CN111859174A

CN111859174A - Method and system for determining recommended boarding point

Info

Publication number: CN111859174A
Application number: CN201911225778.1A
Authority: CN
Inventors: 杨建涛; 贺明慧; 束纬寰; 马利
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-12-03
Filing date: 2019-12-03
Publication date: 2020-10-30

Abstract

The application provides a method and a system for determining recommended boarding points. The method comprises the following steps: acquiring position information of a user and historical behavior information of the user; determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user; inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining at least one recommended interest point from the candidate interest points; obtaining search interest point information selected by a user, wherein the search interest point is one of the at least one recommended interest point; determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user; features associated with the plurality of candidate pick-up points are input to a pick-up point recommendation model, and at least one recommended pick-up point is determined from the plurality of candidate pick-up points.

Description

Method and system for determining recommended boarding point

Technical Field

The present application relates to the field of shared vehicles, and in particular, to a method and system for determining recommended boarding points.

Background

With the rapid development of shared transportation services, more and more users choose to use online taxi service on mobile phones. The user inputs the boarding place, the destination and the boarding time through the client and sends the vehicle using request. And the order receiving driver goes to the boarding place to receive driving. However, the boarding place input by the passenger may be a place which cannot be reached by the driver, a detour exists during the driver pickup or the passenger needs to walk for a long distance to reach, and in order to enable the driver to efficiently pick up the user, the online taxi taking service platform recommends the optimal boarding place for the user.

Currently, a Point of Interest (POI) recommendation list of an online taxi service platform generally recommends a personalized taxi-boarding Point and a high-heat POI near a current location of a user to the user. However, the POI recommendation method does not fully consider the actual requirements of different users, and may provide irrelevant POIs to the users, which causes resource waste of the POI recommendation list, and may even prolong the time for the users to place orders. Accordingly, it is desirable to provide a method and system for determining recommended pick-up points.

Disclosure of Invention

One aspect of the present application provides a method of determining a recommended pick-up point. The method comprises the following steps: acquiring position information of a user and historical behavior information of the user; determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user; inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining at least one recommended interest point from the candidate interest points; obtaining search interest point information selected by a user, wherein the search interest point is one of the at least one recommended interest point; determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user; features associated with the plurality of candidate pick-up points are input to a pick-up point recommendation model, and at least one recommended pick-up point is determined from the plurality of candidate pick-up points.

Another aspect of the present application provides a system for determining a recommended pick-up point. The system comprises: the first acquisition module is used for acquiring the position information of a user and the historical behavior information of the user; the candidate interest point determining module is used for determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user; the recommended interest point determining module is used for inputting the characteristics of the candidate interest points into an interest point recommending model and determining at least one recommended interest point from the candidate interest points; the second acquisition module is used for acquiring search interest point information selected by a user, wherein the search interest point is one of the at least one recommended interest point; the candidate boarding point determining module is used for determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user; and the recommended boarding point determining module is used for inputting the characteristics associated with the candidate boarding points into a boarding point recommendation model and determining at least one recommended boarding point from the candidate boarding points.

Another aspect of the present application provides an apparatus for determining a recommended pick-up point, comprising at least one storage medium and at least one processor; the at least one storage medium is configured to store computer instructions; the at least one processor is configured to execute the computer instructions to implement the method of determining a recommended pick-up point as previously described.

Another aspect of the present application provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the method of determining a recommended pick-up point as previously described.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a system for determining recommended pick-up points according to some embodiments of the present application;

FIG. 2 is a block diagram of a system for determining recommended pick-up points according to some embodiments of the present application;

FIG. 3 is an exemplary flow chart of a method of determining recommended pick-up points according to some embodiments of the present application;

FIG. 4 is an exemplary flow diagram illustrating the determination of a recommended point of interest from candidate points of interest according to some embodiments of the present application;

FIG. 5 is an exemplary flow chart illustrating the determination of a recommended pick-up point from among candidate pick-up points according to some embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Embodiments of the present application may be applied to different traffic service systems, including but not limited to one or a combination of land, surface, aviation, aerospace, and the like. Such as a human powered vehicle, a vehicle, an automobile (e.g., a small car, a bus, a large transportation vehicle, etc.), rail transportation (e.g., a train, a bullet train, a high-speed rail, a subway, etc.), a boat, an airplane, an airship, a satellite, a hot air balloon, an unmanned vehicle, etc. The application scenarios of the different embodiments of the present application include but are not limited to one or a combination of several of transportation industry, warehouse logistics industry, agricultural operation system, urban public transportation system, commercial operation vehicle, etc. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these drawings.

The term "pick-up point" in this application may refer to a location where a driver begins to provide a service initiated by a user. For example, in an online taxi service, a driver may pick up a user initiating the service at the gate of a school and send the user to the user's destination. The school doorway may be the boarding point for the service. The term "candidate pick-up points" in this application may refer to locations that include potential locations where a driver begins to provide a service initiated by a target user terminal in the area, and/or locations of historical pick-up points in the area.

The terms "passenger", "passenger end", "vehicle occupant", "user terminal", "customer", "requester", "service requester", "consumer side", "use requester" and the like are used interchangeably herein to refer to a party that needs or orders a service, either a person or a tool. Similarly, "driver," "provider," "service provider," "server," and the like, as described herein, are interchangeable and refer to an individual, tool, or other entity that provides a service or assists in providing a service. In addition, a "user" as described herein may be a party that needs or subscribes to a service, or a party that provides or assists in providing a service.

The position and/or trajectory in the present application may be obtained by a positioning technique embedded in the user terminal. The positioning technology used in the present application may include one or any combination of Global Positioning System (GPS), global satellite navigation system (GLONASS), COMPASS navigation system (COMPASS), galileo positioning system, quasi-zenith satellite system (QZSS), wireless fidelity (Wi-Fi) positioning technology, and the like. One or more of the above positioning techniques may be used interchangeably in this application.

FIG. 1 is a schematic diagram of an application scenario of a system for determining recommended pick-up points according to some embodiments of the present application. The system for determining recommended pick-up points 100 may determine recommended pick-up points and recommend them to the user. The system for determining recommended pick-up points 100 may be an online service platform for internet services. For example, the system 100 for determining recommended pick-up points may be an online transportation service platform for a transportation service. In some embodiments, the system for determining recommended pick-up points 100 may be applied to network appointment services, such as taxi calls, express calls, special calls, mini-bus calls, carpools, bus services, driver employment and pickup services, and the like. In some embodiments, the system 100 for determining recommended pick-up points may also be applied to designated driving services, courier delivery, take-away delivery, and the like. The system 100 for determining recommended pick-up points may include a server 110, a network 120, a user terminal 130, a driver's terminal 140, and a storage device 150. The server 110 may include a processing device 112.

The server 110 may process data and/or information from at least one component of the system 100 that determines recommended pick-up points. The server 110 may communicate with the user terminal 130 to provide various functions of an online service. For example, the server 110 may receive a service request from the user terminal 130 and process the service request to recommend a pick-up point to the user terminal 130.

In some embodiments, the server 110 may be a single processing device or a group of processing devices. The processing device group may be a centralized processing device group connected to the network 120 via an access point or a distributed processing device group respectively connected to the network 120 via at least one access point. In some embodiments, server 110 may be connected locally to network 120 or remotely from network 120. For example, the server 110 may access information and/or data stored in the user terminal 130, the driver's terminal 140, and/or the storage device 150 via the network 120. As another example, the storage device 150 may serve as a back-end data store for the server 110. In some embodiments, the server 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.

In some embodiments, the server 110 may include a processing device 112. Processing device 112 may process information and/or data related to at least one function described herein. In some embodiments, the processing device 112 may perform the primary function of the system 100 of determining recommended pick-up points. In some embodiments, the processing device 112 may perform other functions related to the methods and systems described herein. In some embodiments, the processing device 112 may include at least one processing unit (e.g., a single core processing device or a multiple core processing device). By way of example only, the processing device 112 includes a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may facilitate the exchange of information and/or data. In some embodiments, at least one component (e.g., server 110, user terminal 130, storage device 150) in the system 100 that determines recommended pick-up points may send information and/or data to other components in the system 100 that determine recommended pick-up points via the network 120. For example, the processing device 112 may obtain historical behavior data of the user from the storage device 150 via the network 120.

In some embodiments, the network 120 may be any form of wired or wireless network, or any combination thereof. By way of example only, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof. In some embodiments, network 120 may include at least one network access point. For example, the network 120 may include wired or wireless network access points, such as base stations and/or Internet exchange points 120-1, 120-2, … …, to which at least one component of the system 100 may connect to exchange data and/or information by determining recommended pick-up points.

User terminal 130 may communicate with server 110 via network 120. In some embodiments, the user of the user terminal 130 may be the service requester himself. In some embodiments, the user of the user terminal 130 may be a person other than the service requester. For example, in the network appointment service, the user of the user terminal 130 may be the vehicle occupant himself or herself, or may be a person who places an order with the vehicle occupant, such as a relative or a friend of the vehicle occupant.

In some embodiments, the user terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, etc., or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart televisions, smart cameras, interphones, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, smart footwear, smart glasses, smart helmet, smart watch, smart clothing, smart backpack, smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a point of sale (P) OS), etc., or any combination thereof. In some embodiments, the virtual reality device and/or the enhanced virtual reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality patch, an augmented reality helmet, augmented reality glasses, an augmented reality patch, and the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a google glass^TM、OculusRift^TM、Hololens^TMOr GearVR^TMAnd the like.

In some embodiments, the user terminal 130 may send the service request to the server 110 for processing. In some embodiments, the user terminal 130 may be a device with location technology to determine the location of the service requester and/or the user terminal 130 and send to one or more devices in the system 100, such as the server 110, that determine the recommended pick-up point.

The driver's terminal 140 can communicate with the server 110 via the network 120. The user of the driver's terminal 140 may be the service provider himself. In some embodiments, the user of the driver's terminal 140 can be someone other than the service provider. For example, in the online car booking service, the user of the driver terminal 140 may be the service provider himself or a person who helps the service provider to take an order.

In some embodiments, the driver terminal 140 may be a similar or identical device as the user terminal 130. In some embodiments, the driver's terminal 140 can send the transport service demand to the server 110 for processing. In some embodiments, the driver's terminal 140 may be a device with location technology to determine the location of the service provider and/or driver's terminal 140 and to send to one or more devices in the system 100, such as the server 110, that determine recommended pick-up points.

Storage device 150 may store data and/or instructions. For example, historical search points of interest, click through rates for historical search points of interest, historical recommended points of interest, historical points of interest, and the like may be stored. In some embodiments, the storage device 150 may store data obtained/obtained from the server 110, the user terminal 130, or the driver's terminal 140. In some embodiments, storage device 150 may store data and/or instructions that may be executed by processing device 112, and server 110 may execute or use the data and/or instructions to implement the example methods described herein. In some embodiments, storage device 150 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read and write memories can include Random Access Memory (RAM). Exemplary RAM may include Dynamic Random Access Memory (DRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Static Random Access Memory (SRAM), thyristor random access memory (T-RAM), zero capacitance random access memory (Z-RAM), and the like. Exemplary read-only memories may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (perrom), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the storage device 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.

In some embodiments, a storage device 150 may be connected to the network 120 to communicate with one or more components (e.g., the server 110, the user terminal 130, the driver terminal 140, etc.) in the system 100 that determine recommended pick-up points. One or more components in the system 100 that determine recommended pick-up points may access data or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected or in communication with one or more components in the system 100 (e.g., the server 110, the user terminal 130, the driver terminal 140, etc.). In some embodiments, the storage device 150 may be part of the server 110.

It should be noted that the above description of the system 100 for determining recommended pick-up points is for purposes of example and illustration only and is not intended to limit the scope of applicability of the present application. Various modifications and variations of the system 100 for determining recommended pick-up points will be apparent to those skilled in the art in light of the present application. However, such modifications and variations are intended to be within the scope of the present application.

FIG. 2 is a block diagram of a system for determining recommended pick-up points according to some embodiments of the present application. The system for determining recommended boarding points may include a first obtaining module 210, a candidate point of interest determining module 220, a recommended point of interest determining module 230, a second obtaining module 240, a candidate boarding point determining module 250, and a recommended boarding point determining module 260. These modules may be hardware circuitry of at least a portion of the processing device 112. These modules may also be implemented as applications or instructions that are read or executed by the processing device 112. Further, these modules may be any combination of hardware circuitry and applications/instructions. These modules may be part of processing device 112, for example, when the processing device executes applications/instructions.

The first acquisition module 210 can receive information related to the service request from one or more components in the system 100 (e.g., the user terminal 130, the driver terminal 140, the storage device 150, etc.). For example, the first obtaining module 210 may obtain the location information of the user terminal. The location information of the user terminal may be based on location information obtained by a location system built in the user terminal 130. The location information may be latitude and longitude coordinates of the user terminal, or may be a geocode indicating a location, or the like. For another example, the first obtaining module 210 may obtain historical behavior information of the user. In some embodiments, the first obtaining module 210 may send the obtained historical behavior information of the user to other units and/or modules of the processing engine 112 for further processing. For example, the first obtaining module 210 may send the historical behavior information of the user to the candidate point of interest determination module 220 and/or the candidate pick-up determination module 250 for further processing. For another example, the first obtaining module 210 may send the location information of the user terminal to the storage device 150 for storage.

The candidate point of interest determination module 220 may be configured to determine a plurality of candidate points of interest. For example, the candidate point of interest determination module 220 may determine a plurality of candidate points of interest based on the location information of the user and the historical behavior information of the user. The candidate point of interest determination module 220 may further include at least a high-heat point of interest determination unit 222 and a historical search point of interest determination unit 224.

The high-heat interest point determination unit 222 may be configured to determine at least one high-heat interest point. For example, the high-calorie interest point determination unit 222 may determine at least one high-calorie interest point within a certain range from the user according to the location information of the user. In some embodiments, the high-heat interest point determining unit 222 may determine at least one high-heat interest point from the interest point set as a candidate interest point according to at least the heat of each interest point in the interest point set within a preset range from the user position.

The historical search interest point determination unit 224 may be configured to determine at least one historical search interest point. For example, the historical search interest point determination unit 224 may determine at least one historical search interest point searched by the user according to the historical behavior information of the user. In some embodiments, the historical search interest point determining unit 224 may obtain historical search interest point information of the user, and may determine at least one historical search interest point as a candidate interest point according to a location position, a time and/or a click rate of the historical search interest point of the user.

The recommended point of interest determination module 230 may be configured to determine at least one recommended point of interest from a plurality of candidate points of interest. For example, the recommended point of interest determination module 230 may input the features of the candidate points of interest into the point of interest recommendation model, and determine at least one recommended point of interest from the candidate points of interest. The recommended point of interest determination module 230 may further include a candidate point of interest score determination unit 232, a candidate point of interest similarity determination unit 234, a candidate point of interest deduplication unit 236, and a recommended point of interest determination unit 238.

The candidate point of interest score determination unit 232 may be configured to determine a likelihood score for each of a plurality of candidate points of interest. For example, the candidate interest point score determining unit 232 may input the features of the plurality of candidate interest points to the interest point recommendation model, and determine the likelihood score of each of the plurality of candidate interest points. In some embodiments, the features of the candidate interest points may include, but are not limited to, attribute features of the candidate interest points, relationship features of the candidate interest points to user positioning locations, historical click-through rate features of the candidate interest points, and the like.

The candidate interest point similarity determination unit 234 may be configured to calculate a similarity between any two candidate interest points of the plurality of candidate interest points. For example, the candidate interest point similarity determination unit 234 may calculate the similarity of any two candidate interest points according to the name similarity of the candidate interest points, the coordinate similarity of the candidate interest points, and/or the address similarity of the candidate interest points.

The candidate point of interest deduplication unit 236 may be configured to deduplicate the plurality of candidate points of interest. For example, the candidate interest point deduplication unit 236 may perform deduplication on the plurality of candidate interest points according to the similarity between any two candidate interest points.

The recommended point of interest determination unit 238 may be configured to determine a recommended point of interest in response to a service request of the user. For example, the recommended interest point determining unit 238 may determine at least one candidate interest point as the recommended interest point according to the likelihood score of the candidate interest points after the deduplication. In some embodiments, the recommended interest point determining unit 238 may rank the candidate interest points after the deduplication according to the likelihood score of the candidate interest points after the deduplication, and determine at least one candidate interest point as the recommended interest point according to the ranking result. For more details regarding determining a recommended point of interest from a plurality of candidate points of interest, reference may be made to fig. 4 and its associated description.

The second acquisition module 240 can be used to receive information related to the service request from one or more components in the system 100 (e.g., the user terminal 130, the driver terminal 140, the storage device 150, etc.). For example, the first obtaining module 210 may obtain the search interest point information selected by the user, where the search interest point is one of the at least one recommended interest point. In some embodiments, the second obtaining module 240 may send the obtained user-selected search point of interest information to other elements and/or modules of the processing engine 112 for further processing. For example, the second obtaining module 240 may send the search point of interest information selected by the user to the candidate pick-up point determination module 250 for further processing. For another example, the second obtaining module 240 may transmit the search point-of-interest information selected by the user to the storage device 150 for storage.

The candidate pick-up point determination module 250 may be configured to determine a plurality of candidate pick-up points. For example, the candidate pick-up point determination module 250 may determine a plurality of candidate pick-up points according to the search interest point information selected by the user and the historical behavior information of the user. The candidate pick-up point determination module 250 may further include at least a relevant pick-up point determination unit 252 and a historical pick-up point determination unit 254.

The associated pick-up point determination unit 252 may be configured to determine at least one associated pick-up point. For example, the related boarding point determining unit 252 may determine at least one related boarding point within a certain range from the search interest point according to the search interest point information sent by the user.

Historical boarding point determination unit 254 may be used to determine at least one historical boarding point for the user. For example, the historical boarding point determination unit 254 may determine at least one historical boarding point of the user based on the historical behavior information of the user.

The recommended pick-up point determination module 260 may be configured to determine at least one recommended pick-up point from a plurality of candidate pick-up points. For example, the recommended boarding point determination module 250 may input features associated with the plurality of candidate boarding points to a boarding point recommendation model to determine at least one recommended boarding point from the plurality of candidate boarding points. The recommended pick-up point determination module 260 may further include a candidate pick-up point score determination unit 262 and a recommended pick-up point determination unit 264.

The candidate pick-up point score determination unit 262 may be configured to determine a likelihood score for each of the plurality of candidate pick-up points. For example, the candidate boarding point score determining unit 262 may input the associated features of the plurality of candidate boarding points to the boarding point recommendation model, and determine a likelihood score for each of the plurality of candidate boarding points. In some embodiments, the features associated with the candidate pick-up points may include, but are not limited to, attribute features of the candidate pick-up points, relationship features of the candidate pick-up points to the user location, attribute features of the search interest points, relationship features of the search interest points to the candidate pick-up points, features of historical search interest points, and the like.

The recommended boarding point determination unit 264 may be configured to determine at least one candidate boarding point as a recommended boarding point. For example, the recommended boarding point determining unit 264 may determine at least one candidate boarding point as the recommended boarding point according to the probability score of each candidate boarding point. For more details regarding determining a recommended pick-up point from a plurality of candidate pick-up points, reference may be made to fig. 5 and its associated description.

It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the system and its modules for determining recommended pick-up points is merely for convenience of description and should not limit the scope of the present application to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the first obtaining module 210, the candidate interest point determining module 220, and the recommended interest point determining module 230 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, the candidate point of interest determination module 220 may be integrated in the recommended point of interest determination module 230 as a single module that may determine multiple candidate points of interest. For another example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.

FIG. 3 is an exemplary flow chart of a method of determining recommended pick-up points according to some embodiments of the present application. In some embodiments, one or more steps of the method 300 of determining a recommended pick-up point may be implemented in the system 100 shown in FIG. 1. For example, one or more steps of method 300 may be stored as instructions in storage device 150 and/or memory, and invoked and/or executed by server 110 (e.g., processing engine 112 in server 110). In some embodiments, the instructions may be transmitted in the form of electrical current or electrical signals.

Step 310, obtaining the position information of the user and the historical behavior information of the user. Specifically, step 310 may be performed by the first obtaining module 210.

In some embodiments, the location information may include the location at which the user initiated the service request. The user may obtain the location of the user terminal 130 by communicating with a positioning system through a taxi-taking application installed on his mobile device (e.g., the user terminal 130). The user terminal 130 may transmit the user location information to the server 110 through the network 120. Exemplary positioning technologies may include at least one of Global Positioning System (GPS) technology, global navigation satellite system (GLONASS) technology, beidou navigation system technology, galileo positioning system (GLONASS) technology, quasi-zenith satellite system (QAZZ) technology, base station positioning technology, WiFi positioning technology, and the like. The location information may exist in the form of latitude and longitude coordinates, geographic location names, and the like.

In some embodiments, the first obtaining module 210 may access and read data in the storage server 110, the user terminal 130, the driver terminal 140, and/or the storage device 150 through the network 120, and obtain historical behavior information of the target user according to the information (e.g., user name, ID, etc.) of the user. In some embodiments, the historical behavior information includes, but is not limited to, historical search points of interest, click-through rates for historical search points of interest, historical boarding points, and the like, and any combination thereof. In some embodiments, the historical search interest points represent interest points that the user has historically searched or clicked on. The historical search interest points may be various types of words, letters, numbers, characters, etc., or combinations thereof, which are historically input or clicked by the user through the user terminal 130. For example, search points of interest that a user has historically entered or clicked on may include keywords related to hotels, shopping malls, hospitals, cells, stations, schools, intersections, and the like. For example, the search points of interest may be the western university of Qinghua, Beijing collaborator hospital outpatient, etc. In some embodiments, the click-through rate of historical search interest points represents a ratio of the number of times that the user searches/clicks on a target interest point within a preset time period to the total number of times that the user searches/clicks on all interest points within the preset time period. The click rate of the historical search interest points can truly reflect the probability that the target interest point is clicked or searched by the user to a certain extent, and if the click rate of the target interest point is higher, the probability that the target interest point is clicked or retrieved is higher. In some embodiments, the historical boarding points represent boarding points in the user's historical taxi taking service. Further, the historical pick-up points may be selected ones of the historical candidate pick-up points that correspond to the historical orders.

Step 320, determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user. In particular, step 320 may be performed by the candidate point of interest determination module 220.

In some embodiments, the candidate point of interest determination module 220 may obtain at least high heat points of interest and historical search points of interest as candidate points of interest. In some embodiments, the high-heat interest point determining unit 222 may obtain at least one high-heat interest point in an area within a certain range from the user according to the location information of the user. Specifically, the high-heat interest point determining unit 222 may obtain an interest point set within a preset range from the user according to the position information of the user, and determine, based on the heat of each interest point in the interest point set, an interest point with the heat greater than a preset threshold as a high-heat interest point. The popularity of the interest point may be represented as the number of times the interest point is searched or clicked, and if the number of times the interest point is searched or clicked is greater, the popularity is higher. The area within a certain range from the user may be a geographical area with a set coverage area including the location of the user terminal. For example, taking the user position as a center point, obtaining a set of interest points within a preset range (e.g., 600m, 800m) with a certain radius from the center point, and screening the interest points with the heat degree greater than a preset threshold (e.g., 0.7) from the set of interest points as high-heat interest points. In some embodiments, the predetermined range may be a set value, such as 100m, 200m, 500m, 800m, etc., predetermined by the system 100 for determining recommended boarding points. In some embodiments, the preset range is related to a location of a user making an order request. For example, the predetermined range of the busy area where the user location is located may be smaller than the predetermined range of the remote area where the user location is located.

In some embodiments, the historical search interest point determination unit 224 may determine at least one historical search interest point searched by the user according to the historical behavior information of the user. Specifically, the historical search interest point determining unit 224 may obtain a set of interest points that have been searched by the user in the history as the historical search interest points according to the historical behavior information of the user. In some embodiments, the historical search interest point determination unit 224 may obtain historical search interest points within a preset time period (e.g., 1 month, 3 months, half year, one year). In some embodiments, the historical search interest point determining unit 224 may further filter, according to the search heat of the historical search interest points, the historical search interest points whose search heat is greater than a preset threshold as candidate interest points. The search popularity of the historical search interest point can be represented as the number of times that the search interest point is searched historically, and if the number of times that the search interest point is searched historically by the user is more, the search popularity is higher, and the search is more likely to be performed again by the user.

Step 330, inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining at least one recommended interest point from the candidate interest points. In particular, step 330 may be performed by the recommended point of interest determination module 230.

In some embodiments, the recommended interest point determining module 230 may input features of a plurality of candidate interest points to the interest point recommendation model, determine a likelihood score of each of the plurality of candidate interest points, then calculate a similarity of any two candidate interest points of the plurality of candidate interest points, perform deduplication on the plurality of candidate interest points according to the similarities of the any two candidate interest points, and determine at least one candidate interest point as the recommended interest point according to the likelihood score of the candidate interest points after the deduplication. In some embodiments, the recommended interest point determining module 230 may rank the candidate interest points after the deduplication according to the likelihood score of the candidate interest points after the deduplication, and then determine at least one candidate interest point as the recommended interest point according to the ranking result. For more details regarding determining a recommended point of interest from a plurality of candidate points of interest, reference may be made to fig. 4 and its associated description.

In some embodiments, after the recommended point of interest determination module 230 determines the recommended point of interest, the determined recommended point of interest may be sent to the user terminal 130 and displayed. In some embodiments, a prompt mark may also be added to the recommended point of interest so that the user may be guided to focus when displayed on the interface of the user terminal 130.

Step 340, obtaining the search interest point information selected by the user, wherein the search interest point is one of the at least one recommended interest point. In particular, step 340 may be performed by the second obtaining module 240.

In some embodiments, after the recommended interest point determining module 230 determines the recommended interest point, the determined recommended interest point may be sent to the user terminal 130 through the network 120 and displayed, and the user may select at least one interest point for searching based on a recommended interest point list displayed on an interface of the terminal 130. In some embodiments, the second obtaining module 240 may obtain search point-of-interest information selected by the user. For example, the recommended interest point list may include multiple interest points such as beijing university-east 2 gate, beijing university-east subway station, beijing university-geologic building, cheng fu road west gate, cheng road and great city lane intersection, from which a user may select any one of the interest points (e.g., beijing university-east 2 gate) to search, and the second obtaining module 240 may obtain "beijing university-east 2 gate" interest point information selected by the user.

Step 350, determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user. Specifically, step 350 may be performed by the candidate pick-up point determination module 250.

In some embodiments, the candidate pick-up point determination module 250 may obtain at least the relevant pick-up points and the historical pick-up points as candidate pick-up points. In some embodiments, the candidate pick-up points represent preset pick-up location points that may be provided to the user. The candidate boarding points may include, but are not limited to, hospital gates, school gates, park gates, bus stops, community gates, scenic area exits, intersections, and the like, or any combination thereof.

In some embodiments, the related boarding point determining unit 252 may determine at least one related boarding point within a certain range from the search interest point according to the search interest point information transmitted by the user. Specifically, the related boarding point determining unit 252 may obtain a boarding point set within a preset distance range from the search interest point according to the location information of the search interest point sent by the user. For example, at least one pick-up point within a range of radii (e.g., 50m, 100m, 200m) may be determined as a relevant pick-up point, with the location of the search point of interest (e.g., latitude and longitude coordinates of the search point of interest) as a center point. In some embodiments, the set of pick-up points may be a set of location points on a transport services platform (e.g., an online taxi hailing platform) where all passengers may pick up or where the driver is waiting for a passenger to pick up. For example, Beijing collaborates hospitals-Beijing, Western-style of Beijing post and telecommunications university, peace-Western bridge bus station, five-way subway station A, etc.

In some embodiments, historical boarding point determination unit 254 may determine at least one historical boarding point for a user based on historical behavior information for the user. In some embodiments, historical pick-up point determination unit 254 may obtain at least one historical pick-up point for a historical order from a user. In some embodiments, the historical boarding point determination unit 254 may obtain historical boarding points for a preset period of time (e.g., 1 month, 3 months, half year, one year).

Step 360, inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining at least one recommended interest point from the candidate interest points. In particular, step 360 may be performed by the recommended pick-up point determination module 260.

In some embodiments, the recommended boarding point determination module 260 may input the associated features of the plurality of candidate boarding points into the boarding point recommendation model, determine a likelihood score for each of the plurality of candidate boarding points, and determine at least one candidate boarding point as the recommended boarding point based on the likelihood score for each candidate boarding point. Wherein the characteristics associated with the candidate pick-up points may include at least one of: the attribute feature of the candidate boarding point, the relation feature of the candidate boarding point and the user positioning position, the attribute feature of the search interest point, the relation feature of the search interest point and the candidate boarding point and the feature of the historical search interest point. For more details regarding determining a recommended pick-up point from a plurality of candidate pick-up points, reference may be made to fig. 5 and its associated description.

FIG. 4 is an exemplary flow chart illustrating the determination of recommended points of interest from candidate points of interest according to some embodiments of the present application.

Step 410, inputting the features of the candidate interest points into the interest point recommendation model, and determining a likelihood score for each of the candidate interest points. In particular, step 410 may be performed by the candidate point of interest score determination unit 232.

In some embodiments, the candidate interest point score determining unit 232 may extract features of a plurality of candidate interest points and input the extracted features to the interest point recommendation model, obtaining a likelihood score for each of the plurality of candidate interest points. The point of interest recommendation model may be obtained by training an initial machine learning model using data related to a plurality of historical behaviors of the user. The initial Machine learning model may include, but is not limited to, a Classification and Logistic Regression (Logistic Regression) model, a K-Nearest Neighbor algorithm (K-Nearest Neighbor, kNN) model, a Naive Bayes (Naive Bayes, NB) model, a Support Vector Machine (Support Vector Machine, SVM), a Decision Tree (Decision Tree, DT) model, a Random Forest (RF) model, a Regression Tree (Classification and Regression Trees, CART) model, a Gradient Boost Decision Tree (GBDT) model, an xgboost (expandable Gradient), a lightweight Gradient boost Machine (LightGBM), a Gradient boost (boost ), a gblasso (source), a network Neural network, and an Artificial Neural network (Artificial Neural network, and so), and any combination thereof. Preferably, the initial machine learning model may be xgboost. For example only, the data related to the historical behaviors of the user may be used as a training sample, and the candidate interest point score determining unit 232 may extract features from the data related to the historical behaviors of the user by using the extracted features and sample data containing the group Truth as model inputs, and train the xgboost model, with the interest points actually selected and searched in the historical service of the user as a correct criterion (group Truth). In some embodiments, the initial machine learning model may have default settings (e.g., one or more initial parameters) or may be adjusted under different circumstances.

In some embodiments, the features of the candidate interest points may include, but are not limited to, attribute features of the candidate interest points, relationship features of the candidate interest points to user positioning locations, historical click-through rate features of the candidate interest points, and the like. The attribute features of the candidate interest points may include, but are not limited to, candidate boarding points recalled by the candidate interest points, popularity of the candidate interest points, amount of retrieved orders of the candidate interest points in a recent period of time (e.g., 30 days, 60 days, 90 days), and the like. The relationship feature of the candidate interest point and the user positioning position may include, but is not limited to, a distance from the user positioning position to the candidate interest point center, whether the user positioning position and the candidate interest point center are located in the same road segment, and the like. The historical click rate characteristics of the candidate point of interest may include the number (or frequency) of times the candidate point of interest has been historically retrieved or clicked.

In some embodiments, the likelihood score (or referred to as the "hit probability") represents the probability that each candidate point of interest is considered a search point of interest by the user. The higher the likelihood score of the candidate point of interest, the greater the probability that the candidate point of interest is used by the user as a search point of interest. For example, the probability score of the candidate point of interest a is 0.4, the probability score of the candidate point of interest B is 0.6, and the probability score of the candidate point of interest C is 0.9, which indicates that the probability that the user uses the candidate point of interest C as the search point of interest is the greatest.

Step 420, calculating the similarity of any two candidate interest points in the plurality of candidate interest points. In particular, step 420 may be performed by the candidate interest point similarity determination unit 234.

In some embodiments, the similarity of the any two candidate points of interest is related to at least one of: the name similarity of the candidate interest points, the coordinate similarity of the candidate interest points and the address similarity of the candidate interest points. The similarity refers to the degree of closeness of two or more kinds of information. In some embodiments, the candidate interest point similarity determining unit 234 may be configured to determine the name similarity of any two candidate interest points, and determine whether the two candidate interest points are the same according to the name similarity of any two candidate interest points. The name similarity determination method may include, but is not limited to, calculating a Jaccard (Jaccard) similarity coefficient, a cosine similarity, a manhattan distance, a euclidean distance, a minuscule distance, an edit distance, and the like between the two. For example, the names of the candidate interest points may be subjected to text segmentation, keyword information in the candidate interest points is extracted, the keyword information of any two candidate interest points is compared to obtain similarity of the keywords, and the similarity of the names of any two candidate interest points is determined by performing weighted fitting on the similarity of the keywords. In some embodiments, the candidate interest point similarity determining unit 234 may be configured to determine the coordinate similarity of any two candidate interest points, and determine whether the two candidate interest points are the same according to the coordinate similarity of any two candidate interest points. For example, the euclidean distance between any two candidate interest points may be calculated based on the coordinates of any two candidate interest points, and the similarity of the coordinates of any two candidate interest points may be determined based on the euclidean distance. In some embodiments, the candidate interest point similarity determining unit 234 may be configured to determine the address similarity of any two candidate interest points, and determine whether the two candidate interest points are the same according to the address similarity of any two candidate interest points. For example, the addresses of any two candidate interest points may be text-divided, administrative region information, road name information, area number information, and the like in any two candidate interest point addresses are extracted, the administrative region information, road name information, area number information, and the like extracted from any two candidate interest point addresses are respectively compared to determine the similarity of each part, and the similarity of the addresses of any two candidate interest points is determined by performing weighted fitting on the similarities of each part.

And 430, performing duplicate removal on the candidate interest points according to the similarity of any two candidate interest points. In particular, step 430 may be performed by the candidate point of interest deduplication unit 236.

In some embodiments, the candidate interest point deduplication unit 236 may perform deduplication processing on any two candidate interest points whose similarity is greater than a preset similarity threshold according to the similarity of the any two candidate interest points. For example, if the similarity between the point of interest "inception space" and the point of interest "inception space universe technology" is greater than a preset similarity threshold (e.g., 0.7), any one point of interest (e.g., inception space) may be retained as a candidate point of interest, and another candidate point of interest (e.g., inception space universe technology) may be screened from the candidate point of interest set.

Step 440, determining at least one candidate interest point as a recommended interest point according to the probability score of the candidate interest points after the duplication removal. In particular, step 440 may be performed by the recommended point of interest determination unit 238.

In some embodiments, the recommended interest point determining unit 238 may rank the candidate interest points after the deduplication according to the likelihood score of the candidate interest points after the deduplication, and determine at least one candidate interest point as the recommended interest point according to the ranking result. In some embodiments, the recommended point of interest determination unit 238 may determine the recommended point of interest according to a manner of setting a score threshold. The threshold value may be set manually or determined experimentally. The threshold value may be set to 0.6, 0.7, 0.8, 0.9, or the like. For example, when the score threshold is set to 0.7, the recommended point of interest determination unit 238 may determine a candidate point of interest having a likelihood score greater than or equal to 0.7 as the recommended point of interest. In some embodiments, the set threshold may be adjusted according to different scenarios and different goals. For example, if the location of the user when making the service request is frequently visited by the user, the set threshold for determining the recommended point of interest may be adjusted higher. For another example, if the location of the user when making the service request is not frequently visited by the user, the set threshold for determining the recommended point of interest may be adjusted down. In some embodiments, the recommended point of interest determination unit 238 may also determine the top N (e.g., top 10) candidate points of interest with the top scores as the recommended point of interest.

Step 510, inputting the features associated with the plurality of candidate boarding points into a boarding point recommendation model, and determining a likelihood score of each candidate boarding point in the plurality of candidate boarding points. Specifically, step 510 may be performed by the candidate pick-up point score determination unit 262.

In some embodiments, the candidate pick-up point score determining unit 262 may extract features associated with a plurality of candidate pick-up points and input the extracted features to the pick-up point recommendation model to obtain a likelihood score for each of the plurality of candidate pick-up points. The boarding point recommendation model can be obtained by training an initial machine learning model by using relevant data of a plurality of historical behaviors of the user. In some embodiments, the initial Machine learning model may include, but is not limited to, a Logistic Regression (Logistic Regression) model, a K-Nearest Neighbor algorithm (K-Nearest Neighbor, kNN) model, a Naive Bayes (Naive Bayes, NB) model, a Support Vector Machine (SVM), a Decision Tree (Decision Tree, DT) model, a Random Forest (RF) model, a Regression Tree (Classification and Regression Trees, CART) model, a Gradient Boosting Decision Tree (GBDT) model, an xgboost (xtransition Gradient), a Light Gradient Boosting Machine (Light Gradient Boosting Machine, lighting Machine), a Gradient Boosting Machine (Boosting, Light Gradient), a Gradient Boosting Machine (Light, abstract), a Light Gradient Boosting, a network, a Neural network, and an Artificial Neural network, or any combination thereof. Preferably, the initial machine learning model may be a LambdaMART model. For example only, the data related to the historical behaviors of the user may be used as a training sample, and the candidate pick-up point score determining unit 262 may extract the feature associated with the candidate pick-up point from the sample data and train the initial machine learning model based on the feature associated with the candidate pick-up point and the sample data to obtain the pick-up point recommendation model. Wherein the sample data may be relevant data comprising a plurality of identified historical behaviors of the user. The plurality of identified sample data may include positive samples and negative samples. The distance between the real upper vehicle point and the recommended upper vehicle point in the positive sample is less than or equal to a distance threshold (e.g., 30m), and the distance between the real upper vehicle point and the recommended upper vehicle point in the negative sample is greater than the distance threshold (e.g., 30 m). The positive and negative examples may be identified by binary values, respectively. For example, a positive sample may be identified as a "1" and a negative sample may be identified as a "0".

In some embodiments, the features associated with the candidate pick-up points may include, but are not limited to, attribute features of the candidate pick-up points, relationship features of the candidate pick-up points to the user location, attribute features of the search interest points, relationship features of the search interest points to the candidate pick-up points, and features of historical search interest points. In some embodiments, the attribute characteristics of the candidate pick-up points may include, but are not limited to, a heat of the candidate pick-up points, an order volume of the candidate pick-up points over a recent period of time (e.g., 30 days, 60 days, 90 days, 180 days), a median of the offsets of the pick-up points and the billing points in the orders of the candidate pick-up points over the recent period of time (e.g., 30 days, 60 days, 90 days, 180 days), a 30 meter fix rate of the orders of the candidate pick-up points over the recent period of time (e.g., 30 days, 60 days, 90 days, 180 days). In some embodiments, the relationship feature of the candidate boarding point to the user location may be determined based on the location information of the candidate boarding point and the location information of the user, and may include a cross-road relationship feature and/or a distance relationship feature. Specifically, the relationship characteristic between the candidate boarding point and the user positioning position may include, but is not limited to, a distance from the user positioning position to the candidate boarding point, and whether the user positioning position and the candidate boarding point are located on the same road segment. In some embodiments, the attribute characteristics of the search interest points may include, but are not limited to, the number of candidate pick-up points recalled by the search interest points, the popularity of the search interest points, the amount of retrieved orders in the recent period of time (e.g., 30 days, 60 days, 90 days) of the search interest points. In some embodiments, the relationship features of the search interest point and the candidate boarding point may be determined based on the location information of the search interest point and the location information of the candidate boarding point, and may include a cross-route relationship feature and/or a distance relationship feature. Specifically, the relationship between the search interest point and the candidate pick-up point may include, but is not limited to, a ratio of a last period (e.g., 30 days, 60 days, 90 days) of the search interest point to a candidate pick-up point, a ratio of a last period (e.g., 30 days, 60 days, 90 days) of the search interest point to a same-route pick-up point, and a ratio of a last period (e.g., 30 days, 60 days, 90 days) of the search interest point to a same-route pick-up point. In some embodiments, the characteristics of the historical search interest points may include the number of times (or frequency) the historical search interest points were searched/clicked.

And step 520, determining at least one candidate boarding point as a recommended boarding point according to the possibility score of each candidate boarding point. Specifically, step 520 may be performed by the recommended boarding point determining unit 264.

In some embodiments, the recommended pick-up point determining unit 264 may determine at least one candidate pick-up point as the recommended pick-up point according to the likelihood score of each candidate pick-up point. The likelihood score (or "hit probability") may represent the probability that the user will consider each candidate pick-up point as a pick-up point. The higher the likelihood score of the candidate pick-up point, the greater the probability that the candidate pick-up point is used by the user as a pick-up point. For example, the probability score for the candidate boarding point a is 0.4, the probability score for the candidate boarding point B is 0.6, and the probability score for the candidate boarding point C is 0.9, indicating that the probability that the user uses the candidate boarding point C as the boarding point is the greatest.

In some embodiments, the recommended boarding point determination unit 264 may determine the recommended boarding point according to a manner of setting a threshold. The threshold value may be set manually or determined experimentally. The threshold value may be set to 0.6, 0.7, 0.8, 0.9, or the like. For example, when the threshold is set to 0.7, the recommended boarding point determination module 260 may determine a candidate boarding point having a likelihood score greater than or equal to 0.7 as the recommended boarding point. In some embodiments, the set threshold may be adjusted according to different scenarios and different goals. For example, if the search interest point is frequently accessed by the user, the set threshold for determining recommended boarding points may be adjusted higher. As another example, if the search interest point is not frequently accessed by the user, the set threshold for determining recommended pick-up points may be adjusted lower. In some embodiments, the recommended pick-up point determination module 260 may rank the top N (e.g., top 3) candidate pick-up points ranked according to the likelihood scores of the candidate pick-up points as the recommended pick-up points.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) based on the characteristics of user position information, historical behavior information, historical click rate of POI, similarity of POI names and the like, recommending proper POI to the user, improving the effective utilization rate of resources of a POI recommendation list and reducing the time length of issuing orders by the user; (2) by providing the personalized recommended boarding points for the user, the starting point fixed point rate and the trip experience of the user can be improved; (3) the vehicle getting-on point recommendation is carried out based on the historical behavior information of the user and the selection of the user, the vehicle getting-on point recommendation based on the actual requirement of the user is realized, and the individual requirement of the user is met. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visualbasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing processing device or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of determining a recommended pick-up point, the method comprising:

acquiring position information of a user and historical behavior information of the user;

determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user;

inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining at least one recommended interest point from the candidate interest points;

obtaining search interest point information selected by a user, wherein the search interest point is one of the at least one recommended interest point;

determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user;

features associated with the plurality of candidate pick-up points are input to a pick-up point recommendation model, and at least one recommended pick-up point is determined from the plurality of candidate pick-up points.

2. The method of determining recommended pick-up points of claim 1, wherein the historical behavior information comprises at least one of: historical search interest points, click-through rates of historical search interest points, and historical vehicle getting-on points.

3. The method of claim 2, wherein determining a plurality of candidate points of interest based on the location information of the user and historical behavior information of the user comprises:

the candidate interest points at least comprise high-heat interest points and historical search interest points;

determining at least one high-heat interest point within a certain range from the user according to the position information of the user; and the number of the first and second groups,

and determining at least one historical search interest point searched by the user according to the historical behavior information of the user.

4. The method of claim 3, wherein the inputting the characteristics of the plurality of candidate points of interest to a point of interest recommendation model, the determining at least one recommended point of interest from the plurality of candidate points of interest comprises:

inputting the characteristics of the candidate interest points into an interest point recommendation model, and determining a likelihood score of each candidate interest point in the candidate interest points;

Calculating the similarity of any two candidate interest points in the candidate interest points;

according to the similarity of any two candidate interest points, carrying out duplicate removal on the candidate interest points;

and determining at least one candidate interest point as a recommended interest point according to the probability score of the candidate interest points after the duplication removal.

5. The method of claim 4, wherein the characteristics of the candidate points of interest comprise at least one of: the method comprises the following steps of obtaining attribute characteristics of candidate interest points, relation characteristics of the candidate interest points and user positioning positions and historical click rate characteristics of the candidate interest points.

6. The method of claim 4, wherein the similarity between any two candidate points of interest is related to at least one of: the name similarity of the candidate interest points, the coordinate similarity of the candidate interest points and the address similarity of the candidate interest points.

7. The method of claim 4, wherein determining at least one candidate point of interest as a recommended point of interest based on the de-duplicated candidate points of interest likelihood score comprises:

Sorting the candidate interest points after the duplication elimination according to the possibility scores of the candidate interest points after the duplication elimination;

and determining at least one candidate interest point as a recommended interest point according to the sorting result.

8. The method of claim 2, wherein the determining a plurality of candidate boarding points according to the search interest point information sent by the user and the historical behavior information of the user comprises:

the candidate boarding points at least comprise related boarding points and historical boarding points;

determining at least one relevant boarding point within a certain range from the search interest point according to the search interest point information sent by the user; and the number of the first and second groups,

and determining at least one historical boarding point of the user according to the historical behavior information of the user.

9. The method of claim 8, wherein the input of the associated features of the plurality of candidate pick-up points to a pick-up point recommendation model, the determining at least one recommended pick-up point from the plurality of candidate pick-up points comprises:

inputting the associated features of the candidate boarding points into a boarding point recommendation model, and determining a likelihood score of each candidate boarding point in the candidate boarding points;

And determining at least one candidate boarding point as a recommended boarding point according to the possibility score of each candidate boarding point.

10. The method of determining recommended boarding points of claim 9, wherein the candidate boarding point associated features comprise at least one of: the attribute feature of the candidate boarding point, the relation feature of the candidate boarding point and the user positioning position, the attribute feature of the search interest point, the relation feature of the search interest point and the candidate boarding point and the feature of the historical search interest point.

11. The method of claim 10, wherein the relationship between the candidate pick-up point and the user location is characterized by comprising:

determining the relation characteristic of the candidate boarding point and the user positioning position based on the position information of the candidate boarding point and the positioning information of the user;

wherein the relational features comprise at least one of: a cross-road relationship feature and a distance relationship feature.

12. The method of claim 10, wherein the searching for the relationship between the point of interest and the candidate pick-up point comprises:

determining the relation characteristics of the search interest points and the candidate boarding points based on the position information of the search interest points and the position information of the candidate boarding points;

13. A system for determining recommended pick-up points, the system comprising:

the first acquisition module is used for acquiring the position information of a user and the historical behavior information of the user;

the candidate interest point determining module is used for determining a plurality of candidate interest points according to the position information of the user and the historical behavior information of the user;

the recommended interest point determining module is used for inputting the characteristics of the candidate interest points into an interest point recommending model and determining at least one recommended interest point from the candidate interest points;

the second acquisition module is used for acquiring search interest point information selected by a user, wherein the search interest point is one of the at least one recommended interest point;

the candidate boarding point determining module is used for determining a plurality of candidate boarding points according to the search interest point information selected by the user and the historical behavior information of the user;

and the recommended boarding point determining module is used for inputting the characteristics associated with the candidate boarding points into a boarding point recommendation model and determining at least one recommended boarding point from the candidate boarding points.

14. The system for determining recommended boarding points of claim 13, wherein the historical behavior information comprises at least one of: historical search interest points, click-through rates of historical search interest points, and historical vehicle getting-on points.

15. The system for determining recommended boarding points of claim 14, wherein the candidate point of interest determination module further comprises:

the high-heat interest point determining unit is used for determining at least one high-heat interest point within a certain range from the user according to the position information of the user; and the number of the first and second groups,

the historical search interest point determining unit is used for determining at least one historical search interest point searched by the user according to the historical behavior information of the user;

the candidate interest points at least comprise high-heat interest points and historical search interest points.

16. The system for determining recommended boarding points of claim 15, wherein the recommended point of interest determination module further comprises:

a candidate interest point score determining unit, configured to input features of the multiple candidate interest points to an interest point recommendation model, and determine a likelihood score for each of the multiple candidate interest points;

The candidate interest point similarity determining unit is used for calculating the similarity of any two candidate interest points in the candidate interest points;

the candidate interest point duplicate removal unit is used for removing the duplicate of the candidate interest points according to the similarity of any two candidate interest points;

and the recommended interest point determining unit is used for determining at least one candidate interest point as a recommended interest point according to the probability score of the candidate interest points after the duplication removal.

17. The system for determining recommended boarding points of claim 16, characterized in that the features of the candidate points of interest comprise at least one of: the method comprises the following steps of obtaining attribute characteristics of candidate interest points, relation characteristics of the candidate interest points and user positioning positions and historical click rate characteristics of the candidate interest points.

18. The system for determining recommended boarding points of claim 16, characterized in that the similarity of any two candidate points of interest is related to at least one of: the name similarity of the candidate interest points, the coordinate similarity of the candidate interest points and the address similarity of the candidate interest points.

19. The system for determining recommended boarding points of claim 16, characterized in that the recommended point of interest determination unit is further configured to:

20. The system for determining recommended pick-up points of claim 14, wherein the candidate pick-up point determination module further comprises:

the relevant boarding point determining unit is used for determining at least one relevant boarding point within a certain range from the search interest point according to the search interest point information sent by the user; and the number of the first and second groups,

the historical boarding point determining unit is used for determining at least one historical boarding point of the user according to the historical behavior information of the user;

the candidate boarding points at least comprise related boarding points and historical boarding points.

21. The system for determining recommended boarding points of claim 20, wherein the recommended boarding point determination module further comprises:

a candidate boarding point score determining unit, configured to input features associated with the multiple candidate boarding points into a boarding point recommendation model, and determine a likelihood score for each of the multiple candidate boarding points;

And the recommended boarding point determining unit is used for determining at least one candidate boarding point as the recommended boarding point according to the possibility score of each candidate boarding point.

22. The system for determining recommended boarding points of claim 21, wherein the candidate boarding point associated features comprise at least one of: the attribute feature of the candidate boarding point, the relation feature of the candidate boarding point and the user positioning position, the attribute feature of the search interest point, the relation feature of the search interest point and the candidate boarding point and the feature of the historical search interest point.

23. The system for determining recommended boarding points of claim 22, wherein the relationship features of the candidate boarding points to user-located locations comprise:

24. The system for determining recommended boarding points of claim 22, wherein the relational features of the search interest points and candidate boarding points comprise:

25. An apparatus for determining recommended pick-up points, comprising at least one storage medium and at least one processor;

the at least one storage medium is configured to store computer instructions;

the at least one processor is configured to execute the computer instructions to implement the method of determining a recommended pick-up point of any of claims 1-12.

26. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method of determining a recommended pick-up point as claimed in any one of claims 1 to 12.