[0052] For example, the Metaverse platform can be a Metaverse platform for multiple interactive activities for users, and some interactive data can be collected from multiple interactive activity platforms of the Metaverse. This part of the interactive data can include information such as user speech keywords, consumption habits, and access records. Some interactive data is multimodal data. Among them, multimodal data refers to data that contains multiple types of information at the same time, usually involving multiple forms such as text, images, and audio.

[0053] It should be noted that partial interaction data refers to the interaction data of users on each Metaverse platform. Since there can be multiple Metaverse platforms, partial interaction data of users on multiple Metaverse platforms are integrated to form relatively complete interaction data. Integration refers to sorting and merging.

[0054] In this way, more accurate and detailed interaction data can be obtained by using the interaction between the Metaverse platform and users. In addition, the effective integration of data in different modes can make up for the shortcomings of using single-mode data to recommend financial products to customers, such as poor recommendation effect, lack of pertinence, and poor customer experience.

[0055] Figure 3 The flowchart of determining the n-dimensional weight coefficient according to an embodiment of the present disclosure is schematically shown.

[0056] like Figure 3 As shown, in this embodiment, the above operation S220 processes the interactive data based on the decision unit to obtain the user's n-dimensional weight coefficient, including performing the following operations S321 to S323 for any target dimension of the n dimensions.

[0057] In operation S321, multiple keywords and weight components corresponding to each keyword are pre-set in the target dimension, wherein each keyword represents a decision basis for the user's consumption tendency in the target dimension, and the sum of multiple weight components of the multiple keywords is 1.

[0058] Each weight component can be understood as the influence factor of the corresponding keyword among multiple keywords. The larger the influence factor, the greater the consumption tendency of the corresponding dimension of the keyword.

[0059] In operation S322, for each keyword among the multiple keywords, the keyword is taken as a decision unit, and a positive frequency frontier and a negative frequency frontier of the decision unit are determined according to the interaction data.

[0060] Each keyword is regarded as a decision making unit (DMU), and there are n decision making units in total. In each decision making unit, the positive frequency frontier and the negative frequency frontier can be analyzed based on the interaction data collected above.

[0061] In this embodiment, the positive frequency frontier and negative frequency frontier of the decision unit are determined based on the interaction data, including: based on the interaction data, counting the number of occurrences of keywords in the interaction data within a preset time period; and determining the positive frequency frontier and negative frequency frontier of the decision unit based on the number of occurrences and the preset time period.

[0062] It can be understood that for any decision-making unit, the larger the positive frequency frontier, the larger the weight coefficient of the user in this dimension, and vice versa. And the larger the negative frequency frontier, the smaller the weight coefficient of the user in this dimension, and vice versa.

[0063] In operation S323, according to the positive frequency frontier, the negative frequency frontier, and the weight component, determine the weight coefficient of the user in the target dimension.

[0064] Thus, the present disclosure provides an implementation manner for accurately calculating the weight coefficients of the user in multiple dimensions.

[0065] In this embodiment, determining the weight coefficient of the user in the target dimension according to the positive frequency frontier, the negative frequency frontier, and the weight component includes: calculating the difference between the positive frequency frontier and the negative frequency frontier of each decision-making unit; performing weighted summation on the multiple differences of the multiple decision-making units with the corresponding weight components respectively to obtain the weight coefficient of the user in the target dimension.

[0066] For example, for any target dimension of n dimensions, the weight coefficient of the user in the target dimension can be determined according to the following formula:

[0067]

[0068] In the formula, K(i) is the weight coefficient of the i-th dimension, i = 1, 2,..., n, are respectively the positive frequency frontier and the negative frequency frontier of the j-th decision-making unit of the i-th dimension, is the weight component of the j-th keyword of the i-th dimension, j = 1, 2,..., m, and m is the number of keywords of the i-th dimension.

[0069] It should be noted that for different dimensions of n dimensions, different numbers of keywords can be set. The weight coefficient 0 < K(i) < 1 for each dimension of the user, and the sum of the weight coefficients K(i) of n dimensions is 1.

[0070] For example, according to the user's needs, n = 5 can be set, and the n dimensions are respectively risk tolerance, cycle prediction, cash demand, trading frequency, investment return, etc. The present disclosure does not make specific limitations on each dimension of the n-dimensional weight coefficient.

[0071] Through the embodiments of the present disclosure, each keyword is regarded as a decision-making unit, the number of occurrences of the selected keywords in the interaction data is used as the data pool, and the frequency frontiers are drawn for the positive evaluation and negative evaluation of all keywords by using an algorithm. The distance between the two is used to measure the weight coefficient K(i) of this dimension. Finally, a relative comparison is made on all selected keywords to calculate the accurate weight coefficient on the target dimension.

[0072] Figure 4The structure diagram of the n-dimensional model according to an embodiment of the present disclosure is schematically shown.

[0073] like Figure 4 As shown, in this embodiment, the n-dimensional model of the above operation S230 displays n-dimensional product parameters and n-dimensional weight coefficients through n axes, where:

[0074] The position of the user's n-dimensional weight coefficient on each axis is connected by a broken line to form a closed polygon, which is recorded as the first polygon;

[0075] The positions of the n-dimensional product parameters of each product to be recommended on each axis are connected by a broken line to form a closed polygon, which is recorded as the second polygon.

[0076] It can be seen that the n-dimensional model is equivalent to a radar chart, which is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. In this way, the relationship between n-dimensional product parameters and n-dimensional weight coefficients of users is presented on the n-dimensional model, and users can intuitively understand the relationship and trade-off between products and users, make decisions and optimize strategies.

[0077] Figure 5 The flowchart of target product determination according to an embodiment of the present disclosure is schematically shown.

[0078] like Figure 5 As shown, in this embodiment, the above operation S240 determines the target product matched by the user from multiple recommended products according to the n-dimensional model, including operations S541 to S542.

[0079] In operation S541, for any product to be recommended among a plurality of recommended products, a degree of overlap between a first polygon and a second polygon of the product to be recommended is calculated.

[0080] In operation S542, when the degree of overlap is greater than a preset degree of overlap threshold, the product to be recommended is determined as a target product matched by the user.

[0081] The overlap threshold can be set by the product designer. In this way, the relationship between the n-dimensional product parameters and the n-dimensional weight coefficients of the user is presented on the n-dimensional model. If the overlap reaches a certain level, the product can be recommended to the user.

[0082] Based on the above-mentioned product recommendation method based on Metaverse, the present disclosure also provides a product recommendation device based on Metaverse. Figure 6 The device is described in detail.

[0083] Figure 6 The structural block diagram of a product recommendation device based on the metaverse according to an embodiment of the present disclosure is schematically shown.

[0084] like Figure 6 As shown, the product recommendation device 600 based on the metaverse of this embodiment includes an interactive data collection module 610, a weight coefficient determination module 620, an n-dimensional model establishment module 630 and a target product determination module 640.

[0085] The interactive data collection module 610 is used to collect the user's interactive data in response to the user's interaction with the Metaverse platform. In one embodiment, the interactive data collection module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0086] The weight coefficient determination module 620 is used to process the interactive data based on the decision unit to obtain the user's n-dimensional weight coefficient, where n is an integer greater than 1, and the weight coefficient represents the user's consumption tendency. In one embodiment, the weight coefficient determination module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0087] The n-dimensional model building module 630 is used to obtain multiple products to be recommended to the user and n-dimensional product parameters of each product to be recommended, and to build an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficient. In one embodiment, the n-dimensional model building module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0088] The target product determination module 640 is used to determine the target product matched by the user from multiple recommended products according to the n-dimensional model. In one embodiment, the target product determination module 640 can be used to perform the operation S240 described above, which will not be described in detail here.

[0089] According to an embodiment of the present disclosure, there are multiple Metaverse platforms; collecting user interaction data includes: collecting partial interaction data of users on each Metaverse platform, wherein the partial interaction data is multimodal data; integrating multiple partial interaction data of multiple Metaverse platforms to obtain user interaction data.

[0090] According to an embodiment of the present disclosure, the interactive data is processed based on the decision unit to obtain the user's n-dimensional weight coefficient, including performing the following operations for any target dimension of the n dimensions: on the target dimension, multiple keywords and the weight components corresponding to each keyword are pre-set, wherein each keyword represents a decision basis for the user's consumption tendency in the target dimension, and the sum of the multiple weight components of the multiple keywords is 1; for each keyword in the multiple keywords, the keyword is used as a decision unit, and the positive frequency frontier and the negative frequency frontier of the decision unit are determined according to the interactive data; based on the positive frequency frontier, the negative frequency frontier and the weight component, the weight coefficient of the user in the target dimension is determined.

[0091] According to an embodiment of the present disclosure, a positive frequency frontier and a negative frequency frontier of a decision unit are determined based on interactive data, including: based on the interactive data, counting the number of occurrences of keywords in the interactive data within a preset time period; and determining the positive frequency frontier and the negative frequency frontier of the decision unit based on the number of occurrences and the preset time period.

[0092] According to an embodiment of the present disclosure, the weight coefficient of the user in the target dimension is determined based on the positive frequency frontier, the negative frequency frontier and the weight component, including: calculating the difference between the positive frequency frontier and the negative frequency frontier of each decision unit; weightedly summing up multiple differences of multiple decision units with the corresponding weight components, to obtain the weight coefficient of the user in the target dimension.

[0093] According to an embodiment of the present disclosure, the n-dimensional model displays n-dimensional product parameters and n-dimensional weight coefficients through n axes, wherein: the position of the user's n-dimensional weight coefficient on each axis is connected by a broken line to form a closed polygon, recorded as the first polygon; the position of the n-dimensional product parameter of each product to be recommended on each axis is connected by a broken line to form a closed polygon, recorded as the second polygon.

[0094] According to an embodiment of the present disclosure, a target product matching a user is determined from multiple recommended products based on an n-dimensional model, including: for any product to be recommended among multiple recommended products, calculating the degree of overlap between a first polygon and a second polygon of the product to be recommended; when the degree of overlap is greater than a preset degree of overlap threshold, determining the product to be recommended as the target product matching the user.

[0095] According to an embodiment of the present disclosure, any multiple modules of the interactive data acquisition module 610, the weight coefficient determination module 620, the n-dimensional model building module 630 and the target product determination module 640 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the interactive data acquisition module 610, the weight coefficient determination module 620, the n-dimensional model building module 630 and the target product determination module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the interactive data collection module 610, the weight coefficient determination module 620, the n-dimensional model building module 630 and the target product determination module 640 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0096] Figure 7 A block diagram of an electronic device suitable for implementing a product recommendation method based on the metaverse according to an embodiment of the present disclosure is schematically shown.

[0097] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0098] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0099] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0100] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0101] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0102] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the product recommendation method based on the metaverse provided by the embodiment of the present disclosure.

[0103] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0104] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0105] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0106] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0107] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0109] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A product recommendation method based on Metaverse, characterized in that: The method comprises: In response to a user interacting with the Metaverse platform, collecting interaction data of the user; Processing the interactive data based on the decision unit to obtain an n-dimensional weight coefficient of the user, wherein n is an integer greater than 1, and the weight coefficient represents the consumption tendency of the user; Acquire a plurality of products to be recommended to the user and n-dimensional product parameters of each product to be recommended, and establish an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficients; According to the n-dimensional model, a target product matched with the user is determined from the multiple recommended products.

2. The method according to claim 1, characterized in that There are multiple Metaverse platforms; The collecting of the user's interactive data comprises: Collecting part of the interaction data of the user on each of the Metaverse platforms, wherein the part of the interaction data is multimodal data; Integrate the multiple partial interaction data of the multiple Metaverse platforms to obtain the user's interaction data.

3. The method according to claim 1, characterized in that The decision-based unit processes the interaction data to obtain the n-dimensional weight coefficient of the user, including performing the following operations for any target dimension of the n dimensions: In the target dimension, a plurality of keywords and a weight component corresponding to each keyword are pre-set, wherein each keyword represents a decision basis for the consumption tendency of the user in the target dimension, and the sum of the plurality of weight components of the plurality of keywords is 1; For each keyword in the plurality of keywords, taking the keyword as a decision unit, determining a positive frequency frontier and a negative frequency frontier of the decision unit according to the interaction data; The weight coefficient of the user in the target dimension is determined according to the positive frequency frontier, the negative frequency frontier and the weight component.

4. The method according to claim 3, characterized in that The determining of the positive frequency front and the negative frequency front of the decision unit according to the interaction data comprises: According to the interactive data, within a preset time period, counting the number of occurrences of the keyword in the interactive data; According to the number of occurrences and the preset time period, a positive frequency front and a negative frequency front of the decision unit are determined.

5. The method according to claim 3, characterized in that: The step of determining the weight coefficient of the user in the target dimension according to the positive frequency frontier, the negative frequency frontier and the weight component includes: Calculating the difference between the positive frequency front and the negative frequency front of each decision unit; The plurality of difference values ​​of the plurality of decision units are weightedly summed with the corresponding weight components to obtain a weight coefficient of the user in the target dimension.

6. The method according to claim 1, characterized in that The n-dimensional model displays the n-dimensional product parameters and the n-dimensional weight coefficients through n axes, where: The positions of the user's n-dimensional weight coefficients on each axis are connected by a broken line to form a closed polygon, which is recorded as the first polygon; The positions of the n-dimensional product parameters of each of the products to be recommended on each axis are connected by a broken line to form a closed polygon, which is recorded as a second polygon.

7. The method according to claim 6, characterized in that Determining the target product matched by the user from the plurality of recommended products according to the n-dimensional model includes: For any product to be recommended among the plurality of recommended products, calculating the degree of overlap between the first polygon and the second polygon of the product to be recommended; When the degree of overlap is greater than a preset threshold of degree of overlap, the product to be recommended is determined as a target product matched by the user.

8. A product recommendation device based on Metaverse, characterized in that: The device comprises: An interactive data collection module, for collecting interactive data of a user in response to the user interacting with the Metaverse platform; A weight coefficient determination module, used to process the interaction data based on the decision unit to obtain an n-dimensional weight coefficient of the user, wherein n is an integer greater than 1, and the weight coefficient represents the consumption tendency of the user; An n-dimensional model building module, used for obtaining a plurality of products to be recommended to the user and n-dimensional product parameters of each product to be recommended, and building an n-dimensional model according to the n-dimensional product parameters and the n-dimensional weight coefficients; A target product determination module is used to determine a target product matched with the user from the multiple recommended products according to the n-dimensional model.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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