Service recommendation method and apparatus, vehicle, and storage medium

Abstract

The application relates to the field of vehicle electronics and communication technology, in particular to a service recommendation method and device, a vehicle and a storage medium, wherein the method comprises the following steps: receiving a recommendation request of a current service to be recommended, wherein the recommendation request contains identification information of the current service to be recommended; obtaining target resistance days of the current service to be recommended from a preset identification information-resistance days relationship based on the identification information of the current service to be recommended, and judging whether actual days of the current service to be recommended from last time of recommendation are greater than the target resistance days; if the actual days are greater than the target resistance days, the current service to be recommended is recommended to a user, otherwise, the current service to be recommended is cancelled. Therefore, the problems that a vehicle terminal cannot utilize feedback of fewer users to timely adjust the frequency of different recommendations are solved, and the maximization of recommendation opportunities is ensured.

Description

Technical Field

[0001] This application relates to the fields of vehicle electronics and communication technology, and in particular to a service recommendation method, apparatus, vehicle, and storage medium. Background Technology

[0002] With the continuous development of big data technology and deep learning algorithms, recommendation technology, which is becoming increasingly mature on mobile devices, is gradually shifting to the automotive field, and more and more recommendation technologies are being applied to in-vehicle scenarios.

[0003] However, unlike mobile devices, in-vehicle recommendation services interact with users on in-vehicle terminals, which presents challenges such as low usage rates, high user demands, strong user feedback, and security requirements. The question of how to effectively utilize the limited number of user feedback opportunities, adjust the frequency of different recommendations in a timely manner, and maximize recommendation opportunities remains to be addressed. Summary of the Invention

[0004] This application provides a service recommendation method, apparatus, vehicle, and storage medium, which solves the problem that current vehicle terminals cannot utilize feedback from a small number of users to adjust the frequency of different recommendations in a timely manner, while ensuring the maximization of recommendation opportunities.

[0005] The first aspect of this application provides a service recommendation method, comprising the following steps: receiving a recommendation request for a currently recommended service, the recommendation request including identification information of the currently recommended service; based on the identification information of the currently recommended service, obtaining the target resistance days of the currently recommended service from a preset identification information-resistance days relationship, and determining whether the actual number of days since the last recommendation of the currently recommended service is greater than the target resistance days; if the actual number of days is greater than the target resistance days, then recommending the currently recommended service to the user; otherwise, canceling the currently recommended service.

[0006] Based on the above technical means, the problem that current in-vehicle terminals cannot utilize feedback from a small number of users to adjust the frequency of different recommendations in a timely manner has been solved, while ensuring the maximization of recommendation opportunities.

[0007] Furthermore, before obtaining the target resistance days of the currently recommended service from the preset identification information-resistance days relationship based on the identification information of the currently recommended service, the method further includes: obtaining identification information of at least one target recommended service; obtaining the user's feedback operations on each target recommended service within a preset time period based on the identification information of each target recommended service, and calculating the negative feedback probability of each target recommended service based on the feedback operations; obtaining the number of consecutive rejections of each target recommended service before the current time, and calculating the resistance days of each target recommended service based on the number of consecutive rejections; obtaining the final resistance days of each target recommended service based on the product of the negative feedback probability of each target recommended service and the resistance days of each target recommended service, and establishing the preset identification information-resistance days relationship based on the final resistance days of each target recommended service and the identification information of each target recommended service.

[0008] Based on the aforementioned technical means, by calculating the number of resistance days, the recommendation frequency of the recommended services can be adjusted for users, so that each recommended service has the greatest chance.

[0009] Further, the step of calculating the resistance days for each target recommendation service based on the number of consecutive rejections includes: calculating the resistance days for each target recommendation service based on a preset resistance days calculation formula, wherein the preset resistance days calculation formula is:

[0010]

[0011] Among them, D γ denoted as the number of resistance days, n as the maximum recommended interval number of days, R as the critical value coefficient for the number of resistance days, and x as the number of consecutive rejections before the current time.

[0012] Based on the aforementioned technical means, the resistance days for each recommended service are calculated using a preset resistance day count.

[0013] Furthermore, recommending the currently recommended service to the user includes: identifying the recommendation type of the currently recommended service; if the recommendation type is a display recommendation, then recommending it to the user via voice and / or pop-up window; if the recommendation type is a direct function enable or direct function disable recommendation, then generating a time window for receiving user feedback information while recommending the currently recommended service to the user.

[0014] Based on the aforementioned technical means, services can be recommended to users directly, via voice, or through pop-up windows, thereby improving the intelligence of the vehicle.

[0015] Furthermore, after recommending the currently recommended service to the user, the method further includes receiving new feedback information sent by the user based on the voice method, the pop-up method, or the time window; and updating the resistance days of the currently recommended service based on the new feedback information.

[0016] Based on the aforementioned technical means, users can provide feedback on the recommended services in different ways, thereby improving their vehicle usage experience.

[0017] A second aspect of this application provides a service recommendation method apparatus, comprising: a receiving module, configured to receive a recommendation request for a currently recommended service, the recommendation request including identification information of the currently recommended service; a judging module, configured to obtain a target resistance day number for the currently recommended service from a preset identification information-resistance day number relationship based on the identification information of the currently recommended service, and to judge whether the actual number of days since the last recommendation of the currently recommended service is greater than the target resistance day number; and a recommending module, configured to recommend the currently recommended service to the user if the actual number of days is greater than the target resistance day number, otherwise cancel the currently recommended service.

[0018] Further, before obtaining the target resistance days of the currently recommended service from the preset identification information-resistance days relationship based on the identification information of the currently recommended service, the judgment module is specifically used for: obtaining the identification information of at least one target recommended service; obtaining the user's feedback operation on each target recommended service within a preset time period based on the identification information of each target recommended service, and calculating the negative feedback probability of each target recommended service based on the feedback operation; obtaining the number of consecutive rejections of each target recommended service before the current time, and calculating the resistance days of each target recommended service based on the number of consecutive rejections; obtaining the final resistance days of each target recommended service based on the product of the negative feedback probability of each target recommended service and the resistance days of each target recommended service, and establishing the preset identification information-resistance days relationship based on the final resistance days of each target recommended service and the identification information of each target recommended service.

[0019] Further, the determination module, in calculating the resistance days for each target recommendation service based on the number of consecutive rejections, is specifically used to: calculate the resistance days for each target recommendation service based on a preset resistance days calculation formula, wherein the preset resistance days calculation formula is:

[0020]

[0021] Among them, D γdenoted as the number of resistance days, n as the maximum recommended interval number of days, R as the critical value coefficient for the number of resistance days, and x as the number of consecutive rejections before the current time.

[0022] Furthermore, the recommendation module is specifically used to: identify the recommendation type of the service to be recommended; if the recommendation type is a display recommendation, then recommend it to the user via voice and / or pop-up window; if the recommendation type is a direct function enable or direct function disable recommendation, then while recommending the service to be recommended to the user, generate a time window for receiving user feedback information.

[0023] Furthermore, after recommending the currently recommended service to the user, the recommendation module is also used to: receive new feedback information sent by the user based on the voice method, the pop-up method, or the time window; and update the resistance days of the currently recommended service according to the new feedback information.

[0024] A third aspect of this application provides a vehicle including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the service recommendation method as described in the above embodiments.

[0025] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the service recommendation method as described in the above embodiments.

[0026] Therefore, this application receives recommendation requests for services currently to be recommended, and based on the identification information of the services to be recommended, obtains the target resistance days of the services to be recommended from a preset identification information-resistance days relationship. It then determines whether the actual number of days since the last recommendation of the services to be recommended is greater than the target resistance days. If the actual number of days is greater than the target resistance days, the services to be recommended are recommended to the user; otherwise, the services to be recommended are canceled. This solves the problem that current in-vehicle terminals cannot utilize feedback from a small number of users to adjust the frequency of different recommendations in a timely manner, while maximizing recommendation opportunities.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 This is a flowchart of a service recommendation method provided according to an embodiment of this application;

[0030] Figure 2 This is a schematic diagram of the drag function curve and calculation results according to an embodiment of this application;

[0031] Figure 3 This is a schematic diagram of an implementation software structure according to an embodiment of this application;

[0032] Figure 4 This is a flowchart of a service recommendation method according to an embodiment of this application;

[0033] Figure 5 This is a block diagram of a service recommendation device according to an embodiment of this application;

[0034] Figure 6 This is a structural schematic diagram of a vehicle according to an embodiment of this application.

[0035] Explanation of reference numerals in the attached drawings: 10-Service recommendation device, 100-Receiving module, 200-Judgment module, 300-Recommendation module, 601-Memory, 602-Processor, 603-Communication interface. Detailed Implementation

[0036] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0037] The following description, with reference to the accompanying drawings, outlines a service recommendation method, apparatus, vehicle, and storage medium according to embodiments of this application. Addressing the issue mentioned in the background art where current in-vehicle terminals cannot promptly adjust the frequency of different recommendations based on feedback from a limited number of users, this application provides a service recommendation method. In this method, a recommendation request for a service to be recommended is received, the request containing identification information of the service to be recommended. Based on the identification information of the service to be recommended, a target resistance day number for the service to be recommended is obtained from a preset identification information-resistance day relationship. It is then determined whether the actual number of days since the last recommendation of the service to be recommended is greater than the target resistance day number. If the actual number of days is greater than the target resistance day number, the service to be recommended is recommended to the user; otherwise, the service to be recommended is canceled. This solves the problem that current in-vehicle terminals cannot promptly adjust the frequency of different recommendations based on feedback from a limited number of users, while maximizing recommendation opportunities.

[0038] Specifically, Figure 1 This is a flowchart illustrating a service recommendation method provided in an embodiment of this application.

[0039] like Figure 1 As shown, the service recommendation method includes the following steps:

[0040] In step S101, a recommendation request for the currently recommended service is received, and the recommendation request contains the identification information of the currently recommended service.

[0041] Specifically, the recommendation service registers its identification information, including recommendations for content, services, or applications, with the recommendation arbitration module. The recommendation arbitration module stores the information corresponding to the recommendation service locally, including: ID (i.e., identification information), in-window feedback, last recommendation time, and recommendation resistance days. Each recommendation service has unique identification information. When a recommendation service makes a recommendation to a user, it sends a recommendation request to the recommendation arbitration module. The recommendation request includes the identification information of the current recommendation service. The information table corresponding to the recommendation service is shown in Table 1.

[0042] Table 1

[0043] ID Feedback status in the window Last recommended time Recommended number of resistance days

[0044] In step S102, based on the identification information of the current service to be recommended, the target resistance days of the current service to be recommended are obtained from the preset identification information-resistance days relationship, and it is determined whether the actual number of days since the last recommendation of the current service to be recommended is greater than the target resistance days.

[0045] Optionally, in some embodiments, before obtaining the target resistance days of the currently recommended service from a preset identification information-resistance days relationship based on the identification information of the currently recommended service, the method further includes: obtaining identification information of at least one target recommended service; obtaining user feedback operations on each target recommended service within a preset time period based on the identification information of each target recommended service, and calculating the negative feedback probability of each target recommended service based on the feedback operations; obtaining the number of consecutive rejections of each target recommended service before the current time, and calculating the resistance days of each target recommended service based on the number of consecutive rejections; obtaining the final resistance days of each target recommended service based on the product of the negative feedback probability of each target recommended service and the resistance days of each target recommended service, and establishing a preset identification information-resistance days relationship based on the final resistance days of each target recommended service and the identification information of each target recommended service.

[0046] Specifically, the vehicle terminal is equipped with a monitoring window to obtain the number of user feedback operations on each recommended service within a preset time period, the number of negative feedback operations, the negative feedback probability of each target recommended service, and the number of times the user continuously rejects each target recommended service within the preset time period.

[0047] Furthermore, in some embodiments, calculating the resistance days for each target recommendation service based on the number of consecutive rejections includes: calculating the resistance days for each target recommendation service based on a preset resistance days calculation formula, wherein the preset resistance days calculation formula is:

[0048]

[0049] Among them, D γ denoted as the number of resistance days, n as the maximum recommended interval number of days, R as the critical value coefficient for the number of resistance days, and x as the number of consecutive rejections before the current time.

[0050] Understandably, as the number of consecutive rejections by a user increases, the preset number of resistance days rises. After reaching a critical value (i.e., when x approaches R), the rate of increase in the preset number of resistance days will decrease. Figure 2 As shown, the preset number of resistance days has a maximum value, which avoids the vehicle terminal from not pushing pushes at all. At the same time, the number of resistance days can be set according to the probability of individual feedback on the scenario, reflecting the personalization of resistance.

[0051] In step S103, if the actual number of days is greater than the target resistance number of days, the currently recommended service is recommended to the user; otherwise, the currently recommended service is canceled.

[0052] Understandably, when the vehicle system is running, the recommendation arbitration module determines whether the actual number of days between the last recommendation and the current recommendation has exceeded the target resistance days based on the target resistance days corresponding to the recommendation service. If it has exceeded the target resistance days, the arbitration determines that the recommendation can be given to the user; otherwise, the recommendation service is canceled.

[0053] Optionally, in some embodiments, recommending the currently recommended service to the user includes: identifying the recommendation type of the currently recommended service; if the recommendation type is a display recommendation, then recommending it to the user via voice and / or pop-up window; if the recommendation type is a direct function enable or direct function disable recommendation, then generating a time window for receiving user feedback information while recommending the currently recommended service to the user.

[0054] The recommendation types for the services currently being recommended include: display recommendations and recommendations that allow you to directly enable or disable the function.

[0055] Specifically, such as Figure 3As shown, the recommended arbitration module arbitrates services that can be recommended. After recommending a service to the user, it sends the user's feedback to the recommendation arbitration module. If the recommendation type is a display recommendation, the in-vehicle terminal can ask "Can you recommend a service?" through the voice system or recommend it to the user through a pop-up window on the screen. The user can choose to accept or reject the recommendation. If the recommended service is a function that directly enables or disables the recommendation service for the user, since it is impossible to obtain explicit feedback from the user, a time window for receiving user feedback information is set. Based on the user's actions within a time window after the recommendation service is recommended, it is determined whether the user accepts or rejects the recommendation. After receiving the user's feedback, the recommendation arbitration module updates the corresponding feedback information for the service and calculates the new resistance days for the recommended service.

[0056] Optionally, in some embodiments, after recommending the currently recommended service to the user, the method further includes receiving new feedback information sent by the user via voice, pop-up window, or time window; and updating the resistance days of the currently recommended service based on the new feedback information.

[0057] Specifically, after recommending the current service to the user, the user can provide feedback to the recommendation arbitration module by sending a voice message indicating "satisfied" or "dissatisfied," or by sending a pop-up message on the in-vehicle terminal display, or by accepting or rejecting the recommendation through a time window and sending the feedback to the recommendation arbitration module. The recommendation arbitration module updates and calculates the resistance days for the current service based on the new feedback information, and then re-arbitrates the service recommendation for the user.

[0058] To enable those skilled in the art to further understand the service recommendation method of the embodiments of this application, the following detailed description is provided in conjunction with specific embodiments, such as... Figure 4 As shown.

[0059] Step S401: The recommendation service registers with the recommendation arbitration module.

[0060] Step S402: The arbitration module is recommended to calculate the number of resistance days for each recommended service.

[0061] Step S403: The recommendation service initiates a recommendation request.

[0062] Step S404: The arbitration module determines whether the recommended resistance days are met. If the recommended resistance days are met, proceed to step S405; otherwise, proceed to step S403.

[0063] Step S405: The recommendation service performs the recommendation and sends the user feedback to the arbitration recommendation service.

[0064] In this embodiment of the application, the calculation of resistance can also be deployed in the cloud. The recommendation service uploads information to the cloud, calculates the number of recommendation resistance days for each recommendation service in the cloud, and sends the recommendation resistance days of all services to the recommendation arbitration module for deciding whether the recommendation can be executed.

[0065] Therefore, the service recommendation method proposed in this application receives a recommendation request for a service to be recommended, and based on the identification information of the service to be recommended, obtains the target resistance days of the service to be recommended from a preset identification information-resistance days relationship. It then determines whether the actual number of days since the last recommendation of the service to be recommended is greater than the target resistance days. If the actual number of days is greater than the target resistance days, the service to be recommended is recommended to the user; otherwise, the service to be recommended is canceled. This addresses the current problem of in-vehicle terminals being unable to utilize feedback from a limited number of users to adjust the frequency of different recommendations in a timely manner, while maximizing recommendation opportunities.

[0066] Next, the service recommendation apparatus proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0067] Figure 5 This is a block diagram of a service recommendation device according to an embodiment of this application.

[0068] like Figure 5 As shown, the service recommendation device 10 includes: a receiving module 100, a judging module 200, and a recommendation module 300.

[0069] The receiving module 100 is used to receive a recommendation request for a service to be recommended, the recommendation request containing the identification information of the service to be recommended; the judging module 200 is used to obtain the target resistance days of the service to be recommended from a preset identification information-resistance days relationship based on the identification information of the service to be recommended, and to judge whether the actual number of days since the last recommendation of the service to be recommended is greater than the target resistance days; the recommending module 300 is used to recommend the service to the user if the actual number of days is greater than the target resistance days, otherwise, cancel the service to be recommended.

[0070] Optionally, in some embodiments, before obtaining the target resistance days of the currently recommended service from a preset identification information-resistance days relationship based on the identification information of the currently recommended service, the determination module 200 is specifically used to: obtain the identification information of at least one target recommended service; based on the identification information of each target recommended service, obtain the user's feedback operations on each target recommended service within a preset time period, and calculate the negative feedback probability of each target recommended service based on the feedback operations; obtain the number of consecutive rejections of each target recommended service before the current time, and calculate the resistance days of each target recommended service based on the number of consecutive rejections; obtain the final resistance days of each target recommended service based on the product of the negative feedback probability of each target recommended service and the resistance days of each target recommended service, and establish a preset identification information-resistance days relationship based on the final resistance days of each target recommended service and the identification information of each target recommended service.

[0071] Optionally, in some embodiments, the determination module 200 calculates the resistance days for each target recommendation service based on the number of consecutive rejections. Specifically, the determination module 200 is used to: calculate the resistance days for each target recommendation service based on a preset resistance days calculation formula, wherein the preset resistance days calculation formula is:

[0072]

[0073] Among them, D γ denoted as the number of resistance days, n as the maximum recommended interval number of days, R as the critical value coefficient for the number of resistance days, and x as the number of consecutive rejections before the current time.

[0074] Optionally, in some embodiments, the recommendation module 300 is specifically used to: identify the recommendation type of the service to be recommended; if the recommendation type is a display recommendation, then recommend it to the user via voice and / or pop-up window; if the recommendation type is a direct function enable or direct function disable recommendation, then generate a time window for receiving user feedback information while recommending the service to be recommended to the user.

[0075] Optionally, in some embodiments, after recommending the current service to be recommended to the user, the recommendation module 300 is further configured to: receive new feedback information sent by the user via voice, pop-up window, or time window; and update the resistance days of the current service to be recommended based on the new feedback information.

[0076] It should be noted that the foregoing explanation of the service recommendation method embodiment also applies to the service recommendation device of this embodiment, and will not be repeated here.

[0077] The service recommendation device proposed in this application receives a recommendation request for a currently recommended service. Based on the identification information of the currently recommended service, it obtains the target resistance days of the currently recommended service from a preset identification information-resistance days relationship. It then determines whether the actual number of days since the last recommendation of the currently recommended service is greater than the target resistance days. If the actual number of days is greater than the target resistance days, the currently recommended service is recommended to the user; otherwise, the currently recommended service is canceled. Therefore, this addresses the current problem of in-vehicle terminals being unable to utilize feedback from a limited number of users to adjust the frequency of different recommendations in a timely manner, while maximizing recommendation opportunities.

[0078] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include:

[0079] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0080] When the processor 602 executes the program, it implements the service recommendation method provided in the above embodiments.

[0081] Furthermore, the vehicle also includes:

[0082] Communication interface 603 is used for communication between memory 601 and processor 602.

[0083] The memory 601 is used to store computer programs that can run on the processor 602.

[0084] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0085] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0086] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0087] The processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0088] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the service recommendation method described above.

[0089] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0090] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0091] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0092] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0094] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A service recommendation method, characterized in that, Includes the following steps: Receive a recommendation request for a service currently to be recommended, wherein the recommendation request contains the identification information of the service currently to be recommended; Based on the identification information of the current service to be recommended, the target resistance days of the current service to be recommended are obtained from the preset identification information-resistance days relationship, and it is determined whether the actual number of days since the last recommendation of the current service to be recommended is greater than the target resistance days. as well as If the actual number of days is greater than the target resistance number of days, then the currently recommended service will be recommended to the user; otherwise, the currently recommended service will be cancelled. Before obtaining the target resistance days of the currently recommended service from the preset identification information-resistance days relationship based on the identification information of the currently recommended service, the method further includes: Obtain the identification information of at least one target recommendation service; Based on the identification information of each target recommendation service, obtain the user's feedback operations on each target recommendation service within a preset time period, and calculate the negative feedback probability of each target recommendation service based on the feedback operations; Obtain the number of consecutive rejections for each target recommendation service up to the current time, and calculate the number of resistance days for each target recommendation service based on the number of consecutive rejections; The final resistance days of each target recommendation service are obtained by multiplying the negative feedback probability of each target recommendation service and the resistance days of each target recommendation service, and the preset identification information-resistance days relationship is established based on the final resistance days of each target recommendation service and the identification information of each target recommendation service. The calculation of the resistance days for each target recommendation service based on the number of consecutive rejections includes: Based on a preset formula for calculating the number of resistance days, the number of resistance days for each target recommendation service is calculated according to the number of consecutive rejections, wherein the preset formula for calculating the number of resistance days is: ； in, Where n is the recommended maximum interval in days, and R is the critical value coefficient for the number of days of resistance. This represents the number of consecutive rejections before the current moment.

2. The method according to claim 1, characterized in that, The step of recommending the currently recommended service to the user includes: Identify the recommendation type of the service currently to be recommended; If the recommendation type is a display recommendation, it will be recommended to the user via voice and / or pop-up window. If the recommendation type is to directly enable or disable the function, then while recommending the currently recommended service to the user, a time window for receiving user feedback information is generated.

3. The method according to claim 2, characterized in that, After recommending the currently recommended service to the user, the process also includes: Receive new feedback information sent by the user based on the voice method, the pop-up method, or the time window; The resistance days for the currently recommended service are updated based on the new feedback information.

4. A service recommendation device, characterized in that, include: The receiving module is used to receive a recommendation request for a service to be recommended, wherein the recommendation request contains the identification information of the service to be recommended. The judgment module is used to obtain the target resistance days of the current service to be recommended from the preset identification information-resistance days relationship based on the identification information of the current service to be recommended, and to determine whether the actual number of days since the last recommendation of the current service to be recommended is greater than the target resistance days. as well as The recommendation module is used to recommend the currently recommended service to the user if the actual number of days is greater than the target resistance number of days; otherwise, it cancels the currently recommended service. Before obtaining the target resistance days of the currently recommended service from the preset identification information-resistance days relationship based on the identification information of the currently recommended service, the judgment module is specifically used for: Obtain the identification information of at least one target recommendation service; Based on the identification information of each target recommendation service, obtain the user's feedback operations on each target recommendation service within a preset time period, and calculate the negative feedback probability of each target recommendation service based on the feedback operations; Obtain the number of consecutive rejections for each target recommendation service up to the current time, and calculate the number of resistance days for each target recommendation service based on the number of consecutive rejections; The final resistance days of each target recommendation service are obtained by multiplying the negative feedback probability of each target recommendation service and the resistance days of each target recommendation service, and the preset identification information-resistance days relationship is established based on the final resistance days of each target recommendation service and the identification information of each target recommendation service. The step of calculating the resistance days for each target recommendation service based on the number of consecutive rejections, specifically the judgment module, is used for: Based on a preset formula for calculating the number of resistance days, the number of resistance days for each target recommendation service is calculated according to the number of consecutive rejections, wherein the preset formula for calculating the number of resistance days is: ； in, Where n is the recommended maximum interval in days, and R is the critical value coefficient for the number of days of resistance. This represents the number of consecutive rejections before the current moment.

5. A vehicle, characterized in that, Including memory and processor; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the service recommendation method as described in any one of claims 1-3.

6. A computer-readable storage medium storing a computer program, characterized in that, When executed by the processor, the program implements the service recommendation method as described in any one of claims 1-3.

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