Shopping guide interaction method, device and equipment and computer storage medium

Abstract

The embodiment of the invention discloses a shopping guide interaction method and device, equipment and a computer storage medium. The method comprises the steps that target product selection operation input by a user based on a shopping guide interaction mode corresponding to a target product list page is received, and the target product selection operation carries a to-be-analyzed product identifier corresponding to a to-be-analyzed product selected by the user from the target product list page; in response to a target product selection operation, sending a to-be-analyzed product identifier to a server to enable the server to obtain corresponding to-be-analyzed product information based on the to-be-analyzed product identifier, and analyzing the to-be-analyzed product information by using a target large model to obtain a target analysis result; receiving a target analysis result sent by the server; and displaying the target shopping guide analysis page based on the target analysis result. The operation threshold and cognition cost of obtaining product information analysis by the user are effectively reduced.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a shopping guide interaction method, apparatus, device, and computer storage medium. Background Technology

[0002] Currently, the interactive mode of the Transformer-based Natural Language Processing model (GPT) is becoming increasingly common in daily life. GPT responds to users' questions and interacts accordingly. In the traditional GPT interactive mode, users must manually input questions to trigger the chatbot's dialogue. This method requires a high level of user participation and technical expertise, which to some extent inhibits users' enthusiasm for dialogue. Summary of the Invention

[0003] This application provides a shopping guide interaction method, apparatus, device, and computer storage medium. By adding a shopping guide interaction mode to the target product list page, users can obtain the desired target analysis results from a large target model simply by making selection and input operations. This effectively reduces the operational threshold and cognitive cost for users to obtain product information analysis through the target model, improving user experience. Furthermore, it provides a more intelligent and efficient service method in search and shopping guide scenarios, increasing users' enthusiasm for shopping guide interaction and further enhancing the core competitiveness of the transaction service platform. The above technical solution is as follows:

[0004] In a first aspect, embodiments of this application provide a shopping guide interaction method, including:

[0005] Receives a target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product to be analyzed selected by the user from the target product list page.

[0006] In response to the above target product selection operation, the above product identifier to be analyzed is sent to the server so that the server can obtain the corresponding product information to be analyzed based on the above product identifier, and use the target big model to analyze the above product information to obtain the corresponding target analysis results.

[0007] Receive the target analysis results sent by the aforementioned server;

[0008] Based on the above target analysis results, the corresponding target shopping guide analysis page will be displayed.

[0009] In one possible implementation, the above-mentioned target product selection operation also carries the target user identifier corresponding to the above-mentioned user;

[0010] In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information based on the identifier and analyze the product information using the target large model to obtain the corresponding target analysis results, including:

[0011] In response to the above target product selection operation, the above product identifier to be analyzed and the above target user identifier are sent to the server so that the server can obtain the corresponding product information to be analyzed based on the above product identifier and the corresponding target information based on the above target user identifier, and use the target big model to perform comprehensive analysis on the above product information to be analyzed and the above target information to obtain the corresponding target analysis results.

[0012] The aforementioned target information includes target user information and / or historical order information corresponding to the aforementioned users; the aforementioned target analysis results include at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the aforementioned users corresponding to the aforementioned products to be analyzed.

[0013] In one possible implementation, the target product selection operation carries the corresponding product identifiers of the multiple products to be analyzed selected by the user from the target product list page.

[0014] In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information based on the identifier and analyze the product information using the target large model to obtain the corresponding target analysis results, including:

[0015] In response to the above target product selection operation, the corresponding product identifiers of the multiple products to be analyzed are sent to the server, so that the server can obtain the product information corresponding to the multiple products to be analyzed based on the corresponding product identifiers of the multiple products to be analyzed, and use the target big model to compare and analyze the product information corresponding to the multiple products to be analyzed to obtain the corresponding target analysis results.

[0016] The above-mentioned target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information of the above-mentioned multiple products to be analyzed.

[0017] In one possible implementation, before receiving the user's target product selection operation based on the shopping guide interaction mode corresponding to the target product list page, the method further includes:

[0018] Receive the first trigger operation for the smart entry point in the above target product list page;

[0019] In response to the first triggering operation mentioned above, the corresponding shopping guide interaction mode is entered on the target product list page.

[0020] In one possible implementation, before receiving the first trigger operation for the smart entry point in the target product list page, the method further includes:

[0021] Receive a launch operation from the target application for the aforementioned target product list page; the launch operation carries the target page identifier corresponding to the aforementioned target product list page;

[0022] Based on the above target page identifier and the preset smart page identifier set corresponding to the above target application, determine whether the above target product list page has the above smart entry;

[0023] If so, in response to the above startup operation, the target product list page carrying the above smart entry point will be displayed.

[0024] In one possible implementation, the aforementioned smart entry point is used to initiate the shopping guide interaction mode corresponding to the target product list page. The display form of the aforementioned smart entry point on the target product list page includes at least one of the following: a control, a movable or fixed floating entry point.

[0025] In one possible implementation, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0026] In one possible implementation, the above-mentioned target-oriented shopping guide analysis page, based on the target analysis results, includes:

[0027] Based on the above target analysis results, the corresponding images and text information to be displayed are determined;

[0028] Based on the aforementioned images and text information to be displayed, the corresponding target shopping guide analysis page is rendered and displayed.

[0029] In one possible implementation, the above response to the target product selection operation, sending the identifier of the product to be analyzed to the server, includes:

[0030] In response to the above target product selection operation, the display elements corresponding to the product to be analyzed and the analysis start control are displayed in a pop-up window on the above target product list page; the display elements include the product image and / or product name corresponding to the product to be analyzed;

[0031] Upon receiving the second trigger operation for the aforementioned analysis initiation control, the identifier of the product to be analyzed is sent to the server.

[0032] In one possible implementation, the number of products to be analyzed varies, corresponding to different types of analysis initiation controls; or

[0033] When the number of products to be analyzed is 1, the analysis start control is the recommended analysis start control or the detailed analysis start control corresponding to the product to be analyzed; when the number of products to be analyzed is greater than 1, the analysis start control is the comparative analysis start control corresponding to multiple products to be analyzed.

[0034] In one possible implementation, after receiving the user's target product selection operation based on the shopping guide interaction mode corresponding to the target product list page, the method further includes:

[0035] Determine whether any other operations from the user are received within a preset time period after receiving the above target product selection operation;

[0036] In response to the above target product selection operation, the above-mentioned product identifier to be analyzed is sent to the server, including:

[0037] If not, in response to the above target product selection operation, the above product identifier to be analyzed will be sent to the server.

[0038] In one possible implementation, the target product selection operation includes at least one of the following operations on the product to be analyzed: circle selection, click, long press, and checkmark.

[0039] Secondly, this application provides another shopping guide interaction method, which is applied to the server side and includes:

[0040] Receive the identifier of the product to be analyzed sent by the user terminal; the identifier of the product to be analyzed is the identifier of the product selected by the user from the target product list page based on the shopping guide interaction mode corresponding to the target product list page;

[0041] Based on the above-mentioned product identifier, obtain the corresponding product information to be analyzed, and use the target large model to analyze the above-mentioned product information to obtain the corresponding target analysis results;

[0042] The above target analysis results are sent to the above user terminal so that the user terminal can display the corresponding target shopping guide analysis page based on the above target analysis results.

[0043] In one possible implementation, before analyzing the product information to be analyzed using the target large model to obtain the corresponding target analysis results, the method further includes:

[0044] Receive the target user identifier corresponding to the user sent by the aforementioned user terminal;

[0045] Obtain the corresponding target information based on the aforementioned target user identifier;

[0046] The above-mentioned target analysis results obtained by analyzing the product information to be analyzed using the target large model include:

[0047] By using the target big model to comprehensively analyze the above-mentioned product information and target information, the corresponding target analysis results are obtained.

[0048] The aforementioned target information includes target user information and / or historical order information corresponding to the aforementioned users; the aforementioned target analysis results include at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the aforementioned users corresponding to the aforementioned products to be analyzed.

[0049] In one possible implementation, the product identifier to be analyzed sent by the user terminal includes:

[0050] Receive the identifiers of the products to be analyzed for each of the multiple products to be analyzed sent by the user terminal;

[0051] The above-mentioned product information is obtained based on the product identifier to be analyzed, and the target large model is used to analyze the product information to obtain the corresponding target analysis results, including:

[0052] Based on the product identifiers corresponding to each of the above-mentioned products to be analyzed, obtain the product information corresponding to each of the above-mentioned products to be analyzed, and use the target large model to compare and analyze the product information corresponding to each of the above-mentioned products to be analyzed to obtain the corresponding target analysis results.

[0053] The above-mentioned target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information of the above-mentioned multiple products to be analyzed.

[0054] In one possible implementation, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0055] Thirdly, embodiments of this application provide a shopping guide interaction device, which includes:

[0056] The first receiving module is used to receive the target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the product identifier to be analyzed corresponding to the product to be analyzed selected by the user from the target product list page.

[0057] The first sending module is used to respond to the above target product selection operation by sending the above product identifier to the server, so that the server can obtain the corresponding product information based on the above product identifier and use the target big model to analyze the above product information to obtain the corresponding target analysis result.

[0058] The second receiving module is used to receive the target analysis results sent by the server.

[0059] The first display module is used to display the corresponding target shopping guide analysis page based on the above target analysis results.

[0060] Fourthly, embodiments of this application provide another shopping guide interaction device, wherein the above method is applied to the server side, and the device includes:

[0061] The fifth receiving module is used to receive the identifier of the product to be analyzed sent by the user terminal; the identifier of the product to be analyzed is the identifier of the product to be analyzed selected by the user from the target product list page based on the shopping guide interaction mode corresponding to the target product list page.

[0062] The first acquisition module is used to acquire the corresponding product information to be analyzed based on the above-mentioned product identifier;

[0063] The analysis module is used to analyze the above-mentioned product information using the target large model to obtain the corresponding target analysis results;

[0064] The second sending module is used to send the above target analysis results to the above user terminal, so that the above user terminal can display the corresponding target shopping guide analysis page based on the above target analysis results.

[0065] Fifthly, embodiments of this application provide an electronic device, including: a processor and a memory;

[0066] The processor is connected to the memory.

[0067] The aforementioned memory is used to store executable program code;

[0068] The processor reads the executable program code stored in the memory to run the program corresponding to the executable program code, so as to execute the method provided by the first aspect or any possible implementation of the first aspect or the second aspect or any possible implementation of the embodiments of this application.

[0069] Fourthly, embodiments of this application provide a computer storage medium storing a plurality of instructions, which are adapted to be loaded by a processor and executed by the method steps provided by the first aspect or any possible implementation of the first aspect or the second aspect or any possible implementation of the second aspect of this application.

[0070] In one or more embodiments of this application, a user inputs a target product selection operation based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product selected by the user from the target product list page; in response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server obtains the corresponding product information to be analyzed based on the identifier of the product to be analyzed, and uses the target big model to analyze the product information to be analyzed to obtain the corresponding target analysis result; the target analysis result sent by the server is received; and the corresponding target shopping guide analysis page is displayed based on the target analysis result. Thus, based on the shopping guide interaction mode corresponding to the target product list page, users can realize the analysis of the product information to be analyzed through simple selection input operations and obtain the target analysis result fed back by the target big model, which effectively reduces the operational threshold and cognitive cost for users to obtain product information analysis through the target big model. Furthermore, by integrating the shopping guide interaction mode into the target product list page, not only is the user experience improved, but more intelligent and efficient service means can also be provided in search and shopping guide scenarios, increasing the user's enthusiasm for shopping guide interaction and further enhancing the core competitiveness of the transaction service platform. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a diagram illustrating the promotion and interactive effects of related technologies in shopping guides;

[0073] Figure 2 A schematic diagram of the architecture of a shopping guide interaction system provided for an exemplary embodiment of this application;

[0074] Figure 3 A flowchart illustrating a shopping guide interaction method provided as an exemplary embodiment of this application;

[0075] Figures 4A-4D A schematic diagram of a target product list page provided for an exemplary embodiment of this application;

[0076] Figure 5 A schematic diagram illustrating the implementation process of a shopping guide interaction provided for an exemplary embodiment of this application;

[0077] Figure 6 A schematic diagram illustrating another implementation process of a shopping guide interaction provided as an exemplary embodiment of this application;

[0078] Figures 7A-7B A schematic diagram of a target shopping guide analysis page provided for an exemplary embodiment of this application;

[0079] Figure 8 A schematic diagram illustrating another implementation process of a shopping guide interaction provided as an exemplary embodiment of this application;

[0080] Figure 9 A schematic diagram of the structure of a shopping guide interaction device provided for an exemplary embodiment of this application;

[0081] Figure 10 A schematic diagram of the structure of another shopping guide interaction device provided as an exemplary embodiment of this application;

[0082] Figure 11 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application. Detailed Implementation

[0083] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0084] The terms "first," "second," "third," etc., used in this specification, claims, and the foregoing drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0085] In the relevant GPT interaction model, users must trigger the dialogue by manually entering questions. For example, when a user buys grapes in the target application, if the user wants to know the difference between Green Rose and Sunshine Rose grapes, they can only do so in other applications through the GPT model (e.g., Figure 1 To obtain the corresponding feedback results from the GPT model, input the relevant question text, such as "the difference between Green Rose grapes and Sunshine Rose grapes".

[0086] However, this interaction method not only requires users to exit the current product display page or the target application of the currently purchased product and open other applications that have deployed the GPT model for interaction, but also requires users to manually enter the questions they want to interact with the GPT model. This places high demands on user participation and technical skills, which to some extent inhibits users' enthusiasm for dialogue.

[0087] Please refer to the following. Figure 2 , Figure 2 This is a schematic diagram of the architecture of a shopping guide interaction system provided for an exemplary embodiment of this application. Figure 2 As shown, the shopping guide interaction system may include: a user terminal 210 and a server 220. Wherein:

[0088] User terminal 210 includes terminals corresponding to one or more users (buyers / consumers). A corresponding buyer's version application (target application) can be installed on user terminal 210, where users can register and log in to their respective accounts. Users can purchase goods in a merchant's store or purchase funds or insurance products sold online by fund sales institutions through user terminal 210 using the aforementioned buyer's version application. User terminal 210 can receive target product selection operations input by the user based on the shopping guide interaction mode corresponding to the target product list page. The target product selection operation carries the identifier of the product to be analyzed corresponding to the product selected by the user from the target product list page. Then, in response to the target product selection operation, the identifier of the product to be analyzed is sent to server 220 so that server 220 can obtain the corresponding product information based on the identifier and analyze the product information using the target large model to obtain the target analysis result. Finally, the target analysis result sent by server 220 is received; and the target shopping guide analysis page is displayed based on the target analysis result.

[0089] Understandably, the user terminal 210 may be a mobile phone, tablet computer, desktop computer, laptop computer, notebook computer, ultra-mobile personal computer (UMPC), handheld computer, netbook, personal digital assistant (PDA), wearable electronic device, virtual reality device, etc., and the embodiments of this application do not limit it.

[0090] Server 220 can be a server on user terminal 210 that can install a buyer's version application (target application). It provides services such as opening online stores and selling goods to various merchants, or selling funds or insurance products online to fund sales institutions, while also providing users with online ordering and purchasing services. In this embodiment, server 220 can also receive the identifier of the product to be analyzed sent by user terminal 210, obtain the corresponding product information based on the identifier, and analyze the product information using a target large model to obtain the target analysis result. Finally, server 220 will send the aforementioned target analysis result back to user terminal 210.

[0091] Understandably, the server 220 can be a hardware server, a virtual server, a cloud server, etc., and this application embodiment does not limit it.

[0092] The network can be a medium that provides a communication link between the server 220 and the user terminal 210, or it can be the Internet, which includes network equipment and transmission media, and is not limited thereto. The transmission media can be a wired link, such as, but not limited to, coaxial cable, fiber optic cable, and Digital Subscriber Line (DSL), etc., or a wireless link, such as, but not limited to, Wi-Fi, Bluetooth, and mobile device networks, etc.

[0093] Understandably, Figure 2 The number of user terminals 210 and server terminals 220 in the illustrated shopping guide interaction system is merely an example. In a specific implementation, the shopping guide interaction system can contain any number of user (buyer) terminals 210 and server terminals 220, and this application embodiment does not impose a specific limitation on this. For example, but not limited to, user terminals 210 can be a user terminal cluster composed of multiple user terminals, and server terminals 220 can be a server cluster composed of multiple servers.

[0094] To address the issues raised in the aforementioned technologies, such as the requirement for users to manually input questions to trigger dialogue in the chatbot (GPT model), which places high demands on user participation and technical skills and to some extent inhibits user engagement, the following section will discuss... Figures 1-2 This application describes a shopping guide interaction method provided by an embodiment. Please refer to the following for details. Figure 3 This is a flowchart illustrating a shopping guide interaction method provided in an exemplary embodiment of this application. Figure 3 As shown, this shopping guide interaction method includes the following steps:

[0095] S301: Receives the user's target product selection operation based on the shopping guide interaction mode corresponding to the target product list page.

[0096] Specifically, the aforementioned target product selection operation carries the identifier of the product to be analyzed corresponding to the product selected by the user from the target product list page. This identifier is used to characterize the identity of the product to be analyzed and may include, but is not limited to, the product's corresponding number, product tag, etc.

[0097] Understandably, in order to improve the differentiation analysis of products for different sales channels (such as stores, institutions, etc.) during the shopping guide interaction process, and to improve the practicality of using the target large model for shopping guide analysis during the shopping guide interaction process, for the same product or the same product, the corresponding product identifier will be different depending on the corresponding sales channel (such as stores, institutions, etc.).

[0098] Understandably, the number of product identifiers to be analyzed carried by the target product selection operation may include one or more, and this application embodiment does not limit this. When the target application is a commodity transaction application, the product to be analyzed may be a store to be analyzed, or a commodity to be analyzed, etc.; when the target application is a financial transaction application, the product to be analyzed may be a financial institution to be analyzed, or a fund or insurance to be analyzed, etc.; when the target application is a housing transaction application, the product to be analyzed may be a real estate agency to be analyzed, or a house to be analyzed that is being sold or rented, etc., and this application embodiment does not limit this.

[0099] Furthermore, the aforementioned target product selection operation may include, but is not limited to, at least one of the following: selection of the product to be analyzed, clicking, long-pressing, and checking. That is, the embodiments of this application can quickly elicit corresponding feedback from the target model through at least one interactive input modality such as selection, clicking, long-pressing, and checking. Compared to related technologies where manual input of questions is required to trigger dialogue with the target model, this effectively reduces the operational threshold and cognitive cost for users to obtain product comparison information, and improves the efficiency and enthusiasm of users in interactive shopping.

[0100] Optionally, before receiving the user's target product selection operation based on the shopping guide interaction mode corresponding to the target product list page in S301 above, the shopping guide interaction method includes the following S300a and S300b:

[0101] S300a: Receives the first trigger operation for the smart entry point in the target product list page.

[0102] Specifically, the first triggering operation may be, but is not limited to, the user terminal 210 receiving an operation from the user clicking, long-pressing, or swiping the smart entry point on the target product list page of the target application. The aforementioned smart entry point is used to activate the corresponding shopping guide interaction mode on the target product list page, and may be displayed on the target product list page in various forms, including, but not limited to, controls, movable or fixed-position floating entry points; this embodiment of the application does not limit this.

[0103] Specifically, the target product list page displays product information for at least one product, including but not limited to product name, product image, and product price. After a user opens the target application and enters the target product list page, if the user wants to quickly obtain the target analysis results for a specific product, they do not need to click to view the product's details page. Instead, they can directly input the corresponding first trigger operation through the smart entry point on the target product list page to activate the corresponding shopping guide interaction mode.

[0104] Understandably, when the target application is a commodity transaction application, the product information of at least one product displayed on the target product list page can be the information of at least one store or commodity; when the target application is a financial transaction application, the product information of at least one product displayed on the target product list page can be the information of at least one financial institution, fund, or insurance company; when the target application is a housing transaction application, the product information of at least one product displayed on the target product list page can be the information of at least one intermediary agency, house, or homeowner.

[0105] S300b: In response to the first trigger operation, enter the corresponding shopping guide interaction mode on the target product list page.

[0106] Specifically, after receiving a first trigger operation for the smart entry point on the target product list page, the user terminal can respond to the first trigger operation by controlling the target product list page to enter the corresponding shopping guide interaction mode. This can be achieved, for example, but not limited to, displaying corresponding shopping guide interaction prompts and / or shopping guide interaction tools on the target product list page. This allows users to input simple target product selection operations through the shopping guide interaction tools according to the corresponding shopping guide interaction prompts, so as to quickly obtain the information that users want to analyze and efficiently and easily achieve shopping guide interaction with the target large model.

[0107] For example, when a user opens the target application and enters such as Figure 4A When a user is on a target product list page 400 with a smart entry point 411, and wants to quickly obtain the target analysis results for a specific product or store on the target product list page 400, or the comparative analysis results for multiple products or stores, they can trigger the smart entry point 411 through a first trigger operation such as clicking, long-pressing, or swiping. This will allow the target application to enter the shopping guide interaction mode corresponding to the target product list page 400, for example, but not limited to... Figure 4B As shown, when entering the shopping guide interaction mode corresponding to the target product list page 400, the target product list page 400 will display the corresponding shopping guide interaction prompt information 421 and shopping guide interaction tool 422 to inform the user that the target product list page 400 has entered the shopping guide interaction mode. This mode can, but is not limited to, allow the user to perform simple shopping guide interaction operations through the shopping guide interaction tool 422 according to the corresponding shopping guide interaction prompt information 421, thereby efficiently and easily realizing the shopping guide interaction between the user and the target product model and improving the user experience.

[0108] Please continue to refer to the following. Figure 3 ,like Figure 3 As shown, after receiving the user's target product selection operation input based on the shopping guide interaction mode corresponding to the target product list page in S301 above, the shopping guide interaction method further includes:

[0109] S302: In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server so that the server can obtain the corresponding product information based on the product identifier and use the target large model to analyze the product information to obtain the corresponding target analysis results.

[0110] Specifically, the product information to be analyzed may include, but is not limited to, information such as the product's price, reviews, description, features, and historical sales data. After receiving a user's target product selection operation based on the shopping guide interaction mode, the user terminal can respond by sending the product identifier to the server. The server then retrieves the corresponding product information from the database based on this identifier and inputs it into the target large model. The target large model then analyzes the product information and outputs the corresponding target analysis results. The target large model may include, but is not limited to, a Transformer-based natural language processing model (Generative Pre-trained Transformer, GPT), a large language model, a multimodal model, or a large recommendation system model.

[0111] Understandably, in order to make the answers from the target big model more professional and more in line with industry knowledge and logic, the aforementioned target big model can be, but is not limited to, learned in advance based on the knowledge graph of the industry in which the target application is located.

[0112] Optionally, the above target analysis results are obtained by the target big model analyzing the product information to be analyzed based on preset prompts; the preset prompts include preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0113] For example, when the target application is a food and beverage transaction application, the preset role setting information may include, but is not limited to: you are a food and beverage recommendation consultant, focusing on providing users with personalized food suggestions based on their food ordering behavior, especially adept at providing directional guidance when users have multiple differences in their food ordering choices; the preset language personality information may include, but is not limited to: you communicate with users in an approachable language and style, avoiding the use of complex food and beverage jargon, striving to ensure that every user can easily understand and adopt your suggestions; the user behavior summary and comparison analysis demand information may include, but is not limited to, the user's corresponding store and product preference demand information and / or food ordering behavior pattern comparison demand information, the aforementioned store and product preference demand information may include, but is not limited to, the user's corresponding category preference demand, store loyalty demand, dish comparison demand, and taste preference demand, etc., the aforementioned food ordering behavior pattern comparison demand information may include, but is not limited to, the user's corresponding delivery time sensitivity demand, regular ordering time demand, etc., etc. The aforementioned category preference demand refers to: the food and beverage categories that users frequently choose during the statistical period, such as Chinese food, Western food, fast food, or light meals, etc. The above-mentioned store loyalty requirement is to assess a user's store loyalty based on the frequency of their orders at a particular store. The above-mentioned dish comparison requirement is to reveal a user's preference for specific foods based on the dishes they have repeatedly purchased. The above-mentioned taste preference requirement is to extract mainstream taste characteristics from the user's historical transaction menus, such as spicy, sweet, salty, or other special seasonings, as the user's taste preferences. The above-mentioned delivery time sensitivity requirement is to understand the user's requirements for food delivery speed by calculating the user's historical average delivery time. The above-mentioned regular ordering time requirement is to determine the user's regular demand for breakfast, lunch, dinner, and late-night snacks based on the distribution of the user's historical ordering times. The above-mentioned guidance and suggestion requirements may include, but are not limited to, generating corresponding guidance suggestions based on the user's needs and the reasons for those suggestions.

[0114] Optionally, the process of sending the identifier of the product to be analyzed to the server in response to the target product selection operation may include: first, displaying the display elements corresponding to the product to be analyzed and the analysis start control in the form of a pop-up window on the target product list page in response to the target product selection operation; and then, after receiving the second trigger operation for the analysis start control, sending the identifier of the product to be analyzed to the server. That is, in this embodiment, the identifier of the product to be analyzed is only sent to the server after the user triggers the analysis start control, so as to trigger the interaction between the user and the target large model, thereby avoiding the problem that the interaction between the user and the target large model is automatically triggered before the user has selected all the products to be analyzed, which affects the user experience and improves the effectiveness of the shopping guide interaction. The display elements may include, but are not limited to, the product image and / or product name corresponding to the product to be analyzed.

[0115] Furthermore, the types of analysis launch controls displayed will differ depending on the number of products to be analyzed. For example... Figure 4C As shown, when a user selects only one product to be analyzed through the selection operation 431, the target product list page 400 will display the corresponding display element 432 and the corresponding recommended analysis or detailed analysis start control 434 for that product in the form of a pop-up window. The user can, but is not limited to, trigger the detailed analysis start control 434 by clicking (the second trigger operation) to send the product identifier of the selected product to be analyzed in the selection operation 431 to the server, so that the server can return the corresponding target analysis results using the target large model. Furthermore, after the user selects a product to be analyzed through the selection operation 431, another type of shopping guide interaction prompt information 433 can be displayed based on the shopping guide interaction mode of the target product list page 400, but is not limited to, prompting the user that they can select other products for comparative analysis. This allows the user to intuitively understand the function of the shopping guide interaction mode, providing users with more diversified product analysis services, thereby enhancing the core competitiveness of the target application. For example... Figure 4D As shown, if the user adds another product to be analyzed through the selection operation 441, the target product list page 400 will also display the corresponding display element 442 of the product to be analyzed, as well as the comparison analysis start control 443 corresponding to the two products selected by the selection operations 431 and 441, in the form of a pop-up window. The user can, but is not limited to, trigger the comparison analysis start control 443 by clicking (the second trigger operation) to send the product identifiers of the two products to be analyzed selected by the selection operations 431 and 441 to the server, so that the server can return the corresponding comparison analysis results (target analysis results) using the target large model.

[0116] Optionally, after receiving the user's input of the target product selection operation, in order to avoid the problem that the shopping guide interaction cannot start because the user forgets to input the second trigger operation for the analysis start control, and to reduce the complexity of the user's shopping guide interaction operation, if no other operation is received from the user within a preset time (e.g., but not limited to 5 seconds, 6 seconds, etc.) after the user inputs the target product selection operation, the identifier of the product to be analyzed carried by the target product selection operation can be directly sent to the server, thereby improving the flexibility of the shopping guide interaction and further reducing the operation threshold of the shopping guide interaction.

[0117] Optionally, the aforementioned target product selection operation also carries a target user identifier corresponding to the user. This target user identifier may include, but is not limited to, the user's identity identifier, the account identifier corresponding to the user's logged-in account in the target application, etc. For example... Figure 5As shown, after receiving a user's target product selection operation based on the shopping guide interaction mode, the user terminal can respond to the target product selection operation by sending the identifier of the product to be analyzed and the identifier of the target user to the server. The server can then obtain the corresponding product information based on the identifier of the product to be analyzed and the corresponding target information based on the identifier of the target user. The server can then use the target big model to comprehensively analyze the product information and target information to obtain the corresponding target analysis results. This allows the target big model to provide targeted target analysis results based on each user's information, making the returned target analysis results more relevant to the user's situation, meeting the user's needs, and improving the intelligence and differentiation of the shopping guide interaction.

[0118] Furthermore, the aforementioned target information may include, but is not limited to, the target user information and / or historical order information corresponding to the user. The aforementioned target user information may include, but is not limited to, the user's age, occupation, place of origin, etc., and the aforementioned historical order information may include, but is not limited to, the transaction time, transaction store, transaction product, transaction price, etc., within the user's historical time period (e.g., but not limited to the past year, month, etc.). The aforementioned target analysis results may include, but are not limited to, at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and user recommendation evaluation information corresponding to the product to be analyzed.

[0119] Optionally, the above target product selection operation carries the identifiers of the multiple products to be analyzed selected by the user from the target product list page. For example... Figure 6 As shown, after receiving a user's target product selection operation input based on the shopping guide interaction mode, the user terminal can respond to this operation by sending the corresponding product identifiers of multiple products to be analyzed to the server. The server then obtains the product information corresponding to each of the multiple products based on these identifiers and uses a target big model to compare and analyze this information to obtain the corresponding target analysis results. This shopping guide interaction mode allows users to efficiently interact with the target big model through simple selection operations such as circle-and-click, obtaining comparative analysis results for multiple products, effectively reducing the operational threshold and cognitive cost for users to obtain product comparison information. The aforementioned target analysis results may include, but are not limited to, at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparative analysis summary information for multiple products.

[0120] Understandably, in order to enable the target big model to provide targeted target analysis results based on each user's information, and to make the returned target analysis results more relevant to the user's situation, regardless of whether the user selects one or more products to be analyzed, the user terminal will send the target user identifier corresponding to the user and the product identifiers of all the products selected by the user to the server. This allows the server to obtain the corresponding target information and product information to be analyzed, and then input them into the target big model for analysis, thereby ensuring the intelligence and differentiation of the shopping guide interaction.

[0121] Please continue to refer to the following. Figure 3 ,like Figure 3 As shown, in S302 above: In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information to be analyzed based on the product identifier, and then use the target large model to analyze the product information to obtain the corresponding target analysis result. After that, the shopping guide interaction method also includes:

[0122] S303: Receive the target analysis results sent by the server.

[0123] Specifically, after the server uses the target model to analyze the product information to obtain the corresponding target analysis results, it can directly return these results to the user terminal. In other words, the user terminal can receive the aforementioned target analysis results sent by the server via the network.

[0124] Optionally, to ensure the simplicity and displayability of the target analysis results returned by the server, after obtaining the target analysis results output by the target big model, the server can first extract key information from the target analysis structure according to the preset data structure, that is, complete the differentiation refinement and summarization, and then encapsulate the extracted key information and return it to the user terminal, so that the user terminal can directly render and display it in the corresponding display position on the target shopping guide analysis page according to the encapsulated target analysis structure, thereby improving the display effect and display efficiency of the target analysis results.

[0125] S304: Display the corresponding target shopping guide analysis page based on the target analysis results.

[0126] Specifically, after receiving the target analysis results returned by the server, the user terminal can, but is not limited to, directly enter the target shopping guide analysis page and display the aforementioned target analysis results on the target shopping guide analysis page.

[0127] Optionally, after receiving the target analysis results returned by the server, the user terminal can first determine the corresponding image and text information to be displayed based on the target analysis results, and then render and display the corresponding target shopping guide analysis page based on the image and text information to be displayed. By displaying the target analysis results in an easy-to-understand graphic and textual way, it ensures that users can quickly grasp the key information in the target analysis results, thereby making a more suitable purchase decision for themselves.

[0128] Optionally, after receiving the target analysis results returned by the server, the user terminal can also, but is not limited to, display the target analysis results directly on the target product list page in the form of a pop-up window, thereby eliminating the need for page redirection and allowing users to view the target analysis results more efficiently.

[0129] For example, if a user selects only one product to be analyzed from the target product list page based on the shopping guide interaction mode, for example... Figure 4C As shown, after the user triggers the detail analysis startup control 434 corresponding to the product to be analyzed and receives the corresponding target analysis result returned by the server, the results can be displayed, but are not limited to, based on the target analysis result, as shown below. Figure 7A The target shopping guide analysis page 700 shown can, for example, but not limited to, display the corresponding target analysis results 710 in a dialog form. This allows users to directly view information such as the price, delivery distance, taste, and merchant service (reviews) of the product to be analyzed without having to click into the product details page of the product they selected. They can also know the recommendation evaluation information (such as a recommendation rate of 80% and the recommendation reason "Store A is 95m away from you, which is close, but the price is slightly higher") for the target model.

[0130] For example, if a user selects multiple products to be analyzed on the target product list page based on the shopping guide interaction mode, such as Figure 4D As shown, after the user triggers the comparative analysis start control 443 corresponding to the product to be analyzed and receives the corresponding target analysis result returned by the server, the results can be displayed, but are not limited to, based on the target analysis result. Figure 7B The target shopping guide analysis page 700 shown can, for example but not limited to, display the corresponding target analysis results 720 in the form of a dialogue. This allows users to interact with the target model through simple selection operations without having to manually input product information for multiple products to be analyzed. Users can then learn about the comparison information between multiple products, such as price advantage analysis, taste characteristics comparison, and merchant service evaluation.

[0131] In this embodiment, a first trigger operation is received for the smart entry point on the target product list page; in response to the first trigger operation, the user enters the shopping guide interaction mode corresponding to the target product list page; the user receives a target product selection operation input by the user based on the shopping guide interaction mode; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product selected by the user from the target product list page; in response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server obtains the corresponding product information based on the identifier of the product to be analyzed, and uses the target big model to analyze the product information to obtain the corresponding target analysis result; the target analysis result sent by the server is received; and the corresponding target shopping guide analysis page is displayed based on the target analysis result. This effectively reduces the operational threshold and cognitive cost for users to obtain product information analysis through the target big model by setting a smart entry point on the target product list page, allowing users to quickly trigger and enter the shopping guide interaction mode corresponding to the target product list page, and enabling users to analyze the product information through simple selection and input operations based on the shopping guide interaction mode, and obtain the target analysis result fed back by the target big model. Furthermore, by integrating a shopping guide interaction mode into the target product list page, not only is the user experience improved, but more intelligent and efficient service methods can also be provided in search and shopping guide scenarios, increasing users' enthusiasm for shopping guide interaction and further enhancing the core competitiveness of the transaction service platform.

[0132] In some possible embodiments, such as Figure 8 As shown, the server can pre-mark pages in the target application that have smart entry points to obtain a corresponding preset smart page identifier set, and send the preset smart page identifier set to the user terminal with the target application installed. The preset smart page identifier set includes the page identifiers corresponding to all pages in the target application that have smart entry points. Before receiving the first trigger operation for the smart entry point in the target product list page in S301, the above-mentioned shopping guide interaction method may further include: receiving a launch operation for the target product list page in the target application, the launch operation carrying the target page identifier corresponding to the target product list page; then, determining whether the target product list page has a smart entry point based on the target page identifier and the preset smart page identifier set corresponding to the target application, for example, but not limited to determining whether the target page identifier exists in the preset smart page identifier set; if yes, then in response to the launch operation, displaying the target product list page with a smart entry point. If no, it means that the target product list page launched by the user does not have a smart entry point, and then directly displaying the target product list page without a smart entry point.

[0133] To better understand the shopping guide interaction method provided in the above embodiments of this application Figure 9An exemplary schematic diagram of a shopping guide interaction device provided in an embodiment of this application is shown. Figure 9 As shown, the interactive shopping guide device 900 includes: a first receiving module 910, a first sending module 920, a second receiving module 930, and a first display module 940. Wherein:

[0134] The first receiving module 910 is used to receive the target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product to be analyzed selected by the user from the target product list page.

[0135] The first sending module 920 is used to respond to the above target product selection operation by sending the above product identifier to the server, so that the server can obtain the corresponding product information based on the above product identifier and use the target big model to analyze the above product information to obtain the corresponding target analysis result.

[0136] The second receiving module 930 is used to receive the target analysis results sent by the server.

[0137] The first display module 940 is used to display the corresponding target shopping guide analysis page based on the above target analysis results.

[0138] In one possible implementation, the above-mentioned target product selection operation also carries the target user identifier corresponding to the above-mentioned user;

[0139] The first sending module 920 is specifically used to: in response to the target product selection operation, send the product identifier to be analyzed and the target user identifier to the server, so that the server can obtain the corresponding product information to be analyzed based on the product identifier and the corresponding target information based on the target user identifier, and use the target big model to perform comprehensive analysis on the product information to be analyzed and the target information to obtain the corresponding target analysis result.

[0140] The aforementioned target information includes target user information and / or historical order information corresponding to the aforementioned users; the aforementioned target analysis results include at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the aforementioned users corresponding to the aforementioned products to be analyzed.

[0141] In one possible implementation, the target product selection operation carries the corresponding product identifiers of the multiple products to be analyzed selected by the user from the target product list page.

[0142] The first sending module 920 is specifically used to: in response to the target product selection operation, send the corresponding product identifiers of the plurality of products to be analyzed to the server, so that the server can obtain the corresponding product information of the plurality of products to be analyzed based on the corresponding product identifiers of the plurality of products to be analyzed, and use the target big model to compare and analyze the corresponding product information of the plurality of products to be analyzed to obtain the corresponding target analysis results.

[0143] The above-mentioned target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information of the above-mentioned multiple products to be analyzed.

[0144] In one possible implementation, the aforementioned shopping guide interaction device 900 further includes:

[0145] The third receiving module is used to receive the first trigger operation for the smart entry in the target product list page;

[0146] The shopping guide interaction module is used to respond to the first trigger operation mentioned above and enter the shopping guide interaction mode corresponding to the target product list page mentioned above.

[0147] In one possible implementation, the aforementioned shopping guide interaction device 900 further includes:

[0148] The fourth receiving module is used to receive the launch operation of the target application for the target product list page; the launch operation carries the target page identifier corresponding to the target product list page.

[0149] The judgment module is used to determine whether the target product list page has the above-mentioned smart entry based on the above-mentioned target page identifier and the preset smart page identifier set corresponding to the above-mentioned target application.

[0150] The second display module is used to display the target product list page carrying the smart entry point in response to the above-mentioned startup operation if the above-mentioned startup operation is true.

[0151] In one possible implementation, the aforementioned smart entry point is used to initiate the shopping guide interaction mode corresponding to the target product list page. The display form of the aforementioned smart entry point on the target product list page includes at least one of the following: a control, a movable or fixed floating entry point.

[0152] In one possible implementation, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0153] In one possible implementation, the first display module 940 includes:

[0154] The determining unit is used to determine the corresponding image to be displayed and the text information to be displayed based on the above target analysis results.

[0155] The display unit is used to render and display the corresponding target shopping guide analysis page based on the above-mentioned image to be displayed and the above-mentioned text information to be displayed.

[0156] In one possible implementation, the first sending module 920 is specifically used to: in response to the target product selection operation, display the display elements corresponding to the product to be analyzed and the analysis start control in the form of a pop-up window on the target product list page; the display elements include the product image and / or product name corresponding to the product to be analyzed; and after receiving the second trigger operation for the analysis start control, send the identifier of the product to be analyzed to the server.

[0157] In one possible implementation, the number of products to be analyzed varies, corresponding to different types of analysis launch controls; or, when the number of products to be analyzed is 1, the analysis launch control is a recommended analysis launch control or a detailed analysis launch control corresponding to the product to be analyzed; when the number of products to be analyzed is greater than 1, the analysis launch control is a comparative analysis launch control corresponding to multiple products to be analyzed.

[0158] In one possible implementation, the aforementioned shopping guide interaction device 900 further includes:

[0159] The judgment module is used to determine whether other operations from the user have been received within a preset time after receiving the above target product selection operation.

[0160] The first sending module 920 is specifically used to: if not, in response to the target product selection operation, send the identifier of the product to be analyzed to the server.

[0161] In one possible implementation, the target product selection operation includes at least one of the following operations on the product to be analyzed: circle selection, click, long press, and checkmark.

[0162] Figure 10An exemplary schematic diagram of another shopping guide interaction device provided in an embodiment of this application is shown. Figure 10 As shown, the interactive shopping guide device 1000 includes:

[0163] The fifth receiving module 1010 is used to receive the identifier of the product to be analyzed sent by the user terminal; the identifier of the product to be analyzed is the identifier of the product to be analyzed selected by the user from the target product list page based on the shopping guide interaction mode corresponding to the target product list page.

[0164] The first acquisition module 1020 is used to acquire the corresponding product information to be analyzed based on the above-mentioned product identifier;

[0165] Analysis module 1030 is used to analyze the above-mentioned product information using the target large model to obtain the corresponding target analysis results;

[0166] The second sending module 1040 is used to send the above target analysis results to the above user terminal, so that the above user terminal can display the corresponding target shopping guide analysis page based on the above target analysis results.

[0167] In one possible implementation, the aforementioned shopping guide interaction device 1000 further includes:

[0168] The sixth receiving module is used to receive the target user identifier corresponding to the user sent by the user terminal.

[0169] The second acquisition module is used to acquire the corresponding target information based on the aforementioned target user identifier;

[0170] The aforementioned analysis module 1030 is specifically used to: comprehensively analyze the aforementioned product information to be analyzed and the aforementioned target information using a target big model to obtain corresponding target analysis results; wherein, the aforementioned target information includes target user information and / or historical order information corresponding to the user; the target analysis results include at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the aforementioned user corresponding to the product to be analyzed.

[0171] In one possible implementation, the fifth receiving module 1010 is specifically used for:

[0172] Receive the identifiers of the products to be analyzed for each of the multiple products to be analyzed sent by the user terminal;

[0173] The first acquisition module 1020 is used to acquire the product information corresponding to each of the multiple products to be analyzed based on the product identifier corresponding to each of the multiple products to be analyzed.

[0174] The aforementioned analysis module 1030 is specifically used to: use the target large model to compare and analyze the information of the products to be analyzed corresponding to each of the above-mentioned products to be analyzed to obtain the corresponding target analysis results; wherein, the above-mentioned target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information of the above-mentioned products to be analyzed.

[0175] In one possible implementation, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0176] The division of modules in the above-described shopping guide interaction device is for illustrative purposes only. In other embodiments, the shopping guide interaction device can be divided into different modules as needed to complete all or part of the functions of the shopping guide interaction device. The implementation of each module in the shopping guide interaction device provided in this application embodiment can be in the form of a computer program. This computer program can run on the shopping guide interaction device. The program modules constituted by this computer program can be stored in the memory of the shopping guide interaction device. When the computer program is executed by the processor, it implements all or part of the steps of the shopping guide interaction method described in this application embodiment.

[0177] Please see Figure 11 , Figure 11 This is a schematic diagram of the structure of an electronic device provided for an exemplary embodiment of this application. For example... Figure 11 As shown, the electronic device 1100 may include: at least one processor 1110, at least one network interface 1120, user interface 1130, memory 1140, and at least one communication bus 1150.

[0178] The communication bus 1150 can be used to realize the connection and communication of the above components.

[0179] The user interface 1130 may include a display screen and a camera. Optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0180] The network interface 1120 may optionally include a Bluetooth module, a Near Field Communication (NFC) module, a Wireless Fidelity (Wi-Fi) module, etc.

[0181] The processor 1110 may include one or more processing cores. The processor 1110 connects to various parts within the electronic device 1100 using various interfaces and lines. It executes various functions and processes data of the routing electronic device 1100 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1140, and by calling data stored in the memory 1140. Optionally, the processor 1110 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1110 may integrate one or more of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understandable that the aforementioned modem may not be integrated into the processor 1110, but may be implemented as a separate chip.

[0182] The memory 1140 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 1140 may include a non-transitory computer-readable medium. The memory 1140 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1140 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a receiving function, a display function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 1140 may also be at least one storage device located remotely from the aforementioned processor 1110. Figure 11 As shown, the memory 1140, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs.

[0183] In some possible embodiments, the electronic device 1100 is the one mentioned in the foregoing embodiments. Figure 9If the shopping guide interaction device 900 or user terminal shown is used, then the processor 1110 can be used to call the shopping guide interaction application stored in the memory 1140 and specifically perform the following operations:

[0184] Receive the target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the product identifier to be analyzed corresponding to the product selected by the user from the target product list page.

[0185] In response to the above target product selection operation, the above product identifier to be analyzed is sent to the server so that the server can obtain the corresponding product information to be analyzed based on the above product identifier, and use the target big model to analyze the above product information to obtain the corresponding target analysis results.

[0186] Receive the target analysis results sent by the aforementioned server.

[0187] Based on the above target analysis results, the corresponding target shopping guide analysis page will be displayed.

[0188] In some possible embodiments, the above-described target product selection operation also carries the target user identifier corresponding to the above-described user;

[0189] When the processor 1110 executes the above-mentioned response to the target product selection operation, sending the identifier of the product to be analyzed to the server so that the server can obtain the corresponding product information based on the identifier of the product to be analyzed and analyze the product information using the target big model to obtain the corresponding target analysis result, it is specifically used to perform: in response to the above-mentioned target product selection operation, sending the identifier of the product to be analyzed and the identifier of the target user to the server so that the server can obtain the corresponding product information based on the identifier of the product to be analyzed and obtain the corresponding target information based on the identifier of the target user, and perform comprehensive analysis on the product information and the target information using the target big model to obtain the corresponding target analysis result; wherein, the target information includes the target user information and / or historical order information corresponding to the user; the target analysis result includes at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the user corresponding to the product to be analyzed.

[0190] In some possible embodiments, the target product selection operation carries the identifier of the target product to be analyzed corresponding to each of the multiple target products selected by the user from the target product list page.

[0191] When the processor 1110 executes the above-mentioned response to the target product selection operation and sends the identifier of the product to be analyzed to the server, so that the server can obtain the corresponding product information based on the identifier of the product to be analyzed and analyze the product information using the target large model to obtain the corresponding target analysis result, it is specifically used to perform:

[0192] In response to the aforementioned target product selection operation, the identifiers of the multiple products to be analyzed are sent to the server. The server then retrieves the product information corresponding to each of the multiple products to be analyzed based on these identifiers and uses a target big model to compare and analyze this information to obtain corresponding target analysis results. These target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and a summary of the comparison analysis for each of the multiple products to be analyzed.

[0193] In some possible embodiments, the processor 1110 described above is also used to perform:

[0194] Receive the first trigger operation for the smart entry point in the target product list page; in response to the first trigger operation, enter the shopping guide interaction mode corresponding to the target product list page.

[0195] In some possible embodiments, before the processor 1110 executes the first trigger operation for receiving the smart entry in the target product list page, it is further configured to: receive a launch operation for the target product list page in the target application; the launch operation carries a target page identifier corresponding to the target product list page; determine whether the target product list page has the smart entry based on the target page identifier and a preset smart page identifier set corresponding to the target application; if so, in response to the launch operation, display the target product list page carrying the smart entry.

[0196] In some possible embodiments, the aforementioned smart entry is used to initiate the shopping guide interaction mode corresponding to the aforementioned target product list page. The display form of the aforementioned smart entry on the aforementioned target product list page includes at least one of the following: a control, a movable or fixed floating entry.

[0197] In some possible embodiments, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0198] In some possible embodiments, when the processor 1110 executes the above-mentioned display of the corresponding target shopping guide analysis page based on the above-mentioned target analysis results, it is specifically used to perform: determining the corresponding image to be displayed and the text information to be displayed based on the above-mentioned target analysis results; and rendering and displaying the corresponding target shopping guide analysis page based on the above-mentioned image to be displayed and the text information to be displayed.

[0199] In some possible embodiments, when the processor 1110 executes the above-mentioned response to the target product selection operation and sends the above-mentioned product identifier to be analyzed to the server, it is specifically used to perform the following:

[0200] In response to the above target product selection operation, the display elements corresponding to the product to be analyzed and the analysis start control are displayed in the pop-up window on the target product list page; the display elements include the product image and / or product name corresponding to the product to be analyzed; after receiving the second trigger operation for the analysis start control, the identifier of the product to be analyzed is sent to the server.

[0201] In some possible embodiments, the number of products to be analyzed varies, corresponding to different types of analysis launch controls; or, when the number of products to be analyzed is 1, the analysis launch control is a recommended analysis launch control or a detailed analysis launch control corresponding to the product to be analyzed; when the number of products to be analyzed is greater than 1, the analysis launch control is a comparative analysis launch control corresponding to multiple products to be analyzed.

[0202] In some possible embodiments, after the processor 1110 executes the target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page, it is further configured to: determine whether other operations by the user are received within a preset time period after receiving the target product selection operation.

[0203] When the processor 1110 executes the above response to the target product selection operation and sends the above product identifier to be analyzed to the server, it is specifically used to perform: if not, in response to the above target product selection operation, send the above product identifier to be analyzed to the server.

[0204] In some possible embodiments, the target product selection operation described above includes at least one of the following operations on the product to be analyzed: circle selection, click, long press, and checkmark.

[0205] In some possible embodiments, the electronic device 1100 is the one mentioned in the foregoing embodiments. Figure 10 If the shopping guide interaction device 1000 or server shown is used, the processor 1110 can be used to call the shopping guide interaction application stored in the memory 1140, and specifically perform the following operations: receive the identifier of the product to be analyzed sent by the user terminal; the identifier of the product to be analyzed is the identifier of the product to be analyzed selected by the user from the target product list page based on the shopping guide interaction mode corresponding to the target product list page; obtain the corresponding product information to be analyzed based on the identifier of the product to be analyzed, and analyze the product information to be analyzed using the target big model to obtain the corresponding target analysis result; send the target analysis result to the user terminal so that the user terminal displays the corresponding target shopping guide analysis page based on the target analysis result.

[0206] In some possible embodiments, before the processor 1110 executes the above-mentioned analysis of the product information to be analyzed using the target large model to obtain the corresponding target analysis result, it is further configured to: receive the target user identifier corresponding to the user sent by the user terminal; and obtain the corresponding target information based on the target user identifier.

[0207] When the processor 1110 executes the above-mentioned analysis of the product information to be analyzed using the target big model to obtain the corresponding target analysis result, it is specifically used to perform: comprehensive analysis of the product information to be analyzed and the target information using the target big model to obtain the corresponding target analysis result; wherein, the target information includes the target user information and / or historical order information corresponding to the user; the target analysis result includes at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the user corresponding to the product to be analyzed.

[0208] In some possible embodiments, when the processor 1110 executes the above-mentioned receiving of the product identifier to be analyzed sent by the user terminal, it is specifically used to perform: receiving the product identifiers corresponding to each of the multiple products to be analyzed sent by the user terminal.

[0209] When the processor 1110 executes the above-mentioned process of obtaining the corresponding product information based on the product identifier to be analyzed, and using the target large model to analyze the product information to obtain the corresponding target analysis result, it is specifically used to perform the following:

[0210] Based on the product identifiers corresponding to each of the aforementioned products to be analyzed, obtain the product information corresponding to each of the aforementioned products to be analyzed, and use the target large model to compare and analyze the product information corresponding to each of the aforementioned products to obtain the corresponding target analysis results; wherein, the aforementioned target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information corresponding to each of the aforementioned products to be analyzed.

[0211] In one possible implementation, the above target analysis results are obtained by the above target big model analyzing the above product information based on preset prompt information; the preset prompt information includes preset role setting information and preset language personality information; the above target big model is obtained by pre-training based on preset task requirement information; the preset task requirement information includes user behavior summary and comparison analysis requirement information and / or guidance suggestion requirement information.

[0212] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium.

[0213] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0214] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0215] The embodiments described above are merely preferred embodiments of this application and are not intended to limit the scope of this application. Any modifications and improvements made by those skilled in the art to the technical solutions of this application without departing from the spirit of this application should fall within the protection scope defined by the claims.

[0216] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A shopping guide interaction method, characterized in that, The method includes: Receives a target product selection operation input by the user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product to be analyzed selected by the user from the target product list page; In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information based on the product identifier and use the target big model to analyze the product information to obtain the corresponding target analysis result. Receive the target analysis results sent by the server; Based on the target analysis results, the corresponding target shopping guide analysis page will be displayed.

2. The method as described in claim 1, characterized in that, The target product selection operation also carries the target user identifier corresponding to the user; In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information based on the identifier and analyze the product information using the target large model to obtain the corresponding target analysis result, including: In response to the target product selection operation, the identifier of the product to be analyzed and the identifier of the target user are sent to the server, so that the server can obtain the corresponding product information to be analyzed based on the identifier of the product to be analyzed and the corresponding target information based on the identifier of the target user, and use the target big model to perform comprehensive analysis on the product information to be analyzed and the target information to obtain the corresponding target analysis results. The target information includes target user information and / or historical order information corresponding to the user; the target analysis results include at least one of the following: price analysis information, distance analysis information, taste analysis information, merchant service evaluation information, and recommendation evaluation information of the user corresponding to the product to be analyzed.

3. The method as described in claim 1, characterized in that, The target product selection operation carries the corresponding product identifiers for each of the multiple products to be analyzed selected by the user from the target product list page. In response to the target product selection operation, the identifier of the product to be analyzed is sent to the server, so that the server can obtain the corresponding product information based on the identifier and analyze the product information using the target large model to obtain the corresponding target analysis result, including: In response to the target product selection operation, the corresponding product identifiers of the plurality of products to be analyzed are sent to the server, so that the server can obtain the corresponding product information of the plurality of products to be analyzed based on the corresponding product identifiers of the plurality of products to be analyzed, and use the target large model to compare and analyze the corresponding product information of the plurality of products to be analyzed to obtain the corresponding target analysis results. The target analysis results include at least one of the following: price comparison analysis information, distance comparison analysis information, taste comparison analysis information, merchant service evaluation information, and comparison analysis summary information corresponding to each of the multiple products to be analyzed.

4. The method as described in claim 1, characterized in that, Before receiving the user's target product selection operation input based on the shopping guide interaction mode corresponding to the target product list page, the method further includes: Receive the first trigger operation for the smart entry in the target product list page; In response to the first triggering operation, the corresponding shopping guide interaction mode of the target product list page is entered.

5. The method as described in claim 4, characterized in that, Before receiving the first trigger operation for the smart entry in the target product list page, the method further includes: Receive a launch operation from the target application for the target product list page; the launch operation carries a target page identifier corresponding to the target product list page; Based on the target page identifier and the preset smart page identifier set corresponding to the target application, determine whether the target product list page has the smart entry point; If so, in response to the launch operation, the target product list page carrying the smart entry point is displayed.

6. The method as described in claim 1, characterized in that, The step of sending the identifier of the product to be analyzed to the server in response to the target product selection operation includes: In response to the target product selection operation, the display elements corresponding to the product to be analyzed and the analysis start control are displayed in a pop-up window on the target product list page; the display elements include the product image and / or product name corresponding to the product to be analyzed. Upon receiving the second trigger operation for the analysis initiation control, the identifier of the product to be analyzed is sent to the server.

7. A shopping guide interaction method, characterized in that, The method is applied to the server side, and the method includes: Receive the identifier of the product to be analyzed sent by the user terminal; the identifier of the product to be analyzed is the identifier of the product to be analyzed selected by the user from the target product list page based on the shopping guide interaction mode corresponding to the target product list page; Based on the product identifier to be analyzed, obtain the corresponding product information to be analyzed, and use the target large model to analyze the product information to obtain the corresponding target analysis results; The target analysis results are sent to the user terminal so that the user terminal can display the corresponding target shopping guide analysis page based on the target analysis results.

8. A shopping guide interactive device, characterized in that, The shopping guide interactive device includes: The first receiving module is used to receive a target product selection operation input by a user based on the shopping guide interaction mode corresponding to the target product list page; the target product selection operation carries the identifier of the product to be analyzed corresponding to the product to be analyzed selected by the user from the target product list page. The first sending module is used to send the identifier of the product to be analyzed to the server in response to the target product selection operation, so that the server can obtain the corresponding product information to be analyzed based on the product identifier and use the target big model to analyze the product information to obtain the corresponding target analysis result. The second receiving module is used to receive the target analysis results sent by the server; The first display module is used to display the corresponding target shopping guide analysis page based on the target analysis results.

9. An electronic device, characterized in that, include: Processor and memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code stored in the memory to perform the method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a plurality of instructions adapted for loading by a processor and executing the method steps as claimed in any one of claims 1-7.

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