Point location recommendation method and device and electronic equipment

Abstract

The embodiment of the invention discloses a point location recommendation method and device and electronic equipment, and the method comprises the steps: obtaining a target location, responding to the target location as a low-heat location, obtaining the getting-on and getting-off data and track information corresponding to the target location, and recalling at least one recommendation point location according to the getting-on and getting-off data and track information of the target location. And controlling a point position recall page of the user terminal to display at least one recommended point position. Therefore, according to the embodiment of the invention, when the target location is the low-heat location with insufficient historical data, the boarding and alighting points can be intercommunicated and used, and the track information is used as supplementary information to carry out point location recall, so that the number of recallable point locations is enriched, the point location recommendation accuracy of the low-heat location is improved, and the user experience is further improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and more specifically, to a point recommendation method, device, and electronic device. Background Art

[0002] In transportation application scenarios, such as online ride-hailing scenarios, the recommendation of boarding and alighting locations often requires sufficient popularity information. However, in actual applications, there are a large number of low-popularity scenarios (such as new or low-popularity destinations). These low-popularity scenarios do not have sufficient boarding and alighting behavior popularity information available, making it difficult to recommend reasonable locations. Summary of the Invention

[0003] In view of this, an embodiment of the present invention provides a point recommendation method, device and electronic device, so that when the target location is a low-heat location with insufficient historical data, the boarding and alighting points can be interconnected and used interchangeably, and the trajectory information is used as supplementary information for point recall, thereby enriching the number of recallable points, improving the accuracy of point recommendations for low-heat locations, and thereby improving the user experience.

[0004] In a first aspect, an embodiment of the present invention provides a method for recommending a location, the method comprising:

[0005] Get the target location;

[0006] In response to the target location being a low-heat location, obtaining boarding and alighting data and trajectory information corresponding to the target location;

[0007] Determining at least one recommended location based on the boarding and alighting data and the trajectory information;

[0008] The point recall page of the user terminal is controlled to display at least one of the recommended points.

[0009] Furthermore, obtaining the boarding and alighting data and trajectory information corresponding to the target location includes:

[0010] Determine a grid area where the target location is located and a contour area of ​​the target location, wherein the grid area is a geographical area with a predetermined size;

[0011] Obtaining the boarding and getting-off behaviors corresponding to the grid area and the boarding and getting-off behaviors corresponding to the outline area to obtain the boarding and getting-off data;

[0012] Acquire trajectory data within a predetermined range of the target location to obtain the trajectory information.

[0013] Furthermore, determining at least one recommended location based on the boarding and alighting data and the trajectory information includes:

[0014] Aggregating the obtained boarding and alighting behaviors based on the points corresponding to the boarding and alighting behaviors to obtain a first candidate point set;

[0015] Sampling the trajectory points of each trajectory in the trajectory information to obtain a second candidate point set;

[0016] The recommended points are obtained from the first candidate point set and the second candidate point set.

[0017] Furthermore, sampling the trajectory points in the trajectory information to obtain a second candidate point set includes:

[0018] Clustering the trajectory points of each trajectory in the trajectory information to obtain the stay area corresponding to each trajectory;

[0019] Sampling the trajectory points in each of the stay areas to obtain the second candidate point set.

[0020] Furthermore, obtaining the recommended point from the first candidate point set and the second candidate point set includes:

[0021] sorting the candidate points in the first candidate point set and the second candidate point set according to the distance information and / or heat information corresponding to the candidate points;

[0022] A predetermined number of candidate points ranked first are determined as the recommended points.

[0023] Furthermore, the method further comprises:

[0024] Determining a fusion feature of each recommended point based on the features of each recommended point, features of points related to each recommended point, and / or trajectory features corresponding to each recommended point;

[0025] Sort the recommended points according to the fusion features to obtain a recommendation order;

[0026] The recommended points are pushed to a point recall page of the user terminal based on the recommendation order.

[0027] Furthermore, the method further comprises:

[0028] In response to the online time of the target location being less than a predetermined duration and / or the location popularity of the target location being less than a predetermined popularity threshold, the target location is determined to be a low-heat location.

[0029] In a second aspect, an embodiment of the present invention provides a location recommendation device, the device comprising:

[0030] a location acquisition unit, configured to acquire a target location;

[0031] an information acquisition unit configured to acquire boarding and alighting data and trajectory information corresponding to the target location in response to the target location being a low-heat location;

[0032] a recommended point determination unit, configured to determine at least one recommended point based on the boarding and alighting data and the trajectory information;

[0033] The control unit is configured to control the point recall page of the user terminal to display at least one of the recommended points.

[0034] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described above.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0036] In a fifth aspect, an embodiment of the present invention provides a computer program product, which, when executed on a computer, enables the computer to execute the method described above.

[0037] This embodiment of the present invention obtains a target location, and in response to the target location being a low-heat location, obtains boarding and alighting data and trajectory information corresponding to the target location. Based on the boarding and alighting data and trajectory information, at least one recommended location is recalled, and the location recall page of the user terminal is controlled to display the at least one recommended location. Thus, when the target location is a low-heat location with insufficient historical data, this embodiment of the present invention enables interoperability between boarding and alighting points, and uses trajectory information as supplementary information for point recall. This increases the number of recallable locations, improves the accuracy of location recommendations for low-heat locations, and thereby enhances the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0039] Figure 1 is a flow chart of a point recommendation method according to an embodiment of the present invention;

[0040] Figure 2 is a flow chart of a method for obtaining data information according to an embodiment of the present invention;

[0041] Figure 3 is a flow chart of a method for determining recommended points according to an embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of a candidate point determination process according to an embodiment of the present invention;

[0043] Figure 5 is a schematic diagram of another candidate point determination process according to an embodiment of the present invention;

[0044] Figure 6 is a schematic diagram of another candidate point determination process according to an embodiment of the present invention;

[0045] Figure 7 is a schematic diagram of a method for determining recommended points according to an embodiment of the present invention;

[0046] Figure 8 is a schematic diagram of a method for determining a recommended point sequence according to an embodiment of the present invention;

[0047] Figure 9 Schematic diagram of the point recommendation process according to an embodiment of the present invention;

[0048] Figure 10 is a schematic diagram of a point recommendation device according to an embodiment of the present invention;

[0049] Figure 11 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present application is described below based on the following embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without the description of these details. To avoid obscuring the essence of the present application, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0051] Furthermore, persons of ordinary skill in the art will appreciate that the figures provided herein are for illustration purposes only and are not necessarily drawn to scale.

[0052] Unless the context clearly requires otherwise, words like “include”, “comprising” and the like throughout this application should be interpreted as including rather than exclusive or exhaustive; that is, as meaning “including but not limited to”.

[0053] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0054] Where the solutions described in this specification and in the examples involve the processing of personal information, such processing will be conducted with a legitimate basis (e.g., with the consent of the personal information subject or as necessary for the performance of a contract) and only within the prescribed or agreed scope. A user's refusal to process personal information other than that required for basic functions will not affect the user's use of these basic functions.

[0055] The embodiments of the present invention are mainly described using the online car-hailing scenario as an example. It should be understood that other transportation scenarios that require the determination of locations (such as express delivery, food delivery, freight, etc.) can all use the location recommendation method of the embodiments of the present invention to recommend locations.

[0056] In the relevant comparative examples, the low-heat scenarios of online car-hailing often recommend points based on distance. Due to the small number of candidate points and the lack of historical data, it will lead to the inability to find a reasonable location. For example, the recommended point does not deliver to the destination or there are situations such as crossing the road or crossing the wall, which leads to a poor user experience. In other comparative examples, low-heat locations are recommended based on nearby high-heat destinations, but since the point requirements of different destinations may vary greatly, the recommended points may be biased, which in turn leads to the problem of poor user experience. In view of this, the embodiments of the present invention provide a point recommendation method, device and electronic device, so that when the target location is a low-heat location with insufficient historical data, the boarding and alighting points can be interconnected and used, and the trajectory information is used as supplementary information for point recall, which enriches the number of recallable points, improves the accuracy of point recommendations for low-heat locations, and thus improves the user experience.

[0057] Figure 1 This is a flow chart of the method for recommending a location in accordance with an embodiment of the present invention. It should be understood that the method for recommending a location in accordance with an embodiment of the present invention can be executed when the user terminal runs the corresponding application and requires location recommendations, or after the user terminal obtains the target location, the server executes the step of determining the recommended location and sends the recommended location to the user's destination for display. In other words, this embodiment does not limit the specific execution end of the method, which can be deployed according to the needs of the specific application. Figure 1 As shown, the point recommendation method according to the embodiment of the present invention may include the following steps:

[0058] Step S110, obtaining the target location. Optionally, taking online ride-hailing as an example, this embodiment can obtain the target location input by the user through the location input box on the user terminal page, or through the voice input control on the user terminal page, or obtain the target location by obtaining the historical location triggered by the user on the user terminal page. The embodiment of the present invention does not limit how to obtain the target location of the user's current trip, and it can adopt any method of obtaining the target location that can be implemented in the current application or any method that will be developed in the future.

[0059] Furthermore, the target location in this embodiment can be a target starting point or a target end point. If the target location is a target starting point, the recommended location is a recommended boarding point. If the target location is a target end point, the recommended location is a recommended alighting point.

[0060] Step S120, in response to the target location being a low-heat location, obtain the boarding and alighting data and trajectory information corresponding to the target location. Among them, a location whose heat is less than a predetermined heat threshold is regarded as a low-heat location. Optionally, this embodiment may use the number of trips or trip frequency corresponding to a location to characterize the heat of the location. For example, this embodiment may regard a location whose corresponding number of trips (or trip frequency) is less than a predetermined threshold as a low-heat location. The trip corresponding to a location is also a trip with the location as the starting point or end point.

[0061] Furthermore, because location status may change in actual application scenarios, the value of historical data over a long period of time is limited. Therefore, in this embodiment, location popularity can be determined based on historical data from a recent predetermined time period to improve the validity of historical data and, therefore, the accuracy of location popularity. The location status can represent the open or closed state of a location. For example, a location may be closed or partially closed starting from a certain date due to facility changes.

[0062] Further optionally, in this embodiment, the newly added location can be used as a low-heat location, wherein the newly added location is a location whose online time is less than a predetermined time.

[0063] After obtaining the target location, this embodiment can determine whether the target location is a low-popularity location based on the online time or location popularity of the target location.

[0064] Figure 2 Flowchart of the data information acquisition method of the embodiment of the present invention. In an optional implementation, the data information such as boarding and alighting data and trajectory information of the embodiment of the present invention is acquired by the following steps:

[0065] Step S121: Determine the grid area and the outline area of ​​the target location. A grid area is a geographic area of ​​a predetermined size. Furthermore, the grid area of ​​the target location can be a grid area obtained by expanding a predetermined range around the latitude and longitude of the target location, or it can be the grid area within which the target location falls after the entire map is divided.

[0066] In an optional implementation, this embodiment can determine the historical boarding and alighting data and trajectory data within a predetermined range around the coordinates of the target location, and pre-process the historical boarding and alighting data and trajectory data to remove outliers, noise points, etc., and then perform cluster analysis on the pre-processed historical boarding and alighting data and trajectory data to obtain the main activity areas in the historical itinerary, extract boundary points (such as edge points of the activity area, etc.) based on the clustering results and trajectory data, and select key points that can better describe the contour shape from the boundary points, and perform contour reconstruction based on the obtained key points to obtain the contour area of ​​the target location. Optionally, this embodiment can use a convex hull algorithm or other sorting algorithm to arrange the extracted key points in a certain order, and perform interpolation (such as linear interpolation, spline interpolation, etc.) and fitting to achieve contour reconstruction and obtain the contour area of ​​the target location. In other optional implementations, this embodiment can also determine the contour of the location based on the contour key points of the location collected in advance. Among them, the contour key points can be extracted according to the edge detection algorithm or manually marked. It should be understood that this embodiment does not limit the method for determining the contour area of ​​the target location, and any of the above implementation methods, or other existing or future location contour analysis methods can be applied in the embodiments of the present invention.

[0067] Furthermore, the grid area and the outline area of ​​the target location in this embodiment may be predetermined and stored, or may be determined in real time when there is a demand for point recommendation, and this embodiment does not limit this.

[0068] Step S122 , obtaining the boarding and alighting behaviors corresponding to the grid area where the target location is located and the boarding and alighting behaviors corresponding to its outline area, to obtain boarding and alighting data.

[0069] The boarding and alighting activities corresponding to the grid area where the target location is located include boarding and alighting activities within the grid area. The boarding and alighting activities corresponding to the outline area of ​​the target location, i.e., the geographical area formed by the outline of the target location, may include boarding and alighting activities within the outline area or within an area expanded by a predetermined range from the outline area.

[0070] The boarding and alighting behaviors include boarding behaviors with the target location as the starting point and the boarding point located within the grid area of ​​the target location, and alighting behaviors with the target location as the end point and the alighting point located within the grid area of ​​the target location. In another optional implementation, the boarding and alighting behaviors may also include boarding and alighting behaviors with an associated location of the target location as the starting point or end point. The associated location of the target location may be a location within the target location, a location containing the target location, or a location adjacent to the target location.

[0071] Furthermore, in this embodiment, boarding and alighting behaviors may also include user-initiated boarding and alighting behaviors. For example, if a new location is added to the application, a reporting portal may be provided for the new location, allowing users to report their boarding location starting from the new location or their alighting location ending at the new location through the reporting portal.

[0072] This embodiment aggregates the boarding and alighting behaviors corresponding to the historical travel information obtained after user authorization, as well as the boarding and alighting behaviors reported by the user, to obtain boarding and alighting data.

[0073] Step S123 , obtaining trajectory data within a predetermined range of the target location to obtain trajectory information.

[0074] Optionally, the predetermined range of this embodiment may be a geographical range formed by expanding outward by a predetermined distance from the target location as the center. Further optionally, the trajectory data within the predetermined range of the target location of this embodiment may include trajectory data within the grid area where the target location is located and / or trajectory data corresponding to the contour area of ​​the target location. The trajectory data corresponding to the contour area of ​​the target location includes trajectory data within the contour area, and may also include trajectory data within the area after the contour area is expanded by a predetermined range.

[0075] Furthermore, the trajectory data of this embodiment may include the trajectory of historical travel information obtained after user authorization, and may also include trajectory data around the target location actively reported by the user.

[0076] It should be understood that the above steps of obtaining the boarding and alighting data and the trajectory information may be performed simultaneously or sequentially, and this embodiment does not limit the order of execution.

[0077] Step S130: determining at least one recommended location based on the boarding and alighting data and trajectory information corresponding to the target location.

[0078] Figure 3 is a flow chart of a method for determining recommended points according to an embodiment of the present invention. In an optional implementation, Figure 3 As shown, the recommended points in the embodiment of the present invention are obtained by the following steps:

[0079] Step S131 : Aggregate the acquired boarding and getting behaviors based on the points corresponding to the boarding and getting behaviors to acquire a first candidate point set.

[0080] Optionally, in this embodiment, the first point location set is obtained by aggregating the boarding and alighting behaviors within the grid area where the target location is located.

[0081] Figure 4 FIG. 1 is a schematic diagram of a candidate point determination process according to an embodiment of the present invention. Figure 4 As shown in the figure, for the target location P1, which is located in grid area G1, multiple points p1 corresponding to boarding and alighting behaviors within grid area G1 are obtained. Each point p1 is then aggregated to obtain a first point subset. The first point subset includes multiple points p2. After aggregation, each point cluster has a corresponding candidate point p2.

[0082] Furthermore, this embodiment aggregates the boarding and alighting behaviors corresponding to the outline area of ​​the target location to obtain a second point location subset. The aggregation process for the boarding and alighting behaviors corresponding to the outline area is similar to the aggregation process for the boarding and alighting behaviors corresponding to the grid area where the target location is located, and will not be repeated here.

[0083] Furthermore, in this embodiment, the union of the first point location subset and the second point location subset is used as the first candidate point location set.

[0084] It should be understood that this embodiment performs an aggregation algorithm on the points corresponding to both the boarding behavior and the alighting behavior. In other optional implementations, due to certain differences between boarding locations and alighting locations in actual applications, this embodiment may also perform an aggregation algorithm on the points corresponding to the boarding behavior to obtain candidate points corresponding to the boarding behavior, and perform an aggregation algorithm on the points corresponding to the alighting behavior to obtain candidate points corresponding to the alighting behavior. Thus, when recommending alighting points, this embodiment can make the weight of the candidate points corresponding to the alighting behavior greater than the weight of the candidate points corresponding to the boarding behavior, and when recommending boarding points, make the weight of the candidate points corresponding to the boarding behavior greater than the weight of the candidate points corresponding to the alighting behavior, thereby further improving the accuracy of point recommendations.

[0085] Step S132: sampling the track points of each track in the track information to obtain a second candidate point set.

[0086] Since the target location in this embodiment is a low-heat location with relatively little historical data on getting off and getting off, this embodiment can further perform point mining based on trajectory information to supplement the points.

[0087] Furthermore, this embodiment may sample the trajectory points in the trajectory information based on a predetermined method.

[0088] In an optional implementation, each trajectory corresponding to the target location is sampled according to a predetermined step size to obtain multiple candidate points. The predetermined step size can be represented by the distance between two sampling points or the time difference between two sampling points, and this embodiment is not limited to this. Further optionally, since the frequency of changes in the roads around the target location is low, multiple trajectories may overlap. In this embodiment, only the overlapping trajectories may be sampled n times (n is greater than or equal to 1 and less than the number of trajectories in the overlapping trajectory) to avoid excessive candidate points obtained based on the trajectory information, which may interfere with the subsequent determination of recommended points.

[0089] Figure 5 Schematic diagram of another candidate point determination process of an embodiment of the present invention. Taking the sampling process of a trajectory as an example, it is assumed that the predetermined step length is m (m is greater than 0) meters, for example, m is a value between 10-100. Figure 5 As shown, for the trajectory L1 corresponding to the target location P1, sampling is performed with a predetermined step length of m meters to obtain candidate points p3, p4 and p5.

[0090] In another optional implementation, since the trajectory stop area within the predetermined range of the target location can represent parking (i.e., getting on and off) to a certain extent, this embodiment can cluster the trajectory points of each trajectory in the trajectory information, obtain the stop area corresponding to each trajectory, and sample the trajectory points in each stop area to obtain a second candidate point set. Furthermore, this embodiment performs spatiotemporal clustering on the trajectory points of each trajectory, that is, clustering the trajectory points whose distances between the trajectory points in the same time period are less than a predetermined distance to obtain the stop area. Further optionally, since the stop behavior occurs at the same traffic light, this embodiment will filter out the stop areas whose distances from the traffic light are less than a preset distance, and sample the remaining stop areas to obtain a second candidate point set.

[0091] Figure 6 FIG. 1 is a schematic diagram of another candidate point determination process according to an embodiment of the present invention. Figure 6 As shown in FIG, for the trajectories L2 and L3 corresponding to the target location P1, the trajectory points on the trajectories L2 and L3 are temporally and spatially clustered to obtain three point clusters, and the candidate points p6, p7 and p8 are sampled from the three point clusters respectively.

[0092] Furthermore, in this embodiment, one or more trajectory points can be sampled as candidate points in the same stay area. The number of candidate points sampled in the same stay area can be set according to the size of the stay area, and this embodiment does not limit this.

[0093] Further optionally, the candidate points obtained after sampling are further aggregated, that is, the candidate points whose mutual distances are less than the preset corresponding distances are aggregated to form multiple clusters, and the center points of each cluster are combined into the above-mentioned second candidate point set.

[0094] Step S133: Acquire at least one recommended point from the first candidate point set and the second candidate point set.

[0095] Figure 7 FIG is a schematic diagram of a method for determining recommended points according to an embodiment of the present invention. Figure 7 As shown, the method for determining recommended points according to an embodiment of the present invention includes the following steps:

[0096] Step S1331, sorting each candidate point according to the distance information and / or heat information corresponding to the candidate points in the first candidate point set and the second candidate point set. The distance information of the candidate point is used to characterize the distance between the coordinates of the candidate point and the target location. The heat information of the candidate point can be determined based on the number of points in the cluster to which it belongs. Furthermore, if the candidate points in the second candidate point set are determined after further aggregation of the candidate points obtained after sampling, the heat information of the candidate points in the second candidate point set is determined based on the number of clusters to which each point in the aggregated cluster belongs.

[0097] Furthermore, in this embodiment, the popularity information of the candidate location can be determined by weighting the first popularity of the candidate location as a boarding point and the second popularity of the candidate location as an alighting point. Optionally, the weights of the first popularity and the second popularity can be the same or different. Furthermore, if the recommended location is a boarding point, the weight corresponding to the first popularity is greater than the weight of the second popularity. If the recommended location is an alighting point, the weight corresponding to the first popularity is less than the weight of the second popularity.

[0098] Furthermore, this embodiment can also set a first score weight corresponding to the distance information of the candidate point and a second score weight corresponding to the heat information, so as to determine the score of each candidate point based on the first score weight and the second score weight, and sort the candidate points based on the score.

[0099] Step S1332: Determine a recommended point based on a predetermined number of candidate points ranked first.

[0100] In one optional implementation, a predetermined number of candidate points ranked first are determined as recommended points, and a recommendation order for the recommended points is determined based on the ranking, and each recommended point is pushed to a point recall page of the user terminal based on the recommendation order. In another optional implementation, a predetermined number of candidate points ranked first are determined as recommended points, and each recommended point is re-ranked to obtain a new recommendation order, and each recommended point is pushed to a point recall page of the user terminal based on the new recommendation order.

[0101] Figure 8 FIG is a schematic diagram of a method for determining a recommended point sequence according to an embodiment of the present invention. Figure 8 As shown, the method for determining the recommended point order in this embodiment includes the following steps:

[0102] Step S210 : determining fusion features of each recommended point based on the features of each recommended point, features of related points of each recommended point, and / or trajectory features corresponding to each recommended point.

[0103] Among them, the features of each recommended point may include distance information and popularity information. The relevant point features of each recommended point may include the popularity information of the first candidate point whose distance from the recommended point is less than a preset distance threshold. Among them, the first candidate point is located in the first candidate point set. The trajectory feature includes the popularity information of the second candidate point whose distance from the recommended point is less than a preset distance threshold. Among them, the second candidate point is located in the second candidate point set. It should be understood that the method of obtaining the popularity information of the point is the same as Figure 7 The above embodiments are similar and will not be described again here.

[0104] Thus, the fusion feature Fi of each recommended point is Fi = {f1(d,r1),{r21,…,r2x},{r31,…r3y}}. Here, i is the i-th recommended point, f1 is the distance information and popularity information of the i-th recommended point itself, r21-r2x is the popularity information of the x first candidate points whose distance from the i-th recommended point is less than the preset distance threshold, and r31-r3y is the popularity information of the y first candidate points whose distance from the i-th recommended point is less than the preset distance threshold.

[0105] Step S220 , sorting the recommended points according to the fusion features to obtain a recommendation order.

[0106] The recommendation score of each recommended point is determined based on the fusion features of each recommended point, and the recommended points are sorted based on the recommendation score to obtain a recommendation order. It should be understood that in this embodiment, the higher the popularity of the recommended point, the greater the number of first and second candidate points corresponding to the recommended point, and the higher the popularity information of the first and second candidate points corresponding to the recommended point, the higher the recommendation score of the recommended point. In specific application scenarios, corresponding weights can be set to calculate the recommendation score of each recommended point, and this embodiment will not be explained one by one by example here.

[0107] Step S230: Push each recommended point to the point recall page of the user terminal based on the recommendation order.

[0108] This embodiment can sort the candidate points based on their distance information and popularity information to obtain recommended points, and then perform fine sorting based on the fusion features of each recommended point to improve the accuracy of point recommendations and thus enhance user experience. In other optional implementations, this embodiment can also determine the fusion features of each candidate point in the first candidate point set and the second candidate point set, and sort the candidate points based on their fusion features, determine a predetermined number of candidate points ranked first as recommended points, and determine the recommendation order based on the ranking of the recommended points.

[0109] Step S140: Control the point recall page of the user terminal to display at least one recommended point.

[0110] In this embodiment, the point recall page may be a page for the user to determine the boarding point or the alighting point, which may be displayed after the user determines the target starting point or target end point input by the user.

[0111] This embodiment of the present invention obtains a target location, and in response to the target location being a low-heat location, obtains boarding and alighting data and trajectory information corresponding to the target location. Based on the boarding and alighting data and trajectory information, at least one recommended location is recalled, and the location recall page of the user terminal is controlled to display the at least one recommended location. Thus, when the target location is a low-heat location with insufficient historical data, this embodiment of the present invention enables interoperability between boarding and alighting points, and uses trajectory information as supplementary information for point recall. This increases the number of recallable locations, improves the accuracy of location recommendations for low-heat locations, and thereby enhances the user experience.

[0112] Figure 9 Schematic diagram of the point recommendation process according to an embodiment of the present invention. Figure 9 As shown, after determining that the current target location is a low-heat target location, the boarding and alighting behaviors corresponding to the grid area where the target location is located and the boarding and alighting behaviors corresponding to the outline area of ​​the target location are obtained to obtain boarding and alighting data, and obtain trajectory data within a predetermined range of the target location.

[0113] Furthermore, the obtained boarding and alighting behaviors are aggregated based on the points corresponding to each boarding and alighting behavior to obtain a first set of candidate points, and the trajectory points of each trajectory in the trajectory information are sampled to obtain a second set of candidate points. The candidate points are sorted according to the distance information and / or heat information corresponding to the candidate points in the first set of candidate points and the second set of candidate points, and the predetermined number of candidate points ranked first are determined as recommended points. Furthermore, the fusion features of each recommended point are determined based on the features of each recommended point, the features of the related points of each recommended point, and / or the trajectory features corresponding to each recommended point, and the recommended points are sorted according to the fusion features to obtain the recommended points after fine sorting and push them to the user. It should be understood that the steps in the point recommendation process of the embodiment of the present invention are similar to those in the above embodiment and will not be repeated here.

[0114] This embodiment of the present invention obtains a target location, and in response to the target location being a low-heat location, obtains boarding and alighting data and trajectory information corresponding to the target location. Based on the boarding and alighting data and trajectory information, at least one recommended location is recalled, and the location recall page of the user terminal is controlled to display the at least one recommended location. Thus, when the target location is a low-heat location with insufficient historical data, this embodiment of the present invention enables interoperability between boarding and alighting points, and uses trajectory information as supplementary information for point recall. This increases the number of recallable locations, improves the accuracy of location recommendations for low-heat locations, and thereby enhances the user experience.

[0115] Figure 10 Schematic diagram of a point recommendation device according to an embodiment of the present invention. Figure 10 As shown, the location recommendation device 10 according to the embodiment of the present invention includes a location acquisition unit 101 , an information acquisition unit 102 , a recommended location determination unit 103 and a control unit 104 .

[0116] The location acquisition unit 101 is configured to acquire a target location. The information acquisition unit 102 is configured to, in response to the target location being a low-heat location, acquire boarding and alighting data and trajectory information corresponding to the target location. The recommended point determination unit 103 is configured to determine at least one recommended point based on the boarding and alighting data and the trajectory information. The control unit 104 is configured to control the point recall page of the user terminal to display at least one recommended point.

[0117] In an optional implementation, the information acquisition unit 102 is further configured to determine a grid area where the target location is located and a contour area of ​​the target location, where the grid area is a geographical area of ​​a predetermined size, obtain the boarding and alighting behaviors corresponding to the grid area and the boarding and alighting behaviors corresponding to the contour area to obtain the boarding and alighting data, and obtain the trajectory data within a predetermined range of the target location to obtain the trajectory information.

[0118] In an optional implementation, the recommended point determination unit 103 is further configured to aggregate the obtained boarding and getting on behaviors based on the points corresponding to each of the boarding and getting on behaviors, obtain a first set of candidate points, sample the trajectory points of each trajectory in the trajectory information, obtain a second set of candidate points, and obtain the recommended points from the first set of candidate points and the second set of candidate points.

[0119] In an optional implementation, the recommended point determination unit 103 is further configured to cluster the trajectory points of each trajectory in the trajectory information, obtain the stay area corresponding to each trajectory, sample the trajectory points in each of the stay areas, and obtain the second candidate point set.

[0120] In an optional implementation, the recommended point determination unit 103 is further configured to sort each candidate point according to the distance information and / or heat information corresponding to the candidate points in the first candidate point set and the second candidate point set, and determine a predetermined number of the candidate points ranked first as the recommended points.

[0121] In an optional implementation, the point recommendation device 10 also includes a precision sorting unit, which is configured to determine the fusion characteristics of each recommended point based on the characteristics of each recommended point, the characteristics of the related points of each recommended point, and / or the trajectory characteristics corresponding to each recommended point, sort each recommended point according to the fusion characteristics, obtain the recommendation order, and push each recommended point to the point recall page of the user terminal based on the recommendation order.

[0122] In an optional implementation, the location recommendation device 10 also includes a low-heat location judgment unit, which is configured to determine that the target location is a low-heat location in response to the online time of the target location being less than a predetermined duration and / or the location heat of the target location being less than a predetermined heat threshold.

[0123] This embodiment of the present invention obtains a target location, and in response to the target location being a low-heat location, obtains boarding and alighting data and trajectory information corresponding to the target location. Based on the boarding and alighting data and trajectory information, at least one recommended location is recalled, and the location recall page of the user terminal is controlled to display the at least one recommended location. Thus, when the target location is a low-heat location with insufficient historical data, this embodiment of the present invention enables interoperability between boarding and alighting points, and uses trajectory information as supplementary information for point recall. This increases the number of recallable locations, improves the accuracy of location recommendations for low-heat locations, and thereby enhances the user experience.

[0124] Figure 11 Schematic diagram of an electronic device according to an embodiment of the present invention. Figure 11 As shown, electronic device 110 is a general-purpose data processing device comprising a general-purpose computer hardware structure, including at least a processor 111 and a memory 112. Processor 111 and memory 112 are connected via a bus 113. Memory 112 is suitable for storing instructions or programs executable by processor 111. Processor 111 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, processor 111 executes the instructions stored in memory 112, thereby performing the method flow of the above-described embodiment of the present invention to process data and control other devices. Bus 113 connects the above-mentioned multiple components together and also connects them to a display controller 114 and a display device, as well as an input / output (I / O) device 115. I / O device 115 can be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, or other devices known in the art. Typically, I / O device 115 is connected to the system via an I / O controller 116.

[0125] It will be understood by those skilled in the art that the embodiments of the present application may be provided as methods, devices (equipment), or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The present application is described with reference to flowcharts of methods, apparatuses (devices), and computer program products according to embodiments of the present application. It should be understood that each process in the flowcharts can be implemented by computer program instructions.

[0127] These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0128] These computer program instructions can also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0129] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used to enable a computer to execute part or all of the above method embodiments.

[0130] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by specifying relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0131] The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application are intended to be within the scope of protection of the present application.

Claims

1. A point recommendation method, characterized in that: The method comprises: Get the target location; In response to the target location being a low-heat location, obtaining boarding and alighting data and trajectory information corresponding to the target location; Determine at least one recommended point according to the boarding and alighting data and the trajectory information; The point recall page of the user terminal is controlled to display at least one of the recommended points.

2. The method according to claim 1, characterized in that The obtaining of the boarding and alighting data and trajectory information corresponding to the target location includes: Determine a grid area where the target location is located and a contour area of ​​the target location, wherein the grid area is a geographical area with a predetermined size; Obtaining the boarding and getting-in behavior corresponding to the grid area and the boarding and getting-in behavior corresponding to the contour area to obtain the boarding and getting-in data; Acquire trajectory data within a predetermined range of the target location to obtain the trajectory information.

3. The method according to claim 2, characterized in that Determining at least one recommended point according to the boarding and alighting data and the trajectory information includes: Aggregating the obtained boarding and getting behaviors based on the points corresponding to the boarding and getting behaviors to obtain a first candidate point set; Sampling the trajectory points of each trajectory in the trajectory information to obtain a second candidate point set; The recommended points are obtained from the first candidate point set and the second candidate point set.

4. The method according to claim 3, characterized in that The sampling of the trajectory points in the trajectory information to obtain the second candidate point set includes: Clustering the trajectory points of each trajectory in the trajectory information to obtain the stop area corresponding to each trajectory; The trajectory points in each of the stay areas are sampled to obtain the second candidate point set.

5. The method according to claim 3, characterized in that: The acquiring the recommended point from the first candidate point set and the second candidate point set includes: Sorting each of the candidate points in the first candidate point set and the second candidate point set according to the distance information and / or heat information corresponding to the candidate points; A predetermined number of candidate points ranked first are determined as the recommended points.

6. The method according to claim 3, characterized in that The method further comprises: Determining fusion features of each of the recommended points according to features of each of the recommended points, features of points related to each of the recommended points, and / or trajectory features corresponding to each of the recommended points; Sorting the recommended points according to the fusion features to obtain a recommendation order; The recommended points are pushed to a point recall page of the user terminal based on the recommendation order.

7. The method according to claim 1, characterized in that The method further comprises: In response to the online time of the target location being less than a predetermined duration and / or the location heat of the target location being less than a predetermined heat threshold, the target location is determined to be a low heat location.

8. A point recommendation device, characterized in that: The device comprises: A location acquisition unit, configured to acquire a target location; an information acquisition unit, configured to acquire boarding and alighting data and trajectory information corresponding to the target location in response to the target location being a low-heat location; a recommended point determination unit, configured to determine at least one recommended point according to the boarding and alighting data and the trajectory information; The control unit is configured to control the point recall page of the user terminal to display at least one of the recommended points.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the computer program product is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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