Scenic spot recommendation method, device and equipment, storage medium and vehicle

Abstract

The invention discloses a scenic spot recommendation method and device, equipment, a storage medium and a vehicle, and the method comprises the steps: obtaining a target path of a user, determining alternative scenic spots meeting the touring preference of the user in scenic spots through which the target path passes based on the touring preference of the user, taking at least one scenic spot in the alternative scenic spots as a target scenic spot, and recommending the target scenic spot to the user. According to the embodiment of the invention, the scenic spots conforming to the touring preference of the user are automatically recommended for the user based on the target path set by the user, so that the user can quickly select the clock-in point from the recommended scenic spots, the efficiency of setting the clock-in point is effectively improved, and the time of setting the clock-in point is shortened.

Description

Technical Field

[0001] This application belongs to the technical field of scenic spot recommendation, and particularly relates to a scenic spot recommendation method, device, equipment, storage medium and vehicle. Background Art

[0002] With the increasing popularity of cars, more and more people choose self-driving tours when traveling. Usually, during a self-driving tour, multiple scenic spots may be passed by. Users can usually select scenic spots that meet their preferences from numerous scenic spots as check-in points for itinerary planning, where a check-in point refers to a place that the user wants to visit. However, when users select check-in points by themselves, they first need to search for the scenic spots that may be passed by on the self-driving tour route by themselves, and then screen out the check-in points that meet their preferences by sequentially viewing the features of each scenic spot. The whole process takes too long.

[0003] In the existing related technologies, in order to reduce the time consumed by users to determine check-in points, scenic spots are usually recommended to users, so that users can select check-in points from the recommended scenic spots, thereby reducing the time consumed to determine check-in points. However, the existing scenic spot recommendation methods generally only recommend based on the location information or click popularity of the scenic spots themselves, and the recommendation accuracy is relatively low, so that users still need to spend a long time screening check-in points. Summary of the Invention

[0004] The embodiments of this application provide a scenic spot recommendation method, device, equipment, storage medium and vehicle, which can automatically recommend scenic spots that meet the user's tour preferences to the user based on the target path set by the user, thereby shortening the time for the user to set check-in points.

[0005] In a first aspect, the embodiments of this application provide a scenic spot recommendation method, including:

[0006] Obtain the target path of the user;

[0007] Based on the user's tour preferences, determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path;

[0008] Recommend at least one of the alternative scenic spots to the user as the target scenic spot.

[0009] In a second aspect, the embodiments of this application provide a scenic spot recommendation device, including:

[0010] A path acquisition module, configured to obtain the target path of the user;

[0011] An alternative scenic spot determination module, configured to determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences;

[0012] A recommendation module for recommending at least one scenic spot from the alternative scenic spots to a user as a target scenic spot.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, which includes: a processor and a memory storing computer program instructions;

[0014] When the processor executes the computer program instructions, the steps of the scenic spot recommendation method as in the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the steps of the scenic spot recommendation method as in the first aspect are implemented.

[0016] In a fifth aspect, an embodiment of the present application provides a vehicle, including the scenic spot recommendation device as in the second aspect.

[0017] For the scenic spot recommendation method, device, equipment, storage medium and vehicle in the embodiments of the present application, the target path of the user is obtained, and based on the user's tour preferences, alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path are determined. At least one scenic spot from the alternative scenic spots is used as a target scenic spot and recommended to the user. According to the embodiments of the present application, scenic spots that meet the user's tour preferences are automatically recommended to the user based on the user's target path, which facilitates the user to quickly select check-in points from the recommended scenic spots, thereby effectively improving the efficiency of setting check-in points and shortening the time for setting check-in points. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the scenic spot recommendation method provided by an embodiment of the present application;

[0020] Figure 2 It is a logical diagram of the scenic spot recommendation method in a certain scenario provided by an embodiment of the present application;

[0021] Figure 3 It is a structural diagram of the scenic spot recommendation device provided by an embodiment of the present application;

[0022] Figure 4 It is a structural diagram of the electronic device provided by an embodiment of the present application. Detailed Embodiments

[0023] In order to more clearly understand the above objects, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0024] Many specific details are set forth in the following description in order to provide a thorough understanding of the present disclosure, but the present disclosure may be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all the embodiments.

[0025] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the presence of additional identical elements in the process, method, article or device comprising the said elements.

[0026] See Figure 1 , which is a schematic flowchart of a scenic spot recommendation method provided by an embodiment of the present application. As Figure 1 shown, the method may include the following steps S11-S13.

[0027] S11 Obtain the target path of the user.

[0028] In some embodiments of the present application, the scenic spot recommendation method provided by the present application may be applied to a self-driving tour scenario and executed by the in-vehicle entertainment system HU of the vehicle. When a user is on a self-driving tour, the navigation system is usually turned on, and path information such as a starting point, an ending point, and waypoints is set in the navigation system. The navigation system can generate a corresponding path based on the path information set by the user. Based on this, in the self-driving tour scenario, in S11, the path set by the user can be determined from the vehicle's navigation system, and the path set by the user can be used as the target path.

[0029] In some embodiments of the present application, the scenic spot recommendation method provided by the present application can be applied to scenarios where users travel on foot or by public transportation, and is executed by electronic devices such as mobile phones, smart watches, and tablet computers of users. When a user travels on foot or by public transportation, the user can set path information such as a starting point, an ending point, and waypoints in the map application of the electronic device, and the map application can generate a corresponding path based on the path information set by the user. Based on this, in the scenario where the user travels on foot or by public transportation, in S11, the path set by the user can be obtained from the map application of the electronic device, and the path set by the user can be used as the target path.

[0030] The following takes the application of the scenic spot recommendation method provided by the present application to a self-driving tour scenario as an example for illustration.

[0031] S12. Based on the user's tour preferences, determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path.

[0032] In some embodiments of the present application, the target path may pass through multiple scenic spots, that is, the vehicle may pass through multiple scenic spots during the process of driving along the target path. In order to meet the user's tour needs, the user can visit the scenic spots passed by, but if each scenic spot is visited, it will take too much physical strength and time. Therefore, the user usually only needs to visit the scenic spots that meet their own preferences, that is, the tour preferences. Based on this, alternative scenic spots that meet the user's tour preferences can be selected from the scenic spots passed by the target path based on the user's tour preferences.

[0033] As a possible implementation, the user's tour preferences can be determined based on the user's historical travel data, and then alternative scenic spots that meet the user's tour preferences can be selected from the scenic spots passed by the target path.

[0034] In some embodiments of the present application, before executing S12, the user's historical travel data can be obtained first. The historical travel data may include, but is not limited to: path information of historical paths, scenic spots corresponding to historical paths, the satisfaction degree of the user with each scenic spot corresponding to historical paths, etc. Among them, the historical path may include the path planned in the vehicle navigation system when the user travels historically, and the path information may include start and end point information of the path and / or waypoint information of the path. The start and end point information includes start point information and end point information.

[0035] For the convenience of description, the start and end points and waypoints are collectively referred to as points of interest below. The information of each point of interest in the path information can include attribute information such as name, category, longitude, and dimension. Among them, the category is used to indicate whether the point of interest is a scenic area, a service area, a downtown area, etc.

[0036] After obtaining the user's historical travel data, model training can be performed based on the user's historical travel data to obtain a scenic spot prediction model that can screen out scenic spots that meet the user's tour preferences from the path information of the path, and then select alternative scenic spots that meet the user's tour preferences from the scenic spots passed by the target path based on the scenic spot prediction model.

[0037] In some embodiments of the present application, the following method can be used to train the scenic spot prediction model:

[0038] Determine the initial model. The initial model can be a machine learning model, including but not limited to a neural network model;

[0039] Obtain multiple historical travel data of the user;

[0040] Construct multiple training data based on the obtained multiple historical travel data. Each set of training data includes the path information of a historical path, the scenic spots corresponding to the historical path, and the user's satisfaction level with each scenic spot;

[0041] Train the initial model based on the constructed multiple training data. During the training process, input the path information of the historical path in the training data into the initial model as the input quantity to obtain the scenic spots output by the initial model. Determine whether the user's satisfaction level with the scenic spots output by the initial model meets the preset satisfaction requirement based on the scenic spots corresponding to the historical path in the training data and the user's satisfaction level with each scenic spot. If not, adjust the parameters of the initial model and then continue to train the initial model using the training data until the user's satisfaction level with the scenic spots output by the initial model meets the preset satisfaction requirement, and then stop the training;

[0042] Use the initial model obtained after the training stops as the scenic spot prediction model.

[0043] In some embodiments of the present application, in the training data, the user's satisfaction level with the scenic spots can be divided into two types: satisfied and dissatisfied. Based on this, determining whether the user's satisfaction level with the scenic spots output by the initial model meets the preset satisfaction requirement based on the scenic spots corresponding to the historical path in the training data and the user's satisfaction level with each scenic spot can include:

[0044] Determine whether the scenic spots output by the initial model include the scenic spots with a dissatisfied satisfaction level corresponding to the training data. If so, determine that the user's satisfaction level with the scenic spots output by the initial model does not meet the preset satisfaction requirement;

[0045] If not, it can be determined that the user's satisfaction level with the scenic spots output by the initial model meets the preset satisfaction requirement.

[0046] In the above manner, the scenic spot prediction model can predict the scenic spots that meet the user's tour preferences based on the path information of the path.

[0047] In some embodiments of the present application, the trained scenic spot prediction model can be stored at a specified location so that when it is necessary to use the scenic spot prediction model to predict the alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path, the scenic spot prediction model can be directly called from the specified location. Based on this, in S12, determining the alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences may include the following steps:

[0048] Extract the path information of the target path, where the path information includes at least one of the following: start and end point information, passing point information;

[0049] Use the preset scenic spot prediction model to predict the scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the path information of the target path to obtain alternative scenic spots.

[0050] In some embodiments of the present application, the path information of the target path can be directly extracted from the navigation map to which the target path belongs.

[0051] Using the scenic spot prediction model to predict the scenic spots that meet the user's tour preferences among the scenic spots passed by the target path has high accuracy, strong reliability, and high efficiency, and can quickly and accurately predict the scenic spots that meet the user's tour preferences.

[0052] S13. Recommend at least one of the scenic spots to the user as the target scenic spot.

[0053] In some embodiments of the present application, multiple alternative scenic spots may be determined in S12. When recommending scenic spots to the user, some or all of the scenic spots may be used as the target scenic spots to be recommended to the user, and then the target scenic spots are recommended to the user.

[0054] As a possible implementation manner, when there are multiple alternative scenic spots, the degree of preference of the user for each alternative scenic spot can be determined based on the user's historical tour data, and then the scenic spots whose corresponding degree of preference meets the preset requirements are selected from the alternative scenic spots as the target scenic spots.

[0055] In some embodiments of the present application, before performing S13, historical browsing data of the user may be obtained first. The historical browsing data may include, but is not limited to, the scenic spots visited by the user and the browsing information of the user at the scenic spot. The browsing information is used to represent the degree of preference of the user for the scenic spot. The browsing information may include at least one of the following: stay time, evaluation score, etc. Then, based on the historical browsing data of the user, model training is performed to obtain a scenic spot preference prediction model that can predict the degree of preference of the user for the scenic spot. Furthermore, based on the scenic spot preference prediction model, the degree of preference of the user for each scenic spot in the alternative scenic spots is predicted, so as to select the target scenic spot corresponding to the degree of preference that meets the preset requirements from the alternative scenic spots.

[0056] In some embodiments of the present application, the following method may be used to train the scenic spot preference prediction model:

[0057] Determine an initial model. The initial model may be a machine learning model, including but not limited to a neural network model;

[0058] Obtain multiple historical browsing data of the user;

[0059] Based on the obtained multiple historical browsing data, multiple training data are constructed. Each group of training data includes a scenic spot visited by the user and the preference weight of the scenic spot determined based on the browsing information of the user at the scenic spot. The preference weight is used to indicate the degree of preference of the user for the scenic spot. The greater the preference weight, the higher the degree of preference of the user for the scenic spot;

[0060] Based on the constructed multiple training data, the initial model is trained. During the training process, the scenic spot in the training data is used as the input quantity and input into the initial model to obtain the preference weight output by the initial model. Based on the preference weight corresponding to the scenic spot in the training data, it is determined whether the accuracy of the preference weight output by the initial model meets the preset accuracy requirement. If not, after adjusting the parameters of the initial model, the initial model is continued to be trained using the training data until the accuracy of the preference weight output by the initial model meets the preset accuracy requirement, and the training is stopped;

[0061] The initial model obtained after the training stops is used as the scenic spot preference prediction model.

[0062] In some embodiments of the present application, the preference weight of the scenic spot included in the training data may be calculated based on a preset weight calculation rule. The weight calculation rule is used to calculate the corresponding preference weight based on the browsing information of the scenic spot. The specific weight calculation rule may be set according to the actual situation, and no specific limitation is made in this embodiment.

[0063] In some embodiments of the present application, the accuracy of the preference weights output by the initial model can be determined based on the similarity between the preference weights output by the initial model and the preference weights included in the training data. The higher the similarity, the higher the accuracy.

[0064] In the above manner, the scenic spot preference prediction model can predict, based on the scenic spots, the degree of user preference for the scenic spots.

[0065] In some embodiments of the present application, the trained scenic spot preference prediction model can be stored at a specified location so that when it is necessary to use the scenic spot preference prediction model to predict the preference weights of the user for the scenic spots, the scenic spot preference prediction model can be directly called from the specified location. Based on this, when selecting the target scenic spot from the alternative scenic spots in S13, the following steps can be included:

[0066] Use the preset scenic spot preference prediction model to predict the weights of the alternative scenic spots, and obtain the corresponding preference weights of each scenic spot in the alternative scenic spots. The preference weights are used to indicate the degree of user preference for the scenic spots. The greater the preference weight, the higher the degree of user preference for the scenic spot;

[0067] Select the N scenic spots with the highest preference weights from the alternative scenic spots as the target scenic spots, where N is an integer value greater than or equal to 1.

[0068] Among them, the value of N can be set by the user according to actual needs. For example, it can be any integer value between 5 and 20.

[0069] In some embodiments of the present application, after obtaining the target scenic spots, the target scenic spots can be recommended to the user by means of display on a display screen, message reminder, etc., so that the user can select the punching points based on the recommended target scenic spots.

[0070] As a possible implementation manner, in order to facilitate the user to quickly select the punching points from the target scenic spots, when the number of target scenic spots is multiple, when recommending the target scenic spots to the user, the following steps can be included:

[0071] Generate a recommendation list, and the multiple target scenic spots in the recommendation list are arranged in descending order of preference weight;

[0072] Recommend the recommendation list to the user.

[0073] By arranging the target scenic spots in descending order of preference weight, the user can preferentially see the scenic spots that best meet their preferences, which helps the user quickly select the punching points that meet their preferences from multiple target scenic spots.

[0074] The scenic spot recommendation method provided in this embodiment obtains the user's target path, determines alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences, and recommends at least one of the alternative scenic spots to the user as the target scenic spot. According to the embodiments of the present application, scenic spots that meet the user's tour preferences are automatically recommended to the user based on the target path set by the user, which facilitates the user to quickly select check-in points from the recommended scenic spots, thereby effectively improving the efficiency of setting check-in points and shortening the time for setting check-in points.

[0075] As a possible implementation manner, after recommending the target scenic spot to the user, the following steps may further be executed:

[0076] Receive the user's selection operation on the target scenic spot;

[0077] Determine the target scenic spot selected by the selection operation as the check-in point;

[0078] Add the check-in point to the target path.

[0079] In some embodiments of the present application, after recommending the target scenic spot to the user, the user can select the check-in point from the recommended target scenic spots based on actual needs. Based on the user's selection operation, the check-in point selected by the user is added to the target path set by the user, which is convenient for improving the user's itinerary planning.

[0080] Among them, adding the check-in point to the target path may include:

[0081] Obtain the location information of the check-in point;

[0082] Based on the location information of the check-in point, add a check-in mark at the corresponding position in the target path.

[0083] Among them, the check-in mark may include but is not limited to: text mark, picture mark, etc. For example, the check-in mark may include the check-in point text mark, and the check-in mark may also include the check-in point icon.

[0084] In this way, the check-in point can be more intuitively displayed to the user.

[0085] As a possible implementation manner, after receiving the user's selection operation on the target scenic spot, the following steps may further be executed:

[0086] Determine the satisfaction degree of the user with each scenic spot in the target scenic spot based on the selection operation, and store the scenic spot, the corresponding satisfaction degree of the scenic spot, and the target path correspondingly. Among them, for the scenic spot selected by the user, set the satisfaction degree of the scenic spot to satisfied, and for the scenic spot not selected by the user, set the satisfaction degree of the scenic spot to dissatisfied.

[0087] Further, new training data is generated based on the stored scenic spots, the satisfaction levels corresponding to the scenic spots, and the target path, and the new training data is used to train the scenic spot prediction model to improve the accuracy of the scenic spot prediction model.

[0088] As a possible implementation, in the self-driving tour scenario, after adding the check-in point to the target path, the following steps can also be executed:

[0089] Start timing when the vehicle reaches the check-in point location, and record the stay time of the vehicle at the check-in point location;

[0090] Receive the check-in point rating input by the user in the in-vehicle entertainment system HU;

[0091] Take the recorded stay time and the check-in point rating input by the user as the user's tour information at the check-in point;

[0092] Correspondingly store the check-in point and the user's tour information at the check-in point.

[0093] Further, new training data is constructed based on the stored check-in points and the user's tour information at the check-in points, and the scenic spot preference prediction model is trained based on the new training data to improve the accuracy of the scenic spot preference prediction model.

[0094] See Figure 2 for the logical schematic diagram of the scenic spot recommendation method in a certain application scenario. As shown in Figure 2 , when making a scenic spot recommendation in this scenario, obtain the path information of the target path set in the vehicle. The path information includes the relevant information of the starting point, the ending point, and the waypoints. Input the path information into the preset scenic spot prediction model to obtain the alternative scenic spots output by the scenic spot prediction model. Output the alternative scenic spots to the preset scenic spot preference prediction model to obtain the preference weights of each alternative scenic spot output by the scenic spot preference prediction model. Select the target scenic spot based on the preference weights of each alternative scenic spot, generate a recommendation list including the target scenic spot, recommend the recommendation list to the user, receive the selection operation of the user on the recommendation list, and add the target scenic spot selected by the user as a check-in point to the target path to improve the itinerary planning. Further, new training data is generated based on the user's selection operation, and the scenic spot prediction model is trained based on the new training data to improve the accuracy of the scenic spot prediction model. Further, obtain the user's tour information at the check-in point, generate new training data based on the tour information, and train the scenic spot preference prediction model based on the new training data to improve the accuracy of the scenic spot preference prediction model.

[0095] Based on the scenic spot recommendation method provided in the above embodiments, correspondingly, the present application also provides a specific implementation of the scenic spot recommendation device. Please refer to the following embodiments.

[0096] See Figure 3, the scenic spot recommendation device provided by the embodiment of the present application includes the following modules:

[0097] A path acquisition module 301, configured to acquire a target path of a user;

[0098] An alternative scenic spot determination module 302, configured to determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences;

[0099] A recommendation module 303, configured to recommend at least one scenic spot among the alternative scenic spots as a target scenic spot to the user.

[0100] The scenic spot recommendation device provided in this embodiment determines the target path set by the user, determines alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences, and recommends at least one scenic spot among the alternative scenic spots as a target scenic spot to the user. According to the embodiment of the present application, scenic spots that meet the user's tour preferences are automatically recommended to the user based on the target path set by the user, which is convenient for the user to quickly select check-in points from the recommended scenic spots, thereby effectively improving the efficiency of setting check-in points and shortening the time for setting check-in points.

[0101] As a possible implementation manner, the alternative scenic spot determination module 302 is specifically configured to:

[0102] Extract the path information of the target path, where the path information includes at least one of the following: start and end point information, passing point information;

[0103] Use a preset scenic spot prediction model to predict, based on the path information, the scenic spots that meet the user's tour preferences among the scenic spots passed by the target path, and obtain alternative scenic spots.

[0104] Among them, the scenic spot prediction model is trained based on the user's historical travel data, and the historical travel data includes the path information of the historical path, the scenic spots corresponding to the historical path, and the user's satisfaction with each scenic spot.

[0105] As a possible implementation manner, the recommendation module 303 is specifically configured to:

[0106] Use a preset scenic spot preference prediction model to predict the weights of the alternative scenic spots, and obtain the preference weights corresponding to each scenic spot among the alternative scenic spots. The preference weight is used to indicate the user's degree of preference for the scenic spot, and the greater the preference weight, the higher the user's degree of preference for the scenic spot;

[0107] Select the N scenic spots with the highest preference weights from the alternative scenic spots as the target scenic spots, where N is an integer greater than or equal to 1;

[0108] Recommend the target scenic spots to the user;

[0109] The scenic spot preference prediction model is trained based on the user's historical visit data, which includes the scenic spots visited by the user and the user's visit information at the scenic spot. The visit information is used to represent the user's preference degree for the scenic spot.

[0110] As a possible implementation, the visit information includes at least one of the following: stay time, evaluation score.

[0111] As a possible implementation, the recommendation module 303 is specifically configured to:

[0112] When the number of target scenic spots is multiple, generate a recommendation list, and the multiple target scenic spots in the recommendation list are arranged in descending order of preference weight;

[0113] Recommend the recommendation list to the user.

[0114] As a possible implementation, the device may further include: a trip planning module, which is used to:

[0115] After recommending the target scenic spot to the user, receive the user's selection operation on the target scenic spot;

[0116] Determine the target scenic spot selected by the selection operation as the check-in point;

[0117] Add the check-in point to the target route.

[0118] The scenic spot recommendation device provided by the embodiments of the present application can implement Figures 1 to 2 each process implemented by the method embodiments. To avoid repetition, it will not be elaborated here.

[0119] Figure 4 The schematic diagram of the hardware structure of the electronic device provided by the embodiments of the present application is shown.

[0120] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0121] Specifically, the above-mentioned processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0122] The memory 402 may include a mass storage for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 402 may include removable or non-removable (or fixed) media. Where appropriate, the memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 402 is a non-volatile solid-state memory.

[0123] The memory 402 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory 402 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it can perform the operations described in any of the above-described scenic spot recommendation methods of the embodiments.

[0124] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any of the above-described scenic spot recommendation methods of the embodiments.

[0125] In one example, the electronic device may further include a communication interface 403 and a bus 410. Among them, as Figure 4 shown, the processor 401, the memory 402, and the communication interface 403 are connected through the bus 410 and complete communication with each other.

[0126] The communication interface 403 is mainly used to implement communication between each module, device, unit, and / or device in the embodiments of the present application.

[0127] The bus 410 includes hardware, software, or both, and couples the components of the online data flow metering device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 410 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0128] In addition, in combination with the scenic spot recommendation method in the above embodiments, the embodiments of the present application can be implemented by providing a computer storage medium. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the scenic spot recommendation methods in the above embodiments is implemented.

[0129] It should be clear that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0130] The functional blocks shown in the above structure block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0131] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. That is to say, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0132] As described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowchart and / or block diagram, and the combination of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0133] As mentioned above, the above is only the specific implementation manner of this application. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, modules, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A scenic spot recommendation method, characterized in that, it includes: Obtain the user's target path; Based on the user's tour preferences, determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path; Recommend at least one of the alternative scenic spots as the target scenic spot to the user.

2. The method according to claim 1, characterized in that, The step of determining alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences includes: Extract the path information of the target path, and the path information includes at least one of the following: starting and ending point information, passing point information; Use a preset scenic spot prediction model to predict, based on the path information, the scenic spots that meet the user's tour preferences among the scenic spots passed by the target path, and obtain alternative scenic spots; Among them, the scenic spot prediction model is trained based on the user's historical travel data, and the historical travel data includes the path information of the historical path, the scenic spots corresponding to the historical path, and the user's satisfaction with each scenic spot.

3. The method according to claim 1, characterized in that, The step of recommending at least one of the alternative scenic spots as the target scenic spot to the user includes: Use a preset scenic spot preference prediction model to predict the weights of the alternative scenic spots, and obtain the preference weights corresponding to each scenic spot among the alternative scenic spots. The preference weight is used to indicate the user's degree of preference for the scenic spot, and the greater the preference weight, the higher the user's degree of preference for the scenic spot; Select the N scenic spots with the highest preference weights from the alternative scenic spots as the target scenic spots, where N is an integer greater than or equal to 1; Recommend the target scenic spots to the user; The scenic spot preference prediction model is trained based on the user's historical tour data, and the historical tour data includes the scenic spots visited by the user and the user's tour information at the scenic spot, and the tour information is used to represent the user's degree of preference for the scenic spot.

4. The method according to claim 3, characterized in that, The tour information includes at least one of the following: stay time, evaluation score.

5. The method according to claim 3, characterized in that, The step of recommending the target scenic spots to the user includes: In the case where the number of the target scenic spots is multiple, generate a recommendation list, and the multiple target scenic spots in the recommendation list are arranged in descending order of preference weights; Recommend the recommendation list to the user.

6. The method according to any one of claims 1-5, characterized in that, After recommending at least one of the alternative scenic spots as the target scenic spot to the user, the method further includes: Receive the user's selection operation on the target scenic spot; Determine the target scenic spot selected by the selection operation as the check-in point; Add the check-in point to the target path.

7. A scenic spot recommendation device, characterized in that, it includes: A path acquisition module for obtaining the user's target path; An alternative scenic spot determination module, configured to determine alternative scenic spots that meet the user's tour preferences among the scenic spots passed by the target path based on the user's tour preferences; A recommendation module, configured to recommend at least one of the alternative scenic spots as a target scenic spot to the user.

8. An electronic device, Characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the steps of the scenic spot recommendation method according to any one of claims 1-6 are implemented.

9. A computer-readable storage medium, Characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the steps of the scenic spot recommendation method according to any one of claims 1-6 are implemented.

10. A vehicle, Characterized in that, It includes the scenic spot recommendation device according to claim 7.

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