Position recommendation processing method and apparatus

Abstract

This specification provides a location recommendation processing method and apparatus. One location recommendation processing method includes: determining location points matching a user's location information within a target location area, and constructing a historical location point sequence for the user; determining candidate location points for the user within the target location area based on the location points and the historical location point sequence; calculating a recommendation score based on recommendation parameters and corresponding recommendation weights of the candidate location points, and selecting a recommended location point from the candidate location points based on the calculated recommendation score; reading the recommendation dataset associated with the recommended location point and recommending it to the user.

Description

[0001] This patent application is a divisional application of China, filed on March 25, 2021, with application number 2021103209042, entitled "Location Recommendation Processing Method and Apparatus". Technical Field

[0002] This document relates to the field of data processing technology, and in particular to a location recommendation processing method and apparatus. Background Technology

[0003] With the rapid development of mobile internet technology, users have installed an increasingly rich variety of applications on their mobile devices. Consequently, users are becoming more and more reliant on these mobile devices. This is evident in how users rely on these applications for route navigation during travel and for guidance and direction during sightseeing. Especially in situations with high traffic or large numbers of visitors, providing more personalized and accurate guidance and reminders based on the actual scenario to improve the user experience and increase user stickiness has become a key focus for service providers and application platforms. Summary of the Invention

[0004] This specification provides one or more embodiments of a location recommendation processing method. The method includes: determining location points matching a user's location information within a target location area, and constructing a historical location point sequence for the user; determining candidate location points for the user within the target location area based on the location points and the historical location point sequence; calculating a recommendation score based on recommendation parameters and corresponding recommendation weights of the candidate location points, and selecting recommended location points from the candidate location points based on the calculated recommendation scores; and reading the recommendation dataset associated with the recommended location points and recommending them to the user.

[0005] This specification provides one or more embodiments of a location recommendation processing apparatus, including: a location determination module configured to determine location points matching a user's location information within a target location area, and construct a historical location point sequence for the user; a candidate location point determination module configured to determine candidate location points for the user within the target location area based on the location points and the historical location point sequence; a recommended location point selection module configured to calculate a recommendation score based on recommendation parameters and corresponding recommendation weights of the candidate location points, and select recommended location points from the candidate location points based on the calculated recommendation score; and a location point data recommendation module configured to read a recommendation dataset associated with the recommended location points and recommend it to the user.

[0006] This specification provides one or more embodiments of a location recommendation processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: determine location points matching a user's location information within a target location area, and construct a sequence of the user's historical location points; determine candidate location points for the user within the target location area based on the location points and the historical location point sequence; calculate a recommendation score based on recommendation parameters and corresponding recommendation weights of the candidate location points, and select recommended location points from the candidate location points based on the calculated recommendation scores; and read a recommendation dataset associated with the recommended location points and recommend them to the user.

[0007] This specification provides one or more embodiments of a storage medium for storing computer-executable instructions that, when executed, perform the following process: determining location points matching a user's location information within a target location area and constructing a sequence of the user's historical location points; determining candidate location points for the user within the target location area based on the location points and the historical location point sequence; calculating a recommendation score based on recommendation parameters and corresponding recommendation weights of the candidate location points, and selecting a recommended location point from the candidate location points based on the calculated recommendation score; reading the recommendation dataset associated with the recommended location point and recommending it to the user. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart illustrating a location recommendation processing method provided in one or more embodiments of this specification;

[0010] Figure 2 A flowchart illustrating a location recommendation processing method applied to a scenic area recommendation scenario, provided in one or more embodiments of this specification;

[0011] Figure 3 A flowchart illustrating another location recommendation processing method for scenic area recommendation scenarios provided in one or more embodiments of this specification;

[0012] Figure 4 A schematic diagram of a location recommendation processing device provided in one or more embodiments of this specification;

[0013] Figure 5 This is a schematic diagram of a location recommendation processing device provided for one or more embodiments of this specification. Detailed Implementation

[0014] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0015] This specification provides an example of a location recommendation processing method:

[0016] Reference Figure 1 It shows a flowchart of a location recommendation processing method provided in this embodiment, with reference to... Figure 2 It shows a flowchart of a location recommendation processing method applied to a scenic spot recommendation scenario provided in this embodiment, with reference to... Figure 3 This embodiment illustrates a flowchart of another location recommendation processing method applied to scenic area recommendation scenarios.

[0017] Reference Figure 1 The location recommendation processing method provided in this embodiment specifically includes steps S102 to S108.

[0018] Step S102: Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence.

[0019] The location recommendation processing method provided in this embodiment determines the user's current location within the current location area based on the user's location information. It then determines candidate location points by combining this location with the user's historical location sequence. Furthermore, it calculates a recommendation score for each candidate location point based on its recommendation parameters and corresponding recommendation weights. This score serves as the final basis for recommending locations to the user. The method recommends location points to the user by pushing a recommendation dataset composed of streaming media data, activity data, and / or consumption data from the location points. This attracts users with diverse recommended content, increases user interest in the location points, enhances user terminal usage willingness and user experience, and increases foot traffic at the location points by combining location recommendations with consumption activities at the location points. Simultaneously, it enhances user spending enthusiasm at the location points by distributing consumption activity rewards to users.

[0020] The location points mentioned in this embodiment include Points of Interest (POIs) in the geographic information dimension, such as scenic spots, houses, shops, communities, bus stops, parks, etc. The target location area refers to the range of location areas composed of a set of location points that have certain commonalities in a specific dimension (such as commonalities in attributes, types, or business), such as a scenic area composed of multiple scenic spots, or a food street, food court, or urban food check-in area composed of multiple food stores.

[0021] Here, the locations that a user has visited or traversed within the target location area are referred to as historical location points. The historical location point sequence refers to the sequence of location points constructed from all historical location points that the user has visited or traversed within the target location area. For example, if the target location area is a scenic area composed of multiple attractions, then the historical location point sequence is a sequence of visited attractions constructed based on the attractions that the user has visited within the scenic area.

[0022] This embodiment uses a scenic area composed of multiple attractions as an example to specifically explain the attraction recommendation process within the scenic area. The implementation process of recommending location points within a target location area composed of other types of location points is similar to that of recommending attractions within a scenic area. Please refer to the specific implementation process of recommending attractions within a scenic area. This embodiment will not elaborate on it in detail.

[0023] In specific implementation, based on the acquired user location information, the location point matching the location information in the target location area is determined, that is, the user's current location point, and based on the user's historical location points in the target location area, a sequence of the user's historical location points in the target location area is constructed.

[0024] Before determining the location point matching the user's location information within the target location area, it is necessary to obtain the user's location information. In one optional implementation of this embodiment, the user's location information is collected by calling the location collection interface configured on the user terminal based on the access request submitted by the user through a third-party application installed on the user terminal. Alternatively, the user's location information can also be collected by calling the location collection interface based on the access request submitted by the user terminal after scanning the identifier code of the location recommendation service.

[0025] For example, when a user (tourist) opens an application installed on their mobile device within a scenic area and accesses the attraction recommendation service by clicking on the service page provided by the application, the system detects the user's click command submitted on the service page (such as a mini-program page, H5 page, or application page within the application) when accessing the attraction recommendation service, and collects the user's current location information through the mobile device's LBS (Location Based Services).

[0026] It should be noted that the location point recommendation for users within the target location area needs to be based on the recommendation dataset of each location point within the target location area. Therefore, before determining the location point that matches the user's location information within the target location area, or during the process of recommending location points for users within the target location area and before the recommendation scores of the candidate location points are calculated, the recommendation parameters of each location point within the target location area and the recommendation dataset associated with each location point need to be obtained.

[0027] In this embodiment, the recommended parameters for location points within the target location area include visitor flow data and / or time data. The visitor flow data is collected by invoking flow data collection devices configured within the target location area, such as sensors deployed at various scenic spots within the area to collect visitor flow data and calculate the visitor flow for each scenic spot. The time data includes the time required to travel from one location point within the target location area to another, such as the time required to travel from one scenic spot to the next within the scenic area.

[0028] The recommended dataset of location points within the target location area includes streaming media data, activity data, and / or consumption data. The process of determining the streaming media data includes: modeling the location points within the target location area and tagging the acquired streaming media data of the target location area; matching the modeled location points with the tags of the streaming media data to obtain the streaming media data associated with the modeled location points.

[0029] For example, offline processing is used to model the attractions within the scenic area in advance. The modeling process specifically includes: assuming the i-th attraction is represented by x... i The attractions at the entrance and exit of the scenic area are marked as x0 and x, respectively. n Among them, scenic spot x i Next list of accessible attractions i ={x m ,…,x n}, T i,j It refers to scenic spot x i To the attraction x jThe tour time is limited, and starting from attraction x0 at the entrance, multiple feasible tour routes can be planned to reach attraction x at the exit of the scenic area. n Finish;

[0030] After modeling each attraction within the scenic area, the system requests streaming media data from the streaming media platform. The platform returns streaming media data consisting of short video data and live stream source data for the scenic area. This data is then tagged, and the tagged data is matched with the attractions within the scenic area to obtain a set V consisting of short video data and live stream source data for each attraction. i The streaming media collection V i That is, scenic spot x i The associated streaming data set, namely attraction x i The associated recommendation dataset includes live streaming source data, which includes live streaming information from anchors broadcasting live in the scenic area, as well as live streaming information from the live streaming equipment configured in the scenic area.

[0031] The process of determining the activity data and / or the consumption data includes: acquiring the activity data and / or consumption data of consumption points within the target location area; matching the activity data and / or consumption data of the consumption points with the location points to obtain the activity data and / or consumption data associated with each location point.

[0032] For example, information on sales, accommodation, and / or store activities of each shop within the scenic area can be obtained from the scenic area management system. This information can then be matched with the attraction information to obtain the corresponding shop information, accommodation, and / or store activity information for each attraction. Alternatively, the sales, accommodation, and activity information of shops at each attraction can be directly obtained from the scenic area management system as activity data and / or consumption data for each attraction.

[0033] It should be noted that, in the process of determining the streaming media data, activity data, and / or consumption data of the location points within the target location area, the streaming media data, activity data, and / or consumption data of each attraction can also be modeled simultaneously during the modeling of attractions within the target location area. The modeled streaming media data, activity data, and / or consumption data can then be associated with the attractions and stored for later use in recommending location points to users.

[0034] Step S104: Based on the location point and the historical location point sequence, determine the candidate location points of the user within the target location area.

[0035] In this embodiment, determining the location point within the target location area that matches the location information based on the user's location information refers to the location point where the user is currently located within the target location area; the historical location point sequence refers to the location point sequence constructed from all historical location points that the user has visited or passed through within the target location area.

[0036] Based on this, determining the candidate location points of the user within the target area means determining the location points within the target location area that the user can go to. Specifically, the candidate location points do not include the user's current location point or the historical location points that the user has already passed in the historical location point sequence.

[0037] Specifically, the candidate location point can also be the next location point that the user can reach from the current location point, and the number of candidate location points is greater than or equal to 1. For example, by planning all tour routes from the entrance attraction to the exit attraction within the scenic area, and determining the tour route that passes through the attraction where the user is currently located, the next attraction of the current attraction in the determined tour route is the user's candidate attraction within the scenic area.

[0038] Step S106: Calculate the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points, and select a recommended location point from the candidate location points based on the calculated recommendation score.

[0039] As described above, the recommendation parameters include passenger flow data and / or time data, and the recommendation parameters and corresponding recommendation weights include the passenger flow weight corresponding to the passenger flow data and the time weight corresponding to the time data.

[0040] Based on this, a recommendation score is calculated according to the recommendation parameters and corresponding recommendation weights of the candidate locations. This means calculating the recommendation score of the candidate locations based on their passenger flow data and corresponding passenger flow weights, and their time data and corresponding time weights. Further, based on the calculated recommendation scores of the candidate locations, at least one candidate location with the highest recommendation score is selected as the recommended location.

[0041] For example, based on the visitor flow of user t at at least one candidate attraction within the scenic area and the time required for user t to travel from their current location to a candidate attraction, the following algorithm is used to calculate and select recommended attractions for user t:

[0042]

[0043] Among them, score i,j For user t, located at attraction x i Candidate attractions x jRecommended score, F t For user t's historical visit sequence, F t ={x m ,…,x n Next i For user t, in the scenic area, from attraction x i The next destination that can be reached from the starting point, i.e., destination x i The next attraction, C represents the visitor volume of the attraction. w represents the intersection of the next reachable attraction for user t and other attractions outside the historical visitor sequence within the scenic area. i Here, T represents the visitor flow weight corresponding to the visitor flow, and T represents the visit time from the current attraction to the next attraction. The time weight corresponding to the tour duration;

[0044] Calculate the recommended score for each candidate attraction. i,j After that, you can choose the recommended score. i,j The top three attractions or those with the highest recommendation scores are recommended to user t.

[0045] In addition, during the calculation of the recommendation scores for each candidate location, recommendation algorithms such as neural networks and collaborative filtering can be used to calculate the recommendation scores and select the recommended location points. For example, when using neural networks to calculate the recommendation scores, the recommendation parameters and recommendation weights of the candidate location points are input into a location recommendation model that has been pre-built and trained using a neural network algorithm to calculate the recommendation scores and output the recommendation scores of the candidate location points.

[0046] Step S108: Read the recommendation dataset associated with the recommended location point and recommend it to the user.

[0047] As described above, the recommended dataset for location points within the target location area includes streaming media data, activity data, and / or consumption data. The process for determining the streaming media data includes: modeling the location points within the target location area and tagging the acquired streaming media data of the target location area; matching the modeled location points with the tags of the streaming media data to obtain the streaming media data associated with the modeled location points. The process for determining the activity data and / or consumption data includes: acquiring the activity data and / or consumption data of consumption points within the target location area; matching the activity data and / or consumption data of the consumption points with the location points to obtain the activity data and / or consumption data associated with each location point.

[0048] Based on this, in this embodiment, reading the recommendation dataset associated with the recommended location point refers to reading a pre-acquired recommendation dataset composed of streaming media data, activity data, and / or consumption data associated with the recommended location point, and sending the read streaming media data, activity data, and / or consumption data to the user's terminal to achieve location point recommendation to the user. After receiving the recommendation dataset composed of streaming media data, activity data, and / or consumption data, the user terminal plays or displays the streaming media data, activity data, and / or consumption data to the user, allowing the user to select the desired location point by browsing the streaming media data, activity data, and / or consumption data.

[0049] In specific implementation, after reading the recommendation dataset associated with the recommended location points and recommending it to the user, if the user selects a corresponding location point as the desired destination from the recommended location points, to facilitate the user's journey to the destination, this embodiment provides an optional implementation method that uses AR (Augmented Reality) guidance to guide the user to the destination. The specific implementation process includes: obtaining the target location information of the destination based on the target location selected by the user from the recommended location points; performing path rendering based on the location information and the target location information; and displaying the path rendering result data by calling the AR component. The AR component is used to control the AR virtual animation display and short video playback stream on the user terminal side.

[0050] For example, after receiving streaming media and activity data from multiple recommended attractions, a user can choose one of them as their target attraction. After the user selects the target attraction, the system obtains the location information of the target attraction and the user's current location to construct a tour route. The system then uses an AR component to render directional markers of the constructed tour route in the real-world image captured by the image sensor on the user's terminal to guide the user to the target attraction.

[0051] Based on this, in order to increase the likelihood of users going to the recommended location and improve the interactivity of users during their journey in the target location area, thereby increasing the frequency of users accessing the user terminal, in an optional implementation of this embodiment, in addition to guiding users to the target location through AR guidance as described above, it is further necessary to detect whether the user's real-time location information is within the location range of the target location.

[0052] If so, it indicates that the user has reached the target location with the help of location point recommendation and AR guidance. Then, the reward information for the user reaching the target location through AR guidance is determined, and the user is issued a consumption discount voucher corresponding to the reward information, so as to increase the user's enthusiasm to go to the recommended location and use AR guidance. Alternatively, the activity data and / or consumption data associated with the target location are pushed to the user to facilitate the user's access to and participation in the consumption activities of the target location to be visited.

[0053] If not, it indicates that the user has not yet reached the target location or has not gone to the target location, and no action needs to be taken.

[0054] Furthermore, in practical applications, to enhance the user's enjoyment and interactivity while traveling within the target location area, such as increasing the fun of visiting a scenic spot, AR check-in materials can be set up at various attractions within the scenic area. Users can then participate in an "AR check-in" activity at these materials. This embodiment provides an optional implementation method where the "AR check-in" activity is specifically implemented as follows:

[0055] Obtain the material identification image of the AR material identification captured by the AR component on the user's user terminal, and record the user's AR check-in record based on the material identification image;

[0056] The multimedia data of the AR material identifier is sent to the user terminal; the user terminal then plays the multimedia data.

[0057] The recommended dataset is displayed after a trigger command is detected from the user in response to the displayed recommended access control after the multimedia data playback ends.

[0058] For example, after a user "checks in" at an AR material sign at a scenic spot, the user's "AR check-in" record is recorded, and an AR virtual animation of the current scenic spot is played on the user's terminal. After the animation ends, an entry button for accessing the scenic spot recommendation service is displayed. If the user triggers the entry button, the user's terminal will display the streaming media data, activity data, and consumption data associated with the current scenic spot.

[0059] Based on this, if a user selects a corresponding location from the recommended locations as their desired destination, in an optional implementation of this embodiment, AR guidance is used to guide the user to the target location to facilitate their journey. Specifically, if a confirmation command submitted by the user for the selected target location is detected, an image acquisition module is invoked to acquire image information; then, based on the acquired image information, an AR image guiding the user's journey from the selected location to the target location is rendered, and the rendered AR image guiding the user's journey is sent to the user's terminal for display.

[0060] Furthermore, in order to increase the likelihood of users going to the recommended location and improve the interactivity of users during their journey in the target location area, thereby increasing the frequency of users accessing their terminals, in an optional implementation of this embodiment, based on the above-mentioned AR-guided method to guide users to the target location, it is further necessary to detect whether the user's real-time location information is within the location range of the target location.

[0061] If so, it indicates that the user has reached the target location with the help of location point recommendation and AR guidance. Based on the AR check-in record, the reward information for the user's arrival at the target location is determined, and a consumption discount voucher corresponding to the reward information is issued to the user.

[0062] If not, it indicates that the user has not yet reached the target location or has not gone to the target location, and no action needs to be taken.

[0063] The following description uses the application of a location recommendation processing method provided in this embodiment in a scenic area recommendation scenario as an example to further illustrate the location recommendation processing method provided in this embodiment. (See also...) Figure 2 The location recommendation processing method applied to scenic spot recommendation scenarios specifically includes steps S202 to S224.

[0064] Step S202: Model the attractions within the scenic area and label the acquired streaming media data of the scenic area.

[0065] Step S204: Match the modeled attractions with the tags of the streaming media data to obtain the streaming media data associated with the modeled attractions.

[0066] Step S206: Match the activity data and / or consumption data of consumption points within the scenic area with the attractions to obtain the activity data and / or consumption data associated with each attraction.

[0067] Step S208: Construct and store a recommendation dataset associated with each attraction from the streaming media data, activity data and / or consumption data associated with each attraction.

[0068] Step S210: Based on the access request submitted by the user terminal after scanning the identification code of the location recommendation service, the location sensor is invoked to collect the user's location information.

[0069] Step S212: Determine the attractions that match the user's location information within the scenic area, and construct a sequence of attractions visited by the user.

[0070] Step S214: Based on the matched attractions and the sequence of visited attractions, determine at least one candidate attraction within the scenic area for the user.

[0071] Step S216: Calculate recommendation scores based on visitor flow data, browsing time, and corresponding weights of multiple candidate attractions, and select at least one recommended attraction from the multiple candidate attractions based on the calculated recommendation scores.

[0072] Step S218: Read at least one recommended attraction associated with a recommendation dataset and recommend it to the user.

[0073] Step S220: Obtain the target location information of the target attraction based on the target attraction selected by the user from at least one recommended attraction.

[0074] Step S222: Render the path based on the user's location information and the target location information, and display the path rendering result data by calling the AR component.

[0075] Step S224: Detect the user's location within the target attraction's area, determine the reward information for the target attraction, and issue the corresponding coupon to the user.

[0076] The following example uses another location recommendation processing method provided in this embodiment to illustrate the application of this method in a scenic area recommendation scenario. See [link to relevant documentation]. Figure 3 The location recommendation processing method applied to scenic spot recommendation scenarios specifically includes steps S302 to S318.

[0077] Step S302: Determine the attractions that match the user's location information within the scenic area, and construct a sequence of attractions visited by the user.

[0078] Step S304: Based on the matched attractions and the sequence of visited attractions, determine at least one candidate attraction within the scenic area for the user.

[0079] Step S306: Calculate recommendation scores based on visitor flow data, browsing time, and corresponding weights of multiple candidate attractions, and select at least one recommended attraction from the multiple candidate attractions based on the calculated recommendation scores.

[0080] Step S308: Read at least one recommended attraction associated with a recommendation dataset and recommend it to the user.

[0081] Step S310: Obtain the material identification image of the AR material identification collected by the AR component on the user's user terminal, and record the user's AR check-in record.

[0082] Step S312: Send multimedia data of AR material identification to the user terminal.

[0083] Correspondingly, the user terminal plays the received multimedia data, and after detecting that the user has submitted a trigger command to the displayed recommendation access control after the multimedia data playback ends, it displays a recommendation dataset associated with at least one recommended attraction.

[0084] Step S314: If a confirmation command submitted by the user for the selected target attraction is detected, the image acquisition module is invoked to acquire image information.

[0085] Step S316: Based on the collected image information, render an AR image guiding the route from the user's current location to the target location, and send the rendered AR image guiding the route to the user's terminal for display.

[0086] Step S318: Upon detecting the user's AR check-in operation at the target attraction, issue the user a discount voucher for the target attraction.

[0087] The following is an embodiment of a location recommendation processing device provided in this specification:

[0088] In the above embodiments, a location recommendation processing method is provided, and correspondingly, a location recommendation processing apparatus is also provided, which will be described below with reference to the accompanying drawings.

[0089] Reference Figure 4 The diagram shows a location recommendation processing device provided in this embodiment.

[0090] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0091] This embodiment provides a location recommendation processing device, including:

[0092] The location determination module 402 is configured to determine the location points that match the user's location information within the target location area, and to construct a sequence of the user's historical location points;

[0093] The candidate location point determination module 404 is configured to determine candidate location points of the user within the target location area based on the location points and the historical location point sequence.

[0094] The recommended location point selection module 406 is configured to calculate a recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points, and select a recommended location point from the candidate location points based on the calculated recommendation score.

[0095] The location point data recommendation module 406 is configured to read the recommendation dataset associated with the recommended location points and recommend them to the user.

[0096] Optionally, the recommendation dataset includes streaming media data; the streaming media data is determined by running the following module:

[0097] The streaming media data tagging module is configured to model the location points within the target location area and tag the acquired streaming media data within the target location area;

[0098] The streaming media data tagging module is configured to match the modeled location points with the tagging labels of the streaming media data to obtain the streaming media data associated with the modeled location points.

[0099] Optionally, the recommendation dataset may also include activity data and / or consumption data;

[0100] The activity data and / or the consumption data are determined by running the following modules:

[0101] The point-of-sale data acquisition module is configured to acquire activity data and / or consumption data of points of sale within the target location area;

[0102] The point-of-sale data matching module is configured to match the activity data and / or consumption data of the point of sale with the location point to obtain the activity data and / or consumption data associated with each location point.

[0103] Optionally, the location recommendation processing device further includes:

[0104] The module is configured to acquire the AR material identifier image of the AR material identifier collected by the user's user terminal calling the AR component, and record the user's AR check-in record based on the material identifier image;

[0105] The module is configured to send multimedia data of the AR material identifier to the user terminal; and the user terminal plays the multimedia data.

[0106] Optionally, the recommended dataset is displayed after a trigger command submitted by the user to the displayed recommended access control is detected after the multimedia data playback ends.

[0107] Optionally, the location recommendation processing device further includes:

[0108] The image information acquisition module is configured to acquire image information if a confirmation command submitted by the user for the selected target location is detected.

[0109] The AR image rendering module is configured to render a route-guided AR image from the location point to the target location point based on the collected image information, and to send the rendered route-guided AR image to the user's terminal for display.

[0110] Optionally, the location recommendation processing device further includes:

[0111] The location detection module is configured to detect whether the user's real-time location information is within the location range of the target location point;

[0112] If so, the consumer discount voucher issuance module is run; the consumer discount voucher issuance module is configured to determine the reward information for the user reaching the target location point based on the AR check-in record, and issue the consumer discount voucher corresponding to the reward information to the user.

[0113] Optionally, the target location area includes the scenic area where the user is located, and the location point includes attractions within the scenic area; the historical location point sequence includes a sequence of visited attractions constructed based on the attractions the user has visited within the scenic area.

[0114] The following is an embodiment of a location recommendation processing device provided in this specification:

[0115] Corresponding to the location recommendation processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a location recommendation processing device for executing the location recommendation processing method provided above. Figure 5 This is a schematic diagram of a location recommendation processing device provided for one or more embodiments of this specification.

[0116] This embodiment provides a location recommendation processing device, including:

[0117] like Figure 5As shown, the location recommendation processing device can vary considerably due to differences in configuration or performance. It may include one or more processors 501 and a memory 502, where one or more application programs or data may be stored. The memory 502 may be temporary or persistent storage. The application programs stored in the memory 502 may include one or more modules (not shown), each module including a series of computer-executable instructions from the location recommendation processing device. Furthermore, the processor 501 may be configured to communicate with the memory 502 and execute the series of computer-executable instructions stored in the memory 502 on the location recommendation processing device. The location recommendation processing device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, etc.

[0118] In one specific embodiment, the location recommendation processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the location recommendation processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0119] Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence;

[0120] Based on the location point and the historical location point sequence, candidate location points of the user within the target location area are determined;

[0121] A recommendation score is calculated based on the recommendation parameters and corresponding recommendation weights of the candidate locations, and a recommended location is selected from the candidate locations based on the calculated recommendation score.

[0122] Read the recommendation dataset associated with the recommended location points and recommend it to the user.

[0123] Optionally, the recommendation dataset includes streaming media data; the streaming media data is determined in the following manner:

[0124] Model the location points within the target location area and label the acquired streaming media data of the target location area;

[0125] The modeled location points are matched with the tags of the streaming media data to obtain the streaming media data associated with the modeled location points.

[0126] Optionally, the recommendation dataset may also include activity data and / or consumption data;

[0127] The activity data and / or the consumption data are determined in the following manner:

[0128] Acquire activity data and / or consumption data of consumer points within the target location area;

[0129] The activity data and / or consumption data of the consumption points are matched with the location points to obtain the activity data and / or consumption data associated with each location point.

[0130] Optionally, the recommended parameters include passenger flow data and / or time data;

[0131] Accordingly, the recommendation parameters and the corresponding recommendation weights include the passenger flow weights corresponding to the passenger flow data and / or the time weights corresponding to the time data;

[0132] The passenger flow data is collected by calling the flow collection device configured in the target location area.

[0133] Optionally, before executing the instruction to determine the location points matching the user's location information within the target location area and construct the user's historical location point sequence, the method further includes:

[0134] Based on the user's access request for location recommendation service submitted through a third-party application installed on the user terminal, the location sensor configured on the user terminal is invoked to collect the user's location information;

[0135] or,

[0136] Based on the access request submitted by the user terminal after scanning the identifier code of the location recommendation service, the location sensor is invoked to collect the user's location information.

[0137] Optionally, the step of calculating the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points includes:

[0138] The recommendation score for each candidate location is calculated based on the passenger flow data and corresponding passenger flow weights, as well as the time data and corresponding time weights.

[0139] Optionally, the step of calculating the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points includes:

[0140] The recommendation parameters and recommendation weights of the candidate locations are input into a pre-trained location recommendation model to calculate the recommendation score, and the recommendation score of the candidate locations is output.

[0141] Optionally, when the computer-executable instructions are executed, the method further includes:

[0142] Obtain the material identification image of the AR material identification captured by the AR component on the user's user terminal, and record the user's AR check-in record based on the material identification image;

[0143] The multimedia data of the AR material identifier is sent to the user terminal; the user terminal then plays the multimedia data.

[0144] Optionally, the recommended dataset is displayed after a trigger command submitted by the user to the displayed recommended access control is detected after the multimedia data playback ends.

[0145] Optionally, if a confirmation command submitted by the user for the selected target location is detected, the image acquisition module is invoked to acquire image information;

[0146] Based on the collected image information, a route guidance AR image is rendered from the location point to the target location point, and the rendered route guidance AR image is sent to the user's terminal for display.

[0147] Optionally, when the computer-executable instructions are executed, the method further includes:

[0148] Detect whether the user's real-time location information is within the location range of the target location point;

[0149] If so, the reward information for the user reaching the target location is determined based on the AR check-in record, and a consumption discount voucher corresponding to the reward information is issued to the user.

[0150] Optionally, after reading the recommendation dataset associated with the recommended location points and executing the recommendation instruction on the user, the method further includes:

[0151] Based on the target location selected by the user from the recommended location points, obtain the target location information of the target location;

[0152] Path rendering is performed based on the location information and the target location information, and the path rendering result data is displayed by calling the AR component.

[0153] Optionally, when the computer-executable instructions are executed, the method further includes:

[0154] Detect whether the user's real-time location information is within the location range of the target location point;

[0155] If so, determine the reward information for the user reaching the target location through AR guidance, and issue the corresponding consumption discount voucher to the user; or, push the activity data and / or consumption data associated with the target location to the user.

[0156] Optionally, the target location area includes the scenic area where the user is located, and the location point includes attractions within the scenic area; the historical location point sequence includes a sequence of visited attractions constructed based on the attractions the user has visited within the scenic area.

[0157] This specification provides an example of a storage medium as follows:

[0158] Corresponding to the location recommendation processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a storage medium.

[0159] The storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process:

[0160] Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence;

[0161] Based on the location point and the historical location point sequence, candidate location points of the user within the target location area are determined;

[0162] A recommendation score is calculated based on the recommendation parameters and corresponding recommendation weights of the candidate locations, and a recommended location is selected from the candidate locations based on the calculated recommendation score.

[0163] Read the recommendation dataset associated with the recommended location points and recommend it to the user.

[0164] Optionally, the recommended dataset includes streaming media data; the streaming media data is determined as follows: the location points within the target location area are modeled, and the obtained streaming media data of the target location area is labeled; the modeled location points are matched with the labeled tags of the streaming media data to obtain the streaming media data associated with the modeled location points.

[0165] Optionally, the recommendation dataset further includes activity data and / or consumption data; wherein the activity data and / or the consumption data are determined in the following manner:

[0166] Acquire activity data and / or consumption data of consumer points within the target location area;

[0167] The activity data and / or consumption data of the consumption points are matched with the location points to obtain the activity data and / or consumption data associated with each location point.

[0168] Optionally, the recommendation parameters include passenger flow data and / or time data; correspondingly, the recommendation parameters and their respective recommendation weights include the passenger flow weight corresponding to the passenger flow data and / or the time weight corresponding to the time data; wherein, the passenger flow data is collected by calling the traffic collection device configured in the target location area.

[0169] Optionally, before the process of determining the location points that match the user's location information within the target location area and constructing the user's historical location point sequence is executed, the method further includes:

[0170] Based on the user's access request for location recommendation service submitted through a third-party application installed on the user terminal, the location sensor configured on the user terminal is invoked to collect the user's location information;

[0171] or,

[0172] Based on the access request submitted by the user terminal after scanning the identifier code of the location recommendation service, the location sensor is invoked to collect the user's location information.

[0173] Optionally, the step of calculating the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points includes:

[0174] The recommendation score for each candidate location is calculated based on the passenger flow data and corresponding passenger flow weights, as well as the time data and corresponding time weights.

[0175] Optionally, the step of calculating the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points includes:

[0176] The recommendation parameters and recommendation weights of the candidate locations are input into a pre-trained location recommendation model to calculate the recommendation score, and the recommendation score of the candidate locations is output.

[0177] Optionally, the computer-executable instructions, when executed, also implement the following processes:

[0178] Obtain the material identification image of the AR material identification captured by the AR component on the user's user terminal, and record the user's AR check-in record based on the material identification image;

[0179] The multimedia data of the AR material identifier is sent to the user terminal; the user terminal then plays the multimedia data.

[0180] Optionally, the recommended dataset is displayed after a trigger command submitted by the user to the displayed recommended access control is detected after the multimedia data playback ends.

[0181] Optionally, if a confirmation command submitted by the user for the selected target location is detected, the image acquisition module is invoked to acquire image information;

[0182] Based on the collected image information, a route guidance AR image is rendered from the location point to the target location point, and the rendered route guidance AR image is sent to the user's terminal for display.

[0183] Optionally, the computer-executable instructions, when executed, also implement the following processes:

[0184] Detect whether the user's real-time location information is within the location range of the target location; if so, determine the reward information for the user's arrival at the target location based on the AR check-in record, and issue the consumption discount voucher corresponding to the reward information to the user.

[0185] Optionally, after reading the recommendation dataset associated with the recommended location points and executing it in the user recommendation process, the method further includes:

[0186] Based on the target location selected by the user from the recommended location points, obtain the target location information of the target location;

[0187] Path rendering is performed based on the location information and the target location information, and the path rendering result data is displayed by calling the AR component.

[0188] Optionally, the computer-executable instructions, when executed, also implement the following processes:

[0189] Detect whether the user's real-time location information is within the location range of the target location point;

[0190] If so, determine the reward information for the user reaching the target location through AR guidance, and issue the corresponding consumption discount voucher to the user; or, push the activity data and / or consumption data associated with the target location to the user.

[0191] Optionally, the target location area includes the scenic area where the user is located, and the location point includes attractions within the scenic area; the historical location point sequence includes a sequence of visited attractions constructed based on the attractions the user has visited within the scenic area.

[0192] It should be noted that the embodiments concerning the storage medium in this specification and the embodiments concerning the location recommendation processing method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0193] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0194] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using a hardware physical module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0195] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0196] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0197] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0198] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0200] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0201] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0202] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0203] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0204] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0205] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0206] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0207] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0208] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A location recommendation processing method, comprising: Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence; Based on the location points and the historical location point sequence, candidate location points for the user within the target location area are determined; the target location area refers to the range of location areas composed of a set of location points that have certain commonalities in a specific dimension; the historical location point sequence refers to the location point sequence constructed by all historical location points that the user has visited or passed through within the target location area; and the candidate location points are location points within the target location area that the user can go to. The recommendation parameters and corresponding recommendation weights of the candidate locations are input into a pre-trained location recommendation model to calculate the recommendation score, and a recommended location is selected from the candidate locations based on the calculated recommendation score. Read the recommendation dataset associated with the recommended location points and recommend it to the user.

2. The location recommendation processing method according to claim 1, wherein the recommendation dataset includes streaming media data; the streaming media data is determined in the following manner: Model the location points within the target location area and label the acquired streaming media data of the target location area; The modeled location points are matched with the tags of the streaming media data to obtain the streaming media data associated with the modeled location points.

3. The location recommendation processing method according to claim 2, wherein the recommendation dataset further includes activity data and / or consumption data; wherein The activity data and / or the consumption data are determined in the following manner: Acquire activity data and / or consumption data of consumer points within the target location area; The activity data and / or consumption data of the consumption points are matched with the location points to obtain the activity data and / or consumption data associated with each location point.

4. The location recommendation processing method according to claim 1, wherein the recommendation parameters include passenger flow data and / or time data; Accordingly, the recommendation parameters and the corresponding recommendation weights include the passenger flow weights corresponding to the passenger flow data and / or the time weights corresponding to the time data; wherein The passenger flow data is collected by calling the flow collection device configured in the target location area.

5. The location recommendation processing method according to claim 1, before the step of determining the location points matching the user's location information within the target location area and constructing the user's historical location point sequence, further includes: Based on the user's access request for location recommendation service submitted through a third-party application installed on the user terminal, the location sensor configured on the user terminal is invoked to collect the user's location information; or, Based on the access request submitted by the user terminal after scanning the identifier code of the location recommendation service, the location sensor is invoked to collect the user's location information.

6. The location recommendation processing method according to claim 1, wherein calculating the recommendation score based on the recommendation parameters and corresponding recommendation weights of the candidate location points includes: The recommendation score for each candidate location is calculated based on the passenger flow data and corresponding passenger flow weights, as well as the time data and corresponding time weights.

7. The location recommendation processing method according to claim 1, further comprising: Obtain the material identification image of the AR material identification captured by the AR component on the user's user terminal, and record the user's AR check-in record based on the material identification image; The multimedia data of the AR material identifier is sent to the user terminal; The multimedia data is played by the user terminal.

8. The location recommendation processing method according to claim 7, wherein the recommendation dataset is displayed after a trigger command submitted by the user to the displayed recommendation access control is detected after the multimedia data playback ends.

9. The location recommendation processing method according to claim 8, if a confirmation instruction submitted by the user for the selected target location point is detected, the image acquisition module is invoked to acquire image information; Based on the collected image information, a route guidance AR image is rendered from the location point to the target location point, and the rendered route guidance AR image is sent to the user's terminal for display.

10. The location recommendation processing method according to claim 9, further comprising: Detect whether the user's real-time location information is within the location range of the target location point; If so, the reward information for the user reaching the target location is determined based on the AR check-in record, and a consumption discount voucher corresponding to the reward information is issued to the user.

11. The location recommendation processing method according to claim 1, after the step of reading the recommendation dataset associated with the recommended location point and recommending it to the user, further comprising: Based on the target location selected by the user from the recommended location points, obtain the target location information of the target location; Path rendering is performed based on the location information and the target location information, and the path rendering result data is displayed by calling the AR component.

12. The location recommendation processing method according to claim 11, further comprising: Detect whether the user's real-time location information is within the location range of the target location point; If so, determine the reward information for the user reaching the target location through AR guidance, and issue the corresponding consumption discount voucher to the user; or, push the activity data and / or consumption data associated with the target location to the user.

13. The location recommendation processing method according to any one of claims 1 to 12, wherein the target location area includes the scenic area where the user is located, and the location point includes attractions within the scenic area; The historical location sequence includes a sequence of visited attractions constructed based on the attractions that the user has visited within the scenic area.

14. A location recommendation processing apparatus, comprising: The location determination module is configured to determine the location points that match the user's location information within the target location area, and to construct a sequence of the user's historical location points; The candidate location point determination module is configured to determine candidate location points for the user within the target location area based on the location points and the historical location point sequence; the target location area refers to the range of location areas composed of a set of location points that have certain commonalities in a specific dimension, the historical location point sequence refers to the location point sequence constructed by all historical location points that the user has visited or passed through within the target location area, and the candidate location points are location points within the target location area that the user can go to. The recommended location point selection module is configured to input the recommendation parameters and corresponding recommendation weights of the candidate location points into a pre-trained location recommendation model to calculate the recommendation score, and select a recommended location point from the candidate location points based on the calculated recommendation score. The location point data recommendation module is configured to read the recommendation dataset associated with the recommended location points and recommend them to the user.

15. The location recommendation processing apparatus according to claim 14, wherein the recommendation dataset includes streaming media data; the streaming media data is determined by running the following module: The streaming media data tagging module is configured to model the location points within the target location area and tag the acquired streaming media data within the target location area; The streaming media data tagging module is configured to match the modeled location points with the tagging labels of the streaming media data to obtain the streaming media data associated with the modeled location points.

16. The location recommendation processing apparatus according to claim 15, wherein the recommendation dataset further includes activity data and / or consumption data; wherein The activity data and / or the consumption data are determined by running the following modules: The point-of-sale data acquisition module is configured to acquire activity data and / or consumption data of points of sale within the target location area; The point-of-sale data matching module is configured to match the activity data and / or consumption data of the point of sale with the location point to obtain the activity data and / or consumption data associated with each location point.

17. The location recommendation processing apparatus according to claim 14, further comprising: The module is configured to acquire the AR material identifier image of the AR material identifier collected by the user's user terminal calling the AR component, and record the user's AR check-in record based on the material identifier image; The module is configured to send multimedia data of the AR material identifier to the user terminal; The multimedia data is played by the user terminal.

18. The location recommendation processing apparatus according to claim 17, wherein the recommendation dataset is displayed after a trigger command submitted by the user to the displayed recommendation access control is detected after the multimedia data playback ends.

19. The location recommendation processing apparatus according to claim 18, further comprising: The image information acquisition module is configured to acquire image information if a confirmation command submitted by the user for the selected target location is detected. The AR image rendering module is configured to render a route-guided AR image from the location point to the target location point based on the collected image information, and to send the rendered route-guided AR image to the user's terminal for display.

20. The location recommendation processing apparatus according to claim 19, further comprising: The location detection module is configured to detect whether the user's real-time location information is within the location range of the target location point; If so, the consumer discount voucher issuance module is run; the consumer discount voucher issuance module is configured to determine the reward information for the user reaching the target location point based on the AR check-in record, and issue the consumer discount voucher corresponding to the reward information to the user.

21. A location recommendation processing device, comprising: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence; Based on the location points and the historical location point sequence, candidate location points for the user within the target location area are determined; the target location area refers to the range of location areas composed of a set of location points that have certain commonalities in a specific dimension; the historical location point sequence refers to the location point sequence constructed by all historical location points that the user has visited or passed through within the target location area; and the candidate location points are location points within the target location area that the user can go to. The recommendation parameters and corresponding recommendation weights of the candidate locations are input into a pre-trained location recommendation model to calculate the recommendation score, and a recommended location is selected from the candidate locations based on the calculated recommendation score. Read the recommendation dataset associated with the recommended location points and recommend it to the user.

22. A storage medium for storing computer-executable instructions, which, when executed, perform the following process: Determine the location points that match the user's location information within the target location area, and construct the user's historical location point sequence; Based on the location points and the historical location point sequence, candidate location points for the user within the target location area are determined; the target location area refers to the range of location areas composed of a set of location points that have certain commonalities in a specific dimension; the historical location point sequence refers to the location point sequence constructed by all historical location points that the user has visited or passed through within the target location area; and the candidate location points are location points within the target location area that the user can go to. The recommendation parameters and corresponding recommendation weights of the candidate locations are input into a pre-trained location recommendation model to calculate the recommendation score, and a recommended location is selected from the candidate locations based on the calculated recommendation score. Read the recommendation dataset associated with the recommended location points and recommend it to the user.

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