Vehicle recommendation method, computing device, storage medium and program product

Abstract

The embodiment of the invention provides a vehicle recommendation method, computing equipment, a computer readable storage medium and a computer program product. The vehicle recommendation method comprises the steps that vehicle search information sent by a client side is received, and the vehicle search information is obtained by the client side in response to input operation of a target user; identifying at least one search keyword from the vehicle search information; obtaining vehicle preference characteristics of the target user, wherein the vehicle preference characteristics are obtained through recognition based on historical behavior data corresponding to the client; determining at least one target vehicle according to at least one search keyword and the vehicle preference characteristics; and sending the vehicle recommendation information of the at least one target vehicle to the client. According to the technical scheme provided by the embodiment of the invention, the target vehicle recommended to the target user better meets the actual demand of the target user.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular to a vehicle recommendation method, computing device, computer-readable storage medium, and computer program product. Background Art

[0002] In the automotive consumer market, vehicle recommendations are widely available on various car recommendation platforms. When users need to buy a car, they can use the car recommendation platform to get recommendations for models that meet their needs.

[0003] In related technologies, car recommendation platforms typically rely on users manually selecting parameters to recommend vehicles. To obtain recommendations for a specific vehicle type, users must follow a strict hierarchical menu to select parameters. For example, if a user wants to search for "SUVs" and "fuel vehicles" in the "300,000-400,000 RMB" price range, they must navigate through a three-level menu and select "fuel vehicles, SUVs, 300,000-400,000 RMB" in that order.

[0004] In the process of realizing the concept of this application, the inventors discovered that the vehicle recommendation method in the related art relies on standardized parameters, and the vehicles recommended to users often fail to meet the users' needs for vehicles. Summary of the Invention

[0005] Embodiments of the present application provide a vehicle recommendation method, apparatus, computing device, computer-readable storage medium, and computer program product.

[0006] In a first aspect, an embodiment of the present application provides a vehicle recommendation method, which is applied to a server, and the method includes:

[0007] receiving vehicle search information sent by a client, where the vehicle search information is obtained by the client in response to an input operation of a target user;

[0008] identifying at least one search keyword from the vehicle search information;

[0009] Obtaining a vehicle preference feature of the target user, wherein the vehicle preference feature is obtained by identifying historical behavior data corresponding to the client;

[0010] determining at least one target vehicle based on at least one search keyword and the vehicle preference feature;

[0011] The vehicle recommendation information of the at least one target vehicle is sent to the client.

[0012] In a second aspect, an embodiment of the present application provides a vehicle recommendation method, which is applied to a client, and the method includes:

[0013] In response to a target user's vehicle search request, display a vehicle search page;

[0014] Acquiring vehicle search information input by the target user based on the vehicle search page;

[0015] Sending the vehicle search information to a server so that the server identifies at least one search keyword from the vehicle search information; obtaining a vehicle preference characteristic of the target user, the vehicle preference characteristic being obtained by identifying historical behavior data corresponding to the client; and determining at least one target vehicle based on the at least one search keyword and the vehicle preference characteristic;

[0016] receiving vehicle recommendation information of the at least one target vehicle sent by the server;

[0017] At least one of the vehicle recommendation information is displayed.

[0018] In a third aspect, an embodiment of the present application provides a vehicle recommendation device, which is applied to a server, and includes:

[0019] A first receiving module is configured to receive vehicle search information sent by a client, wherein the vehicle search information is obtained by the client in response to an input operation of a target user;

[0020] a first recognition module, configured to recognize at least one search keyword from the vehicle search information;

[0021] A first acquisition module is configured to acquire a vehicle preference feature of the target user, wherein the vehicle preference feature is obtained by identifying historical behavior data corresponding to the client;

[0022] a first matching module, configured to determine at least one target vehicle based on at least one search keyword and the vehicle preference feature;

[0023] The first recommendation module is configured to send vehicle recommendation information of the at least one target vehicle to the client.

[0024] In a fourth aspect, an embodiment of the present application provides a vehicle recommendation device, which is applied to a client, and the device includes:

[0025] A first display module, configured to display a vehicle search page in response to a vehicle search request from a target user;

[0026] A second acquisition module is used to acquire vehicle search information input by the target user based on the vehicle search page;

[0027] a first sending module configured to send the vehicle search information to a server, so that the server can identify at least one search keyword from the vehicle search information; obtain a vehicle preference feature of the target user, the vehicle preference feature being obtained by identifying historical behavior data corresponding to the client; and determine at least one target vehicle based on the at least one search keyword and the vehicle preference feature;

[0028] A second receiving module is configured to receive vehicle recommendation information of the at least one target vehicle sent by the server;

[0029] The second display module is used to display at least one vehicle recommendation information.

[0030] In a fifth aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component;

[0031] The storage component stores a computer program; the computer program is used to be called and executed by the processing component to implement the vehicle recommendation method provided in the embodiment of the present application.

[0032] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processing component, the vehicle recommendation method provided in the embodiment of the present application is implemented.

[0033] In a seventh aspect, an embodiment of the present application provides a computer program product, comprising a computer program or instructions, which, when executed by a processing component, implements the vehicle recommendation method provided in an embodiment of the present application.

[0034] The embodiment of the present application adopts the following technical solutions: receiving vehicle search information sent by a client, wherein the vehicle search information is obtained by the client in response to an input operation of a target user; identifying at least one search keyword from the vehicle search information; obtaining the vehicle preference characteristics of the target user, wherein the vehicle preference characteristics are obtained by identification based on historical behavior data corresponding to the client; determining at least one target vehicle based on at least one search keyword and the vehicle preference characteristics; and sending vehicle recommendation information of the at least one target vehicle to the client. By recommending a vehicle to a target user based on at least one search keyword identified from the vehicle search information and obtaining the vehicle preference characteristics of the target user, the target vehicle can be made to better meet the actual needs of the target user.

[0035] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 A system architecture diagram is shown in which the technical solution of an embodiment of the present application can be applied;

[0038] Figure 2 A flowchart of an embodiment of a vehicle recommendation method provided by this application;

[0039] Figure 3 A flowchart of a vehicle recommendation method provided by another embodiment of the present application is shown;

[0040] Figure 4 A block diagram of a vehicle recommendation device is shown in an embodiment of the present application;

[0041] Figure 5 A block diagram of a vehicle recommendation device is shown in an embodiment of the present application;

[0042] Figure 6 A block diagram of a computing device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] It should be noted that, in the case of user information involved in the embodiments of the present application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) are in compliance with relevant laws and standards.

[0045] In the automotive consumer market, vehicle recommendations are widely available on various car recommendation platforms. When users need to buy a car, they can use the car recommendation platform to get recommendations for models that meet their needs.

[0046] In related technologies, car recommendation platforms typically rely on users manually selecting parameters to recommend vehicles. To obtain recommendations for a specific vehicle type, users must follow a strict hierarchical menu to select parameters. For example, if a user wants to search for "SUVs" and "fuel vehicles" in the "300,000-400,000 RMB" price range, they must navigate through a three-level menu and select "fuel vehicles, SUVs, 300,000-400,000 RMB" in that order.

[0047] In the process of realizing the concept of this application, the inventors discovered that the vehicle recommendation method in the related art relies on standardized parameters, and the vehicles recommended to users often fail to meet the users' needs for vehicles.

[0048] In response to the technical problem that the existing vehicles recommended to users often fail to meet the users' needs for vehicles, an embodiment of the present application provides a solution. The basic idea is: receiving vehicle search information sent by a client, wherein the vehicle search information is obtained by the client in response to the input operation of the target user; identifying at least one search keyword from the vehicle search information; obtaining the vehicle preference characteristics of the target user, wherein the vehicle preference characteristics are obtained by identification based on the historical behavior data corresponding to the client; determining at least one target vehicle based on at least one search keyword and the vehicle preference characteristics; and sending the vehicle recommendation information of the at least one target vehicle to the client. By recommending a vehicle to the target user based on at least one search keyword identified from the vehicle search information and obtaining the vehicle preference characteristics of the target user, the target vehicle can be more in line with the actual needs of the target user.

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0050] Figure 1 A system architecture diagram is shown in which the technical solution of an embodiment of the present application can be applied. The system architecture may include a client 101 and a server 102.

[0051] The client 101 and the server 102 can be connected via a network. The network provides a medium for the communication link between the client 101 and the server 102. The network can include various connection types, such as wired, wireless, or fiber optic cables. The client 101 can interact with the server 102 via the network to receive or send messages.

[0052] The client 101 may be a browser, an APP (Application), or a web application such as an H5 (HyperText Markup Language 5, version 5 of Hypertext Markup Language) application, or a light application (also known as a mini-program, a lightweight application) or a cloud application. The client 101 may be deployed in an electronic device and may rely on the device to run or on certain apps in the device to run. For example, the electronic device may have a display screen and support information browsing, such as a personal mobile terminal such as a mobile phone, a tablet computer, a personal computer, a desktop computer, a smart speaker, a smart watch, etc. For ease of understanding, Figure 1 The client is mainly represented by a device image. Various other types of applications can usually be configured in electronic devices, such as human-computer dialogue applications, model training applications, text processing applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc. Electronic devices can refer to devices used by users, which have the functions of computing, Internet access, communication, etc. required by users, such as mobile phones, tablet computers, personal computers, wearable devices, etc. Electronic devices can usually include at least one processing component and at least one storage component. Electronic devices may also include basic configurations such as network card chips, IO (input / output) buses, audio and video components, which are not limited in this application. Optionally, according to the implementation form of the electronic device, some peripheral devices may also be included, such as keyboards, mice, input pens, printers, etc., which are not limited in this application.

[0053] The server 102 may include servers that provide various services, such as a server for background training that provides support for the model used on the client 101, or a server that processes interactive information sent by the client.

[0054] It should be noted that the server 102 can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. The server can also be a server of a distributed system, or a server combined with a blockchain. The server can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology.

[0055] It should be noted that the vehicle recommendation method provided in the embodiments of the present application is generally executed by the server 102, and the corresponding vehicle recommendation device is generally installed in the server 102. However, in other embodiments of the present application, the client 101 may also have similar functions to the server 102, thereby executing the vehicle recommendation method provided in another embodiment of the present application.

[0056] It should be understood that Figure 1 The number of clients and servers in the example is only for reference. Any number of clients and servers may be used depending on the implementation requirements.

[0057] The implementation details of the technical solution of the embodiment of the present application are described in detail below.

[0058] Figure 2 This is a flowchart of an embodiment of a vehicle recommendation method provided in this application. The technical solution of this embodiment can be executed by the server. Figure 2 The vehicle recommendation method shown may include the following steps:

[0059] 201: Receive vehicle search information sent by a client, where the vehicle search information is obtained by the client in response to an input operation of a target user.

[0060] The target user may be a user who uses the client to search for a vehicle. The target user may be a potential car buyer, a car enthusiast, etc. The target user may enter vehicle search information through the input interface provided by the client.

[0061] Vehicle search information can be used to indicate the target user's specific needs and preferences for the vehicle they are looking for. This vehicle search information can be input in natural language or as structured parameters. For example, vehicle search information input in natural language could be "I'm looking for a family sedan around 300,000 yuan." Vehicle search information input in structured parameter form could be "Budget 300,000 yuan, family, sedan."

[0062] 202 : Identify at least one search keyword from the vehicle search information.

[0063] The at least one search keyword may be a keyword extracted from the vehicle search information that reflects the target user's core needs or screening criteria. For example, from the vehicle search information "I want to find a family sedan priced around 300,000 yuan," the keywords extracted may be: 300,000 yuan, family, and sedan.

[0064] By identifying at least one search keyword from the vehicle search information, the server can determine what kind of vehicle the target user wants to find, and based on the at least one keyword, can generate personalized recommendation results that meet the user's needs.

[0065] In one possible implementation of this application, at least one search keyword can be identified from the vehicle search information entered by the target user during a single interaction. By identifying at least one search keyword from the currently acquired vehicle search information, the response speed of the vehicle recommendation service can be improved without the need for additional analysis of historical conversations.

[0066] However, the present invention is not limited thereto. At least one search keyword may be identified from vehicle search information input by the target user in the current interaction and input by the target user in previous interactions.

[0067] By combining the current conversation with historical conversations, the server can more comprehensively understand the target user's vehicle search intention and provide the target user with more accurate vehicle recommendation services.

[0068] 203: Obtain the target user's vehicle preference characteristics, which are obtained by identifying the target user's corresponding historical behavior data.

[0069] Among them, vehicle preference features can refer to the target user's specific preferences for vehicles, such as power type, model, budget range, brand, etc.

[0070] The historical behavior data may include the target user's operation records and interaction data on the client. For example, the historical behavior data may include: vehicle browsing records, vehicle search records, vehicle consultation records, etc.

[0071] By obtaining the target user's vehicle preference characteristics, the server can generate personalized vehicle recommendation results that better meet the target user's needs.

[0072] 204 : Determine at least one target vehicle based on at least one search keyword and a vehicle preference feature.

[0073] After obtaining at least one search keyword and vehicle preference characteristics, the server can integrate the at least one search keyword and vehicle preference characteristics. For example, at least one search keyword may include "SUV suitable for long-distance self-drive"; the user's vehicle preference characteristics may include "pure electric power type" and "budget within 300,000 yuan." Thus, the integrated vehicle requirement may be "pure electric SUV suitable for long-distance self-drive, budget within 300,000 yuan."

[0074] On this basis, the server can match the integrated vehicle demand with the pre-built vehicle resource library to determine at least one target vehicle that meets the vehicle demand from the vehicle resource library.

[0075] In some embodiments, determining at least one target vehicle based on at least one search keyword and vehicle preference characteristics may be specifically implemented as follows:

[0076] Searching for at least one target vehicle data item that matches at least one search keyword and a vehicle preference feature among a plurality of vehicle data items recorded in a vehicle database, wherein the plurality of vehicle data items are data uploaded by a vehicle merchant;

[0077] The vehicles corresponding to the at least one piece of target vehicle data are determined as target vehicles.

[0078] Among them, the data of the vehicle resource library can come from multiple sources. In one embodiment of the present application, the server can collect the source information of the vehicles listed by users on the vehicle trading platform to build a vehicle resource library. When users list vehicles on the vehicle trading platform, they often upload the vehicle condition information, such as the actual use of the vehicle (such as mileage, maintenance records, accident history), appearance condition (such as whether there are scratches, degree of interior wear), inventory location (such as the dealer address), etc. This information constitutes the vehicle condition description information. By collecting this information, a vehicle resource library can be built.

[0079] In some embodiments, after determining at least one target vehicle, the method may further include:

[0080] Obtaining vehicle condition information and price information of at least one target vehicle;

[0081] Generate vehicle recommendation information based on vehicle condition information and price information.

[0082] 205: Send vehicle recommendation information of at least one target vehicle to the client.

[0083] The vehicle recommendation information may include vehicle condition description information related to the target vehicle to help the target user understand the target vehicle.

[0084] After receiving the vehicle recommendation information, the client can display the vehicle recommendation information.

[0085] In an embodiment of the present application, by adopting: receiving vehicle search information sent by a client, the vehicle search information is obtained by the client in response to the input operation of the target user; identifying at least one search keyword from the vehicle search information; obtaining the vehicle preference characteristics of the target user, the vehicle preference characteristics are obtained by identifying based on the historical behavior data corresponding to the client; determining at least one target vehicle based on at least one search keyword and the vehicle preference characteristics; and sending the vehicle recommendation information of at least one target vehicle to the client. By recommending a vehicle to the target user based on at least one search keyword identified from the vehicle search information and obtaining the vehicle preference characteristics of the target user, the target vehicle can be made to better meet the actual needs of the target user.

[0086] In some embodiments, identifying at least one search keyword from the vehicle search information may be specifically implemented by inputting the vehicle search information into at least one keyword recognition model to obtain at least one search keyword by using the at least one keyword recognition model to recognize the at least one search keyword.

[0087] Among them, the keyword recognition model involved in the embodiment of this application can be a language model (Language Mode, LM) or a multimodal model (Multimodal Model, MM) based on artificial intelligence, and the embodiment of this application does not limit the number of model parameters supported by the model, with the goal of meeting actual needs. If the model parameters are relatively large, the scale of the model will be relatively large, and the model performance will be relatively better. Of course, more time and resources will be consumed during reasoning or training. If the model parameters are relatively small, the scale of the model will be relatively small. When the performance meets the requirements, the model is more lightweight, and the time and resources consumed during reasoning or training are relatively less. The keyword recognition model can be a deep learning model for processing and generating natural language text or multimodal data, which can be implemented based on a neural network architecture, and it can be pre-trained on a large amount of data. In an optional implementation, the keyword recognition model can include an encoder, a decoder, a self-attention layer, and a feed-forward neural network. The encoder is mainly used to convert input data (usually in sequence form) into vector representation. This process can capture the semantic features of the input data. The decoder is responsible for converting the intermediate representation generated by the encoder into output data (usually in sequence form). The self-attention layer is a mechanism that allows the model to pay attention to other positions in the sequence to better encode the current position information. The feedforward neural network can perform nonlinear transformations on the output of the self-attention layer to enhance the model's expressiveness. The various parts work together to enable the models built on them to perform well in various complex processing tasks, such as natural language processing, computer vision, speech recognition, machine translation, text summarization, and intelligent question answering.

[0088] In an embodiment of the present application, the keyword recognition model can be used to extract core words or phrases that can reflect the needs of the target user from the vehicle search information input by the target user as search keywords.

[0089] In an embodiment of the present application, one or more keyword recognition models may be used to perform search keyword recognition from vehicle search information.

[0090] When a keyword recognition model is used to perform search keyword recognition from vehicle search information, the keyword recognition model can be trained based on unified training data and algorithm logic, and can parse vehicle search information in a consistent manner, ensuring the consistency and stability of vehicle keyword recognition.

[0091] When multiple keyword recognition models are used to identify search keywords from vehicle search information, each keyword recognition model can be used to extract specific vehicle keywords from the vehicle search information based on its own training logic. For example, the multiple keyword recognition models may include a vehicle mileage recognition model, a vehicle color recognition model, a vehicle model recognition model, a vehicle brand recognition model, and so on.

[0092] By utilizing multiple keyword recognition models to perform search keyword recognition from vehicle search information, we can fully understand the needs of target users from different dimensions, thereby improving the efficiency and accuracy of vehicle keyword recognition.

[0093] The server may obtain at least one vehicle keyword by integrating the recognition results of at least one keyword recognition model.

[0094] In some embodiments, after determining at least one target vehicle, the method may further include: obtaining vehicle model description information of at least one target vehicle from a vehicle model knowledge base; and generating vehicle recommendation information for each target vehicle based on the vehicle model description information of each target vehicle.

[0095] The vehicle model knowledge base may include a database containing officially released vehicle model information, including basic vehicle information, performance parameters, market prices, configuration information, and other data. Basic information may include, for example, brand, model, year, and power type (fuel, electric, hybrid); performance parameters may include, for example, range, fuel consumption, acceleration time, and drive type (two-wheel drive, four-wheel drive); market prices may include, for example, current market prices and promotional offers; and configuration information may include, for example, exterior color, interior materials, and technological features (panoramic sunroof, autonomous driving function, etc.).

[0096] After determining the target vehicle based on the search keywords and vehicle preference features, the server can retrieve the model description information corresponding to the target vehicle from the model knowledge base based on the name of the target vehicle.

[0097] In one embodiment of the present application, vehicle recommendation information may be generated based on vehicle condition description information and vehicle model description information.

[0098] By generating vehicle recommendation information based on vehicle condition description information and vehicle model description information, target users can fully understand the actual status and official configuration information of the target vehicle, enhancing the transparency and credibility of vehicle information, thereby increasing the target users' willingness to purchase vehicles.

[0099] In some embodiments, after receiving the vehicle search information sent by the client, the method may further include:

[0100] Perform intent recognition on vehicle search information;

[0101] If it is determined according to the intention recognition result that the vehicle search information is used for vehicle search, step 202 is executed; otherwise, guidance prompt information is generated, and the guidance prompt information is used to guide the target user to input the vehicle search information;

[0102] Sending guidance prompt information to the client;

[0103] Return to step 201 and continue execution.

[0104] Vehicle search information can be any information entered by the target user through the client. It can be expressed in natural language and may involve vehicle searches, casual conversations, or inquiries about other information. Due to the uncertainty of vehicle search information, directly using vehicle search information to make vehicle recommendations may result in low recommendation accuracy.

[0105] Based on this, after receiving vehicle search information, natural language processing technology can be used to identify the intent of the vehicle search information. By identifying the intent of the vehicle search information, the target user's needs or purpose can be determined. The intent identification results can include vehicle search intent and other intents.

[0106] The vehicle search intention may indicate that the vehicle search information input by the target user clearly expresses its vehicle search needs. In this case, the step of identifying at least one search keyword from the vehicle search information may be continued.

[0107] Other intents can indicate that the target user's vehicle search information isn't intended to be a vehicle search, but rather to chat or inquire about other information. If the intent is determined to be other intents, the server can generate guidance prompts to help the target user refocus on their vehicle search needs. For example, guidance prompts can be presented in the form of questions or suggestions to guide the target user to provide specific vehicle search information.

[0108] After sending the guidance prompt information to the client, the target user may enter vehicle search information according to the guidance of the guidance prompt information, so that the server can return to the step of receiving the vehicle search information sent by the client to continue execution.

[0109] In an embodiment of the present application, the server can construct personalized guidance prompt information by combining a preset template or a dynamic generation method.

[0110] For example, the preset template can be "Do you need to buy a car recently? If so, please tell me what kind of car you want to buy and I can recommend a suitable model for you."

[0111] In the process of dynamically generating guidance prompt information, guidance prompt information that matches the target user can be dynamically generated based on the content of the vehicle search information and / or historical behavior data. For example, based on historical behavior data, it can be determined that the target user has searched for "new energy vehicles." Based on this, a guidance prompt message such as "Are you interested in new energy vehicles? If so, please tell me what kind of car you would like to buy. I can recommend a suitable model for you" can be generated.

[0112] In some embodiments, the method may further include:

[0113] receiving first search information sent by a client, where the first search information is used to search for at least one purchase link in a vehicle purchase process;

[0114] Retrieving response information matching the first search information from the process knowledge base;

[0115] Send the reply information to the client.

[0116] After selecting a vehicle, the target user may need to know the details of the subsequent car purchasing process. In this case, the first search information may be the target user's questions or needs regarding a specific step in the vehicle purchasing process. For example, "What is the application process for a car loan?", "What are the electric vehicle subsidy policies?", "How do I choose car insurance?"

[0117] The process knowledge base can be a database used to store information related to the car purchasing process, covering knowledge and operational guidelines for each step of the process. For example, in the loan phase, it covers the application requirements, required documents, and approval process for car loans; in the subsidy collection phase, it covers the financial subsidy policies, application methods, and subsidy amounts in different regions; in the insurance phase, it covers the types of auto insurance, insurance billing methods, and insurance recommendations; and in the license plate phase, it covers the required documents and application process.

[0118] After receiving the first search message, the server can extract process keywords from the first search message. It can then use natural language analysis technology to analyze the semantics of the target user's input and match it with data in the process knowledge base to filter out response information based on the degree of match. For example, if the first search message includes car loans, the response information may include information such as the application conditions, required documents, and approval process for the loan.

[0119] Through the process knowledge base, detailed information support for each link in the car purchasing process is provided to target users, so that target users do not need to manually search for relevant information in the car purchasing process, thereby improving user experience.

[0120] In some embodiments, the method may further include:

[0121] Utilize the preference recognition model to identify the target user's vehicle preference characteristics based on the client's corresponding historical behavior data;

[0122] At least one target vehicle is determined based on the vehicle preference characteristics.

[0123] The preference recognition model involved in the embodiments of this application can be an artificial intelligence-based language model (LM) or multimodal model (MM). The preference recognition model can be used to analyze the target user's operation trajectory on the client, such as browsing, searching, clicking, etc., and extract vehicle preference features that can reflect the target user's car purchasing tendency, such as brand preference, model preference, budget range, power type preference, etc.

[0124] After extracting vehicle preference features, the server can recommend vehicles to the target user based on similarity and cluster analysis. For example, the server can calculate the similarity of vehicle preference features between different users to find other users with similar preferences and then recommend vehicles to the target user based on their vehicle preferences. The server can also divide users into different user groups based on vehicle preference features and make vehicle recommendations for each user group.

[0125] Figure 3 A flow chart of a vehicle recommendation method provided by another embodiment of the present application is shown. The method can be applied to a client, such as Figure 3 As shown, the method may specifically include the following steps:

[0126] 301: In response to the target user's vehicle search request, display the vehicle search page;

[0127] 302: Obtain vehicle search information input by the target user based on the vehicle search page;

[0128] 303: Sending the vehicle search information to the server, so that the server can identify at least one search keyword from the vehicle search information; obtaining a target user's vehicle preference feature, the vehicle preference feature being obtained by identifying the target user based on the client's corresponding historical behavior data; and determining at least one target vehicle based on the at least one search keyword and the vehicle preference feature.

[0129] 304: Receive vehicle recommendation information of at least one target vehicle sent by the server;

[0130] 305: Display at least one vehicle recommendation information.

[0131] The detailed implementation and beneficial effects of each step in the method of this embodiment have been described in detail in the aforementioned embodiments and will not be elaborated here.

[0132] The following describes the implementation of the vehicle recommendation method provided in the embodiment of the present application in conjunction with specific application scenarios.

[0133] A target user opens the car recommendation platform client and enters a vehicle search query in natural language: "I want a car priced between 200,000 and 300,000 yuan, suitable for long-distance driving, and with good fuel consumption." The client sends this query to the server, which receives it. Previously, if the target user had entered a vehicle search query such as "How is the car market lately?", the server would use natural language processing technology to identify the intent of the vehicle search query and determine that the user's intent was to inquire about the car market, not to search for a vehicle. The server can then combine the target user's historical behavior data (for example, if the target user has searched for "fuel-efficient sedan") to dynamically generate a guidance prompt: "Are you looking for a fuel-efficient car for daily commuting? If so, please tell me your budget and specific needs, and I can recommend a suitable model." This prompt is then sent to the client. This prompt allows the target user to enter a vehicle search query with the intent of searching for a vehicle.

[0134] The server can input the target user's vehicle search information, such as "I want a car priced between 200,000 and 300,000 RMB, suitable for long-distance driving, and with good fuel consumption," into multiple keyword recognition models. The vehicle model recognition model fails to identify the specific vehicle model keyword; the budget recognition model extracts the keywords "200,000-300,000 RMB"; and the feature recognition model extracts the keywords "suitable for long-distance driving" and "low fuel consumption." The server integrates the results of these keyword recognition models to obtain the search keywords: 200,000-300,000 RMB, suitable for long-distance driving, and low fuel consumption.

[0135] The server can use the preference recognition model to analyze the target user's historical behavior data on the client, including previous vehicle browsing history (he's frequently browsed models like the Toyota Corolla and Nissan Sylphy) and search history (searches for "150,000-250,000 RMB sedans" and "joint venture brand fuel-efficient cars"). Through this analysis, the preference recognition model identifies the target user's vehicle preferences as follows: a budget range of 150,000-250,000 RMB (as reflected in historical search records), a preference for joint venture brands, a preference for sedan models, and a focus on fuel efficiency and cost-effectiveness.

[0136] The server integrates the search keywords "200,000-300,000, suitable for long-distance driving, low fuel consumption" and the vehicle preference characteristics "budget range 150,000-250,000, preference for joint venture brands, preference for sedan models, and more attention to fuel consumption and cost-effectiveness". The integrated vehicle demand is: within 200,000-300,000, joint venture brand, suitable for long-distance driving, and low-fuel-consumption sedan.

[0137] Then, the server can search for vehicle data that matches the integrated vehicle demand in the vehicle database constructed by the vehicle source information uploaded by each vehicle merchant, and determine the vehicles corresponding to these vehicle data as target vehicles.

[0138] On this basis, the server can also obtain the target vehicle's model description information from the vehicle model knowledge base, including official data such as brand, model, year, power type, cruising range, fuel consumption, market price, configuration information, etc.; at the same time, obtain the vehicle condition information of these target vehicles in the vehicle database, such as mileage, maintenance records, etc.

[0139] Then, based on the vehicle condition and model descriptions, detailed vehicle recommendations can be generated. For example, "Target vehicle and quantity 1, 2023 model, hybrid, with a combined fuel consumption of as low as 4.5L / 100km, ideal for long-distance driving. Official guide price: 219,800-269,800 RMB, current dealer discount: 20,000 RMB. The vehicle has only 10,000 km of mileage, no major repairs, and a pristine interior. It has a rich configuration, including a panoramic sunroof and intelligent driver assistance systems."

[0140] Finally, the server sends the vehicle recommendation information to the client, and the client displays the vehicle recommendation content to the target user on the interface.

[0141] If a target user, after viewing the recommended vehicles, becomes interested in the car purchasing process, they can enter a first search query in the client: "What subsidies are available for hybrid vehicles?" The server receives this query, extracts the keywords "hybrid vehicle" and "subsidy policy," and uses natural language analysis to match them against the process knowledge base. It then finds information on hybrid vehicle subsidy policies, such as the amount of financial subsidies in different regions, application methods, and requirements. The server then sends this information to the client, providing the target user with detailed information on the car purchasing process.

[0142] In an embodiment of the present application, a car recommendation platform can be built on a process orchestration platform, which can integrate various functions and data processing processes related to vehicle recommendation in a modular and visual manner to achieve efficient and flexible recommendation services. The process orchestration platform can provide a visual orchestration interface, allowing developers to graphically combine and connect the various functional modules required for the car recommendation platform (such as the user input reception module, keyword recognition module, user preference analysis module, vehicle matching module, recommendation result generation and display module, etc.) like building blocks, define the data flow and the interaction logic between the modules, and thus quickly build a complete car recommendation business process.

[0143] The process orchestration platform provides a large number of prefabricated components and templates that developers can reuse directly, avoiding duplication of development. Furthermore, the visual orchestration approach lowers the development threshold, enabling people with different technical backgrounds to collaborate and accelerate the development of the car recommendation platform.

[0144] It should be noted that the technical solutions of the embodiments of this application are applicable to a virtual network environment. The users described are generally referred to as "virtual users." Real users can register user accounts on the server through registration to obtain user identities in the network environment. The same user account can be logged into the server through different types of client terminals, allowing the server to identify the same user.

[0145] Interactions between the server and the user can be implemented based on user accounts. Data sent or received by the server to the user is also based on user accounts. The user corresponding to the user account actually receives or sends data to the server. Furthermore, users can communicate with each other through user accounts. The term "user" can refer to an individual or an organization, such as a business, and this application does not impose specific restrictions on this.

[0146] It should be noted that in some of the processes described in the above embodiments and the accompanying drawings, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The sequence numbers of the operations, such as 201, 202, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0147] Figure 4 The block diagram of a vehicle recommendation device provided in an embodiment of the present application is shown. The device can be applied to a server, such as Figure 4 As shown, the device may include:

[0148] The first receiving module 401 is configured to receive vehicle search information sent by a client, where the vehicle search information is obtained by the client in response to an input operation of a target user;

[0149] A first recognition module 402 is configured to recognize at least one search keyword from the vehicle search information;

[0150] The first acquisition module 403 is used to obtain the target user's vehicle preference characteristics, which are obtained by identifying the vehicle preference characteristics based on the client's corresponding historical behavior data;

[0151] A first matching module 404 is configured to determine at least one target vehicle based on at least one search keyword and a vehicle preference feature;

[0152] The first recommendation module 405 is configured to send vehicle recommendation information of at least one target vehicle to the client.

[0153] In some embodiments, the first recognition module 402 is specifically configured to input the vehicle search information into at least one keyword recognition model, so as to obtain at least one search keyword by using the at least one keyword recognition model to recognize the vehicle search information.

[0154] In some embodiments, after determining at least one target vehicle, the apparatus may further include:

[0155] A third acquisition module is used to obtain model description information of at least one target vehicle from the model knowledge base;

[0156] The first generating module is used to generate vehicle recommendation information for each target vehicle based on the vehicle model description information of each target vehicle.

[0157] In some embodiments, after receiving the vehicle search information sent by the client, the device may further include:

[0158] A first recognition module, configured to perform intent recognition on the vehicle search information;

[0159] a judgment module configured to execute the first recognition module 402 if it is determined based on the intention recognition result that the vehicle search information is for vehicle search; otherwise, generate guidance prompt information for guiding the target user to input vehicle search information;

[0160] A second sending module is used to send the guidance prompt information to the client;

[0161] The iteration module is used to return to execute the first receiving module 401.

[0162] In some embodiments, the first receiving module 401 is specifically configured to receive vehicle search information sent by the client in response to the guidance prompt information.

[0163] In some embodiments, the apparatus may further include:

[0164] A fourth receiving module is configured to receive first search information sent by a client, where the first search information is used to search for at least one purchase link in a vehicle purchase process;

[0165] A first retrieval module is used to retrieve response information matching the first search information from the process knowledge base;

[0166] The third sending module is used to send the reply information to the client.

[0167] In some embodiments, after determining at least one target vehicle, the apparatus may further include:

[0168] A fourth acquisition module, configured to acquire vehicle condition information and price information of at least one target vehicle;

[0169] The second generating module is used to generate vehicle recommendation information based on vehicle condition information and price information.

[0170] In some embodiments, the first matching module 404 is specifically configured to:

[0171] Searching for at least one target vehicle data item that matches at least one search keyword and a vehicle preference feature among a plurality of vehicle data items recorded in a vehicle database, wherein the plurality of vehicle data items are data uploaded by a vehicle merchant;

[0172] The vehicles corresponding to the at least one piece of target vehicle data are determined as target vehicles.

[0173] In some embodiments, the apparatus may further include:

[0174] The second recognition module is used to use the preference recognition model to identify the target user's vehicle preference characteristics based on the client's corresponding historical behavior data;

[0175] The first determination module is configured to determine at least one target vehicle according to a vehicle preference feature.

[0176] Figure 4 The vehicle recommendation device can perform Figure 2 The implementation principle and technical effects of the vehicle recommendation method of the illustrated embodiment will not be elaborated here. The specific manner in which each module and unit performs operations in the vehicle recommendation device of the above embodiment has been described in detail in the embodiment of the method and will not be elaborated here.

[0177] Figure 5 The block diagram of a vehicle recommendation device provided in an embodiment of the present application is shown. The device can be applied to a client, such as Figure 5 As shown, the device may include:

[0178] The first display module 501 is used to display a vehicle search page in response to a vehicle search request from a target user;

[0179] The second acquisition module 502 is used to acquire vehicle search information input by the target user based on the vehicle search page;

[0180] The first sending module 503 is configured to send the vehicle search information to the server so that the server can identify at least one search keyword from the vehicle search information; obtain a vehicle preference feature of the target user, the vehicle preference feature being obtained by identifying the target user based on the historical behavior data corresponding to the client; and determine at least one target vehicle based on the at least one search keyword and the vehicle preference feature.

[0181] The second receiving module 504 is configured to receive vehicle recommendation information of at least one target vehicle sent by the server;

[0182] The second display module 505 is used to display at least one vehicle recommendation information.

[0183] Figure 5 The vehicle recommendation device can perform Figure 3 The implementation principle and technical effects of the vehicle recommendation method described in the illustrated embodiment will not be elaborated here. The specific manner in which each module and unit performs operations in the vehicle recommendation device in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated here.

[0184] Figure 6 This is a schematic diagram of the structure of an embodiment of a computing device provided by this application. Figure 6 As shown, in practice, the computing device may include: a storage component 601 and a processing component 602 .

[0185] The storage component 601 is used to store computer programs and can be configured to store various other data to support operations on the computing device. Examples of such data include instructions for any application or method operating on the computing device, data structures, contact data, phone book data, messages, images, videos, etc.

[0186] The processing component 602 is coupled to the storage component 601 and is used to execute the computer program in the storage component 601 to implement the following Figure 2 The vehicle recommendation method shown, or, to achieve Figure 3 Recommended method for the vehicle shown.

[0187] The processing component includes one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component can also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0188] The above-mentioned storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0189] The communication component is configured to facilitate wired or wireless communication between the device in which the communication component resides and other devices. The device in which the communication component resides may access a wireless network based on a communication standard, such as a mobile communication network, or a combination thereof. In an exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel.

[0190] The display assembly may include a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.

[0191] The power supply assembly provides power to various components of the device in which the power supply assembly is located. The power supply assembly may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply assembly is located.

[0192] The above-mentioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as call mode, recording mode, and voice recognition mode, the microphone is configured to receive external audio signals. The received audio signal can be further stored in a memory or sent via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0193] Accordingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the steps in the above method embodiment. The computer-readable storage medium includes volatile or non-volatile or a combination thereof, and may be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic cassette, tape disk storage or other magnetic storage device or any other non-transmission medium.

[0194] Accordingly, an embodiment of the present application further provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, the processor is enabled to implement the steps in the above-mentioned method embodiment. It should be understood that each process or a combination of multiple processes in the above-mentioned method flow can be implemented by a computer program or instruction. In addition, these computer programs or instructions can be applied to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device, so that the processor of the general-purpose computer, the special-purpose computer, the embedded processor or other programmable data processing device can be implemented as a device for implementing the corresponding functions in the above-mentioned method embodiment.

[0195] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0196] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0197] Finally, it should be noted that the above are merely examples of the present application and are not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. A vehicle recommendation method, characterized in that: Applied to the server, the method includes: receiving vehicle search information sent by a client, where the vehicle search information is obtained by the client in response to an input operation of a target user; identifying at least one search keyword from the vehicle search information; Obtaining a vehicle preference feature of the target user, wherein the vehicle preference feature is obtained by identifying historical behavior data corresponding to the client; determining at least one target vehicle based on at least one search keyword and the vehicle preference feature; The vehicle recommendation information of the at least one target vehicle is sent to the client.

2. The method according to claim 1, characterized in that The identifying at least one search keyword from the vehicle search information includes: The vehicle search information is input into at least one keyword recognition model to obtain at least one search keyword by using the at least one keyword recognition model to identify the vehicle search information.

3. The method according to claim 1, characterized in that After determining at least one target vehicle, the method further includes: Acquire vehicle model description information of the at least one target vehicle from a vehicle model knowledge base; Based on the vehicle model description information of each target vehicle, vehicle recommendation information for each target vehicle is generated.

4. The method according to claim 1, wherein After receiving the vehicle search information sent by the client, the method further includes: performing intent recognition on the vehicle search information; If it is determined according to the intention recognition result that the vehicle search information is used for vehicle search, performing the step of identifying at least one search keyword from the vehicle search information; otherwise, generating guidance prompt information, the guidance prompt information being used to guide the target user to input vehicle search information; Sending the guidance prompt information to the client; The step of returning to the step of receiving the vehicle search information sent by the client is continued.

5. The method according to claim 1, wherein The method further comprises: receiving first search information sent by the client, where the first search information is used to search for at least one purchase link in a vehicle purchase process; Retrieving response information matching the first search information from a process knowledge base; The reply information is sent to the client.

6. The method according to claim 1, characterized in that After determining at least one target vehicle, the method further includes: Obtaining vehicle condition information and price information of the at least one target vehicle; The vehicle recommendation information is generated according to the vehicle condition information and the price information.

7. The method according to claim 1, characterized in that The determining of at least one target vehicle according to the at least one search keyword and the vehicle preference feature includes: Searching for at least one target vehicle data matching the at least one search keyword and the vehicle preference feature among a plurality of vehicle data recorded in a vehicle database, wherein the plurality of vehicle data are data uploaded by a vehicle merchant; The vehicles corresponding to the at least one piece of target vehicle data are determined as target vehicles.

8. The method according to claim 1, characterized in that The method further comprises: Identifying and obtaining the target user's vehicle preference characteristics based on the historical behavior data corresponding to the client using a preference recognition model; At least one target vehicle is determined based on the vehicle preference characteristics.

9. A vehicle recommendation method, characterized in that: Applied to a client, the method includes: In response to a target user's vehicle search request, display a vehicle search page; Acquiring vehicle search information input by the target user based on the vehicle search page; Sending the vehicle search information to a server so that the server identifies at least one search keyword from the vehicle search information; obtaining a vehicle preference characteristic of the target user, the vehicle preference characteristic being obtained by identifying historical behavior data corresponding to the client; and determining at least one target vehicle based on the at least one search keyword and the vehicle preference characteristic; receiving vehicle recommendation information of the at least one target vehicle sent by the server; At least one of the vehicle recommendation information is displayed.

10. A computing device, characterized in that including processing components and storage components; The storage component stores a computer program; the computer program is used to be called and executed by the processing component to implement the vehicle recommendation method according to any one of claims 1 to 8, or to implement the vehicle recommendation method according to claim 9.

11. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by the processing component, the vehicle recommendation method according to any one of claims 1 to 8 is implemented, or the vehicle recommendation method according to claim 9 is implemented.

12. A computer program product, characterized in that The method comprises a computer program or an instruction, which, when executed by a processing component, implements the vehicle recommendation method according to any one of claims 1 to 8, or implements the vehicle recommendation method according to claim 9.

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