CN111859168A

CN111859168A - Method and system for determining interest points

Info

Publication number: CN111859168A
Application number: CN201910441992.4A
Authority: CN
Inventors: 廖世昌; 陈欢; 马利; 宋奇
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-05-24
Filing date: 2019-05-24
Publication date: 2020-10-30

Abstract

The application provides a method and a system for determining interest points. The method comprises the following steps: acquiring an address search word input by a user and an area where the user is located; determining at least one interest point according to the address search word input by the user and the area where the user is located; wherein the at least one interest point comprises an interest point in a region different from the region in which the user is located.

Description

Method and system for determining interest points

Technical Field

The present application relates to the field of internet, and in particular, to a method and a system for determining a point of interest.

Background

In recent years, with the rapid development of mobile communication technology, a great amount of application software based on intelligent terminals is emerging. Car-call applications are one of those that are popular with the general public. The passenger inputs the information of the starting place and the destination through the client and sends the vehicle using request. The driver can take over the drive according to the starting place information of the passenger and complete the service request according to the destination information.

The passenger can search the starting place or/and the destination by inputting a search word in the mobile terminal before placing an order. Generally, the system will generate a list of points of interest based on the user's search terms, and the passenger may select one of the points of interest and place an order. The traditional taxi calling application generally only provides the same-city interest point query service, namely, passengers can only query interest points in the areas where the passengers are located, however, for passengers with cross-area service requirements, the interest points of the passengers are not in the areas where the passengers are located, and the traditional taxi calling application cannot provide cross-area interest point query results. It is therefore desirable to provide a method and system that provides cross-regional point of interest queries.

Disclosure of Invention

According to the method and the device, the cross-region intention of the user can be identified by analyzing the address search word input by the user and the region where the user is located, and the cross-region interest point is searched and provided for the user.

An aspect of the present application provides a method of determining a point of interest. The method comprises the following steps: acquiring an address search word input by a user and an area where the user is located; determining at least one interest point according to the address search word input by the user and the area where the user is located; wherein the at least one interest point comprises an interest point in a region different from the region in which the user is located.

In some embodiments, the method further comprises: and processing the address search word input by the user and the area where the user is located by using an interest point determination model so as to determine at least one interest point. The interest point determination model is obtained by training using historical cross-regional order information; the historical cross-region order information comprises address retrieval words input by a historical user, a region where the historical user is located and interest points of the historical user, wherein the region where the historical user is located when the historical user initiates the historical cross-region order and the region where the interest points of the historical user are located are different regions.

In some embodiments, the point of interest determination model is a FastText model.

In some embodiments, the method further comprises: and performing word segmentation processing on the address search word input by the user, wherein the interest point determining model determines the at least one interest point based on a word segmentation processing result and the area where the user is located.

In some embodiments, the performing a word segmentation process on the address search word input by the user includes: and performing word segmentation processing on the address search word input by the user by using an N-element grammar model.

Another aspect of the present application provides a point of interest determination system. The system comprises: the system comprises a user information acquisition module and an interest point determination module. The user information acquisition module is used for acquiring address search words input by a user and an area where the user is located; the interest point determining module is used for determining at least one interest point according to the address search word input by the user and the area where the user is located; wherein the at least one interest point comprises an interest point in a region different from the region in which the user is located.

In some embodiments, the interest point determination module is configured to process the address search term input by the user and the area where the user is located by using an interest point determination model, and further determine at least one interest point. The interest point determination model is obtained by training using historical cross-regional order information; the historical cross-region order information comprises address retrieval words input by a historical user, a region where the historical user is located and interest points of the historical user, wherein the region where the historical user is located when the historical user initiates the historical cross-region order is different from the region where the interest points selected by the historical user are located.

In some embodiments, the point of interest determination module is further to: and performing word segmentation processing on the address search word input by the user, and determining the at least one interest point by using the interest point determination model based on a word segmentation processing result and the area where the user is located.

In some embodiments, the point of interest determination module is further to: and performing word segmentation processing on the address search word input by the user by using an N-element grammar model.

Another aspect of the present application provides a point of interest determination apparatus. The apparatus includes at least one processor and at least one memory for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement the operations in the point of interest determination methods described above.

Another aspect of the present application provides a computer-readable medium. The computer-readable medium stores computer instructions that, when executed by a processor, perform operations in the point of interest determination method described above.

Drawings

FIG. 1 is a schematic diagram of a point of interest determination system, shown in accordance with some embodiments of the present application;

FIG. 2 is a block diagram of an exemplary computing device for implementing a dedicated system of the subject technology;

FIG. 3 is a block diagram of an exemplary mobile device for implementing a dedicated system of the subject technology;

FIG. 4 is a block diagram of a point of interest determination system according to some embodiments of the present application;

FIG. 5 is an exemplary flow chart of a method of point of interest determination shown in accordance with some embodiments of the present application; and

FIG. 6 is an exemplary flow chart illustrating a method for determining a point of interest via a point of interest determination model 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.

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.

Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on a client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.

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, 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.

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.

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, regional 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. Such as other similar tracked vehicles.

Fig. 1 is a schematic view illustrating an application scenario of a point of interest determination system according to some embodiments of the present application. The point of interest determination system 100 may provide point of interest query services to the passenger to assist the passenger in quickly determining the address of the order. The point of interest determination system 100 may be an online service platform for internet services. For example, the point of interest determination system 100 may be an online transportation service platform for a transportation service. In some embodiments, the point of interest determination system 100 may be applied to taxi 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 point of interest determination system 100 may also be applied to designated driving services, courier delivery, take-out, and the like. In alternative embodiments, the point of interest determination system 100 may also be applied in the fields of gaming services, travel (e.g., tourism) services, education (e.g., online education) services, and the like. The point of interest determination system 100 may be an online service platform including a server 110, a network 120, a user terminal 130, and a storage device 140. The server 110 may include a processing device 112.

In some embodiments, server 110 may be used to process information and/or data related to point of interest determination. The server 110 may be a stand-alone server or a group of servers. The set of servers can be centralized or distributed (e.g., server 110 can be a distributed system). The server 110 may be regional or remote in some embodiments. For example, server 110 may access information and/or data stored in user terminal 130, storage device 140, through network 120. In some embodiments, server 110 may be directly connected to user terminal 130, storage device 140 to access information and/or material stored therein. In some embodiments, the server 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.

In some embodiments, the server 110 may include a processing device 112. The processing device 112 may process data and/or information related to the service request to perform one or more of the functions described herein. For example, the processing device 112 may receive a car use request signal transmitted by the user terminal 130 to provide the user with the point of interest inquiry service. In some embodiments, the processing device 112 may include one or more sub-processing devices (e.g., a single core processing device or a multi-core processing device). By way of example only, the processing device 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may facilitate the exchange of data and/or information. In some embodiments, one or more components of the point of interest determination system 100 (e.g., server 110, user terminal 130, storage device 140) may send data and/or information to other components of the point of interest determination system 100 via the network 120. In some embodiments, network 120 may be any type of wired or wireless network. For example, 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 one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or Internet switching points 120-1, 120-2, …, through which one or more components of the point of interest determination system 100 may connect to the network 120 to exchange data and/or information. FIG. 2 is a block diagram of an exemplary computing device 200 for implementing a dedicated system of the subject technology.

In some embodiments, the user may obtain the point of interest through the user terminal 130. In some embodiments, the user terminal 130 may include one or any combination of a mobile device 130-1, a tablet 130-2, a laptop 130-3, an automotive built-in device 130-4, and the like. 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, and the like, or any combination thereof. In some embodiments, the smart furniture device may include a smart lighting device, a control device for a smart appliance, a smart monitoring device, a smart television, a smart camera, an intercom, or 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 comprise a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a POS device, or the like, or any combination thereof. In some embodiments, the metaverse device and/or the augmented reality device may include a metaverse helmet, metaverse glasses, metaverse eyewear, augmented reality helmets, augmented reality glasses, augmented reality eyewear, and the like, or any combination thereof. In some embodiments, user terminal 130 may include a location-enabled device to determine the location of the user and/or user terminal 130.

Storage device 140 may store data and/or instructions. In some embodiments, storage device 140 may store the profile retrieved from user terminal 130. In some embodiments, storage device 140 may store information and/or instructions for execution or use by server 110 to perform the example methods described herein. In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-and-write memory (e.g., random access memory, RAM), read-only memory (ROM), the like, or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like, or any combination thereof.

In some embodiments, a storage device 140 may be connected to network 120 to communicate with one or more components of point of interest determination system 100 (e.g., server 110, user terminal 130, etc.). One or more components of the point of interest determination system 100 may access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components (e.g., server 110, user terminal 130) in the address determination system 100. In some embodiments, the storage device 140 may be part of the server 110.

In some embodiments, one or more components in the point of interest determination system 100 (e.g., the server 110, the user terminal 130, etc.) may have access to the storage device 140. In some embodiments, one or more components in the point of interest determination system 100 (e.g., the server 110, the user terminal 130, etc.) may read and/or modify information related to the user and/or the common general knowledge when one or more conditions are satisfied. For example, after the in-vehicle service is over, the server 110 may read and/or modify information for one or more users.

In some embodiments, the exchange of information between one or more components in the point of interest determination system 100 may be accomplished by requesting a service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or an intangible product. Tangible products may include food, medicine, merchandise, chemical products, appliances, clothing, vehicles, houses, luxury items, and the like, or any combination thereof. Intangible products may include one or any combination of service products, financial products, knowledge products, internet products, and the like. The product may be any software and/or application used in a computer or mobile handset, for example. The software and/or applications may be related to social interaction, shopping, transportation, entertainment, learning, investment, etc., or any combination thereof. In some embodiments, the transportation-related software and/or applications may include travel software and/or applications, vehicle scheduling software and/or applications, mapping software and/or applications. In the vehicle scheduling software and/or application, the vehicle may include one or any combination of a carriage, a human powered vehicle (e.g., bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, special car, etc.), a train, a subway, a ship, an aircraft (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), and the like.

FIG. 2 is a schematic diagram of an exemplary computing device according to some embodiments of the present application. In some embodiments, server 110 and/or user terminal 130 may be implemented on computing device 200. For example, the processing device 112 may implement and perform the functions of the processing device 112 disclosed herein on the computing device 200.

As shown in fig. 2, computing device 200 may include a processor 220, a read only memory 230, a random access memory 240, a communication port 250, an input/output interface 260, and a hard disk 270.

Processor 220 may execute the computational instructions (program code) and perform the functions of address determination system 100 described herein. The computing instructions may include programs, objects, components, data structures, procedures, modules, and functions (which refer to specific functions described herein). For example, the processor 220 may process image or text data obtained from any other component of the point of interest determination system 100. In some embodiments, processor 220 may include microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), Central Processing Units (CPU), Graphics Processing Units (GPU), Physical Processing Units (PPU), microcontroller units, Digital Signal Processors (DSP), Field Programmable Gate Array (FPGA), Advanced RISC Machines (ARM), programmable logic devices, any circuit or processor capable of executing one or more functions, or the like, or any combination thereof. For illustration only, the computing device 200 in fig. 2 depicts only one processor, but it should be noted that the computing device 200 in the present application may also include multiple processors.

The memory (e.g., Read Only Memory (ROM)230, Random Access Memory (RAM)240, hard disk 270, etc.) of computing device 200 may store data/information obtained from any other component of the point of interest determination system 100. Exemplary ROMs may include Mask ROM (MROM), Programmable ROM (PROM), erasable programmable ROM (PEROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. Exemplary RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDR SDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance (Z-RAM), and the like.

The input/output interface 260 may be used to input or output signals, data, or information. In some embodiments, the input/output interface 260 may enable a user to interface with the address determination system 100. In some embodiments, input/output interface 260 may include an input device and an output device. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, and the like, or any combination thereof. Exemplary output devices may include a display device, speakers, printer, projector, etc., or any combination thereof. Exemplary display devices may include Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) based displays, flat panel displays, curved displays, television equipment, Cathode Ray Tubes (CRTs), and the like, or any combination thereof. The communication port 250 may be connected to a network for data communication. The connection may be a wired connection, a wireless connection, or a combination of both. The wired connection may include an electrical cable, an optical cable, or a telephone line, etc., or any combination thereof. The wireless connection may include bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobile networks (e.g., 3G, 4G, or 5G, etc.), etc., or any combination thereof. In some embodiments, the communication port 250 may be a standardized port, such as RS232, RS485, and the like. In some embodiments, the communication port 250 may be a specially designed port. For example, the communication port 250 may be designed in accordance with the digital imaging and medical communication protocol (DICOM).

Fig. 3 is a block diagram of a schematic 300 of exemplary hardware and/or software of a mobile device according to some embodiments of the present application.

As shown in fig. 3, the mobile device 300 may include a communication unit 310, a display unit 320, a Graphics Processor (GPU)330, a Central Processing Unit (CPU)340, an input/output unit 350, a memory 360, a storage unit 370, and the like. In some embodiments, operating system 361 (e.g., iOS, Android, Windows Phone, etc.) and application programs 362 may be loaded from storage unit 370 into memory 360 for execution by CPU 340. The applications 362 may include a browser or an application for receiving imaging, graphics processing, audio, or other related information from the address determination system 100.

To implement the various modules, units and their functionality described in this application, a computing device or mobile device may serve as a hardware platform for one or more of the components described in this application. The hardware elements, operating systems, and programming languages of these computers or mobile devices are conventional in nature, and those skilled in the art will be familiar with these techniques to adapt them to the on-demand service system described herein. A computer with user interface elements may be used to implement a Personal Computer (PC) or other type of workstation or terminal device, and if suitably programmed, may also act as a server.

FIG. 4 is a block diagram of a point of interest determination system according to some embodiments of the present application. The point of interest determination system 400 includes a user information acquisition module 410, a point of interest determination module 420, a historical order acquisition module 430, and a training module 440.

The user information obtaining module 410 may obtain order information of the user. In some embodiments, the user information obtaining module 410 may obtain information such as an address search term input by the user, a region where the user is located, and the like. The address search term refers to the content input in the address box before the user places an order, and for example, the content input in the starting box and/or the destination box by the user can be the content. In some embodiments, the manner in which the user enters the address term may include, but is not limited to, any combination of one or more of typing, handwriting, selection, voice, scanning, and the like. Specifically, the typing input may include english input, chinese input, and the like depending on the language. The selection input may include selecting a keyword from a selection list, and the like. The scan input may include a scan barcode input, a scan two-dimensional code input, a scan text input, a scan picture input, and the like. For example, the address search word may be a Chinese character directly input by handwriting by the user. For another example, the address search term may be a character or letter recognized from a user's scanned picture input. For another example, the address search word may be a character or letter recognized from a voice input by the user.

In some embodiments, the user's locale may be the locale of the user when placing an order. The area where the user is located includes, but is not limited to, province, city or county, etc. where the user is located. Specifically, when the user places an order, the user terminal 130 may obtain a location where the user is located through a positioning technology, where the location includes information of an area where the user places the order. In some embodiments, the area of the user may be a standing area (e.g., a standing city) of the user. For example, the user may fill in his or her own standing area information on the relevant service platform, or the user may set his or her own standing address, company address, and other common addresses. For another example, the user information obtaining module 410 may obtain a historical order of the user, and analyze a staying area of the user according to the historical order.

The point of interest determination module 420 may determine the point of interest of the user. In some embodiments, a Point of interest (POI) may refer to an information Point that includes information such as name, category, latitude and longitude. The point of interest may be a place that the user is interested in or wants to go to, including, but not limited to, the user's target origin, destination, approach, etc., for example. In some embodiments, the points of interest may be geographically divided regions such as five-way businessy, Nanjing street, etc., or administrative divisions such as provinces, prefectures, cities, counties, etc. For example only, the point of interest may be a city, such as Beijing; or a prefecture of a city, such as the Haita district of Beijing City; or a more specific address such as Nanmen of Beijing university, Hai lake district, Beijing, etc. In some embodiments, the point of interest determination module 420 may determine at least one point of interest of the user according to the address search term input by the user and the area where the user is located. In some embodiments, a user may have a need for cross-regional transportation services, such as a need to travel to an area outside of the area. To meet the cross-regional needs of the user, the point of interest determination module 420 may determine points of interest in at least one other region outside of the region in which the user is located. In some embodiments, the interest point determining module 420 may determine a plurality of candidate regions, determine a probability of interest to each candidate region by the user, and determine the top K candidate regions with the highest probability as the regions that are likely to be interested by the user, where K is an integer greater than or equal to 1. For example, the area where the user is located is "Suzhou", the entered address term is "airport", and the point of interest determination module 420 may determine that the area in which the user may be interested is Shanghai, Wuxi, Nanjing, or Hangzhou according to "Suzhou" and "airport". In some embodiments, the point of interest determination module 420 may perform cross-region point of interest retrieval. Specifically, after K destination areas that may be of interest to the user are determined, the interest point determining module 420 performs an interest point search in the corresponding interest point database of each of the possible destination areas to determine the interest points that may be of interest to the user. For example, the area where the user is located is "suzhou", the input address search word is "airport", the point-of-interest determination module 420 may search through the point-of-interest databases of shanghai, wuxi, nanjing, and hangzhou, and determine that the point of interest that the user may be interested in is shanghai rainbow bridge international airport, shanghai purdong international airport, wuxiong international airport, nanjing international airport, and hangzhou international airport. In some embodiments, the points of interest determined by the point of interest determination module 420 to be of possible interest to the user may be displayed on the user terminal 130 for selection by the user.

In some embodiments, the interest point determination module 420 may determine the cross-region interest point based on the address search term of the user and the search of the region where the user is located in the cross-region list associated with the region where the user is located. Wherein the cross-region list associated with the region in which the user is located may include at least one other region related to the region in which the user is located. In some embodiments, the list of trans-regional may be determined based on a trans-regional identification method. For example, the interest point determining module 420 may obtain a historical cross-region order, determine the number of orders from the region where the user is located to other regions according to the historical cross-region order, further determine cross-region probabilities from the region where the user is located to other regions, determine other regions where the cross-region probabilities satisfy a certain condition as regions related to the region where the user is located, and form a cross-region list by the related regions. In still other embodiments, the interest point determining module 420 may first perform a search in a local interest point database of an area where the user is located according to a search word input by the user, determine a first number of local interest points, determine whether the first number is smaller than a set threshold, and if the first number is smaller than the set threshold, the interest point determining module 420 may determine a second number of cross-area interest points according to the search word of the user and a cross-area list associated with the area where the user is located. Specifically, the interest point determining module 420 may obtain an area outside the area where the user is located from the cross-area list associated with the area where the user is located, and perform retrieval in the interest point database of each area according to a retrieval word of the user, thereby determining the cross-area interest point. In some embodiments, the point of interest determination module 420 may merge local points of interest with cross-region points of interest.

In some embodiments, the point of interest determination module 420 may determine the point of interest of the user through a point of interest determination model. Specifically, the interest point determination model may be trained by using historical cross-region order information, and then the address search word input by the user and the interest point determination model input by the region where the user is located may be input, and the interest point of the user may be output by the model. Historical cross-regional order information may be obtained by the historical order acquisition module 430. The training of the point of interest determination model may be implemented by the training module 440. For more on determining the user's point of interest by the point of interest determination model, see fig. 6 and its description.

The historical order acquisition module 430 may acquire historical order information. The historical order information includes, but is not limited to, address search words input by the historical users, areas where the historical users are located, points of interest of the historical users, and the like. In some embodiments, the region in which the historical user is located may be the region in which the historical user placed an order. Specifically, the terminal used by the historical user when placing the order can obtain the place where the historical user is located through a positioning technology, and the place contains the information of the area where the historical user is located when placing the order. In some embodiments, the area in which the historical user is located may be a standing area of the historical user. For example, the history user may fill in his or her own standing area information on the service platform, or the history user may set his or her own home address and a commonly used address of a company address. For another example, the historical order obtaining module 430 may obtain a plurality of historical orders of the historical user, and analyze a standing area of the historical user according to the plurality of historical orders. The interest points of the historical users include, but are not limited to, the starting place, the destination, the approach place, and the like of the historical users. In some embodiments, the historical user interest point may be a region, or a region of a region, or a more specific address, or the like. In some embodiments, the interest points of the historical users may be locations such as interest points determined when the historical users place orders or actual interest points. Taking the destination as an example, the interest point of the historical user may be a destination determined when the historical user places an order or an actual getting-off place. In some embodiments, the historical order acquisition module 430 may acquire historical order information from an internal storage device (e.g., storage device 140) or other external storage device of the point of interest determination system 100 via the network 120.

In some embodiments, the historical order acquisition module 430 may acquire historical cross-regional order information. The cross-region order refers to an order of a region where the interest point of the user is located and the region where the user is located. For example, if the area where the user is located is Tianjin and the order destination is Beijing capital International airport, the order is a cross-regional order. For another example, if the area where the user is located is Tianjin and the order is originated from the international airport of capital of Beijing, the order is a cross-regional order. In some embodiments, the historical order acquisition module 430 may acquire historical cross-regional orders over a past period of time (e.g., half a year, 3 months, 1 month, 1 week, etc.).

The training module 440 may train the point of interest determination model. In some embodiments, the training module 440 may train the point of interest determination model according to historical cross-region order information. Specifically, the training module 440 may train the interest point determination model by using the address search term input by the historical user and the area where the historical user is located as the input of the interest point determination model, and using the interest point of the historical user as the output of the model. The interest point determination model includes, but is not limited to, a fast text classification (FastText) model, a Continuous Bag of Words (CBOW) model, a Skip-grammar (Skip-gram) model, a Support Vector Machine (SVM) model, a logistic regression (logistic regression) model, a Neural Network (Neural Network) model, and the like.

For example only, the interest point determination model may be derived by training a FastText model using historical cross-region order information. FastText is a tool for text classification and computation of word vectors. After training, the FastText model inputs a text and can output the probability that the text belongs to different classes. Specifically, FastText includes a three-layer architecture: an input layer, a hidden layer, and an output layer. The input layer may receive continuous text (e.g. a sentence), the input layer may extract feature vectors of a plurality of word sequences from the received continuous text, the feature vectors are mapped to the hidden layer through linear transformation, the hidden layer is further mapped to the output layer through a non-linear activation function, and the output layer finally obtains probabilities that the continuous text belongs to different categories.

In some embodiments, the address search words input by the historical users can be firstly subjected to word segmentation, and then the result of the word segmentation is input into the FastText model to be trained in combination with the regions where the historical users are located and the interest points of the historical users. Word segmentation refers to the division of a continuous text (e.g., a sentence) into sequences of words according to a certain specification. For example, the text "beijing airport entry," the result after the word segmentation process may be "beijing/airport/entry. The word segmentation method includes, but is not limited to, a dictionary-based word segmentation method, an understanding-based word segmentation method, a statistics-based word segmentation method (e.g., an N-gram (N-gram) model, a hidden markov model, etc.), a rule-based word segmentation method (e.g., a minimum matching algorithm, a maximum matching algorithm, a reverse maximum matching algorithm, a word-by-word matching algorithm, an N-shortest path word segmentation algorithm, etc.), and the like.

For the FastText model, the self-contained N-gram function can also be used for segmenting words of the input text, so that the feature vector of each word sequence in the input text is extracted. The traditional word segmentation method is to divide an input text into a plurality of words, so that the sequence relation among the words cannot be represented, an N-gram can divide continuous N words in the input text into a word sequence, and the word segmentation result can represent the sequence relation among the words, wherein N is an integer greater than or equal to 1. For example, the input text is "beijing road KFC", the word segmentation result of the conventional word segmentation method may be "beijing road/KFC", and the word segmentation result of the N-gram is "beijing road/road KFC" (when N ═ 2). The system may identify that the user only wants to inquire Kendeji (KFC) in Beijing if a traditional word segmentation method is adopted, so that interest points related to the KFC are only recalled from an interest point database corresponding to the Beijing city; and by adopting the N-gram method, the system can recall the relevant interest points from the interest point database corresponding to a plurality of cities. In some embodiments, the value of N in the N-gram may be set empirically. For example, N may be 4, 3, 2, or the like.

In some embodiments, the region where the historical user is located can be further encoded, merged with the word segmentation processing result, and input into the FastText model together with the interest points of the historical user for training. The region where the historical user is located may be encoded using various encoding schemes including, but not limited to, one-hot encoding, binary encoding, gray encoding, and the like. In some embodiments, when the codes of the areas where the historical users are located and the word segmentation processing results are combined, the word segmentation processing results can be distinguished from the codes of the areas where the users are located by adding special symbols. For example, if the address search word input by the historical user is "diamond building 1/building", the word segmentation processing result is "diamond/building/1/building", and if the code of the area where the user is located is "1", the word segmentation processing result is combined with the code of the area where the user is located to be "diamond/building/1", and two different meanings of "1" cannot be distinguished. In order to distinguish the historical user location area code from the segmentation process result, a special symbol "&" may be added at the time of combination, and the final combination result is "diamond/building/1/number building/& 1 &", so that "1" in the segmentation process result can be distinguished from "1" of the user location area code.

In some embodiments, when the trained interest point is used to determine the model, the input of the model may be the word segmentation processing result of the search word input by the user and the area where the user is located (or the code of the area where the user is located), and the output is the interest point of the user. In other embodiments, the point of interest determination model itself has a word segmentation function. In some embodiments, when the trained interest point is used to determine the model, the input of the model may be a search word input by the user and the area where the user is located (or the code of the area where the user is located), and the output is the interest point of the user.

It should be noted that the above description of the point of interest determination system 400 is merely for convenience of description and is not intended to limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of the various blocks may be implemented or any block may be added or deleted without departing from such teachings. For example, the historical order acquisition module 430 and/or the training module 440 may be omitted. As another example, the historical order acquisition module 430 and/or the training module 440 may be combined with the point of interest determination module 420 into one module.

FIG. 5 is an exemplary flow chart of an address determination method according to some embodiments of the present application.

Step 501, obtaining an address search word input by a user and an area where the user is located. In some embodiments, step 501 may be performed by user information acquisition module 410.

The address search term may be the content entered by the user in the origin box and/or the destination box. In some embodiments, the manner in which the user enters the address term may include, but is not limited to, any combination of one or more of typing, handwriting, selection, voice, scanning, and the like. Specifically, the typing input may include english input, chinese input, and the like depending on the language. The selection input may include selecting a keyword from a selection list, and the like. The scan input may include a scan barcode input, a scan two-dimensional code input, a scan text input, a scan picture input, and the like. For example, the address search word may be a Chinese character directly input by handwriting by the user. For another example, the address search term may be a character or letter recognized from a user's scanned picture input. For another example, the address search word may be a character or letter recognized from a voice input by the user. In some embodiments, the user's locale may be the locale where the user placed an order. In some embodiments, the area in which the user is located may be a standing area of the user. In some embodiments, the address search term input by the user and the area where the user is located may be directly transmitted by the user terminal 130 to the user information obtaining module 410 through the network 120. In some embodiments, the address search word input by the user and the area where the user is located may be transmitted to the storage device 140 through the user terminal 130 and stored therein, and the user information obtaining module 410 may obtain the address search word input by the user and the area where the user is located from the storage device 140.

Step 503, determining at least one interest point according to the address search word input by the user and the area where the user is located, wherein the at least one interest point comprises an interest point of which the area is different from the area where the user is located. In some embodiments, step 503 may be performed by the point of interest determination module 420.

In some embodiments, the point of interest may be a region, or a more specific address in a region. Some passengers may have a demand for cross-regional transportation services, and the point of interest determination module 420 may determine cross-regional points of interest (i.e., points of interest in a region different from the user). In some embodiments, the interest point determining module 420 may determine at least one candidate region and determine a probability of interest to each candidate region for the passenger, and determine the top K regions with the highest probability as the destination region, where K is an integer greater than or equal to 1. In some embodiments, the point of interest determination module 420 may perform cross-region point of interest retrieval. Specifically, after K destination areas that may be interested by the user are determined, the interest point determining module 420 performs interest point retrieval in the corresponding interest point database of each of the possible destination areas to determine the interest points that the user may want to go.

In some embodiments, the interest point determining module 420 may determine the first number of local interest points by searching in a local interest point database of the area where the user is located according to the search term input by the user. In some embodiments, the point of interest determination module 420 may determine a second number of cross-region points of interest based on the search terms of the user, the cross-region list associated with the region in which the user is located, and the first number. For example, the interest point determining module 420 may determine whether the first number is smaller than a set threshold, and if the first number is smaller than the set threshold, the interest point determining module 420 may determine a second number of cross-region interest points according to the search term of the user and a cross-region list associated with the region where the user is located. Specifically, the interest point determining module 420 may obtain an area outside the area where the user is located from the cross-area list associated with the area where the user is located, and perform retrieval in the interest point database of each area according to a retrieval word of the user, thereby determining the cross-area interest point. In some embodiments, the point of interest determination module 420 may merge local points of interest with cross-region points of interest.

In some embodiments, the determination of the point of interest may be implemented by a model. Specifically, the interest point determination model may be trained by using historical cross-region order information, and then the address search word input by the user and the interest point determination model input by the region where the user is located may be input, and the interest point of the user may be output by the model. The historical order information includes, but is not limited to, address search words input by the historical users, areas where the historical users are located, points of interest of the historical users, and the like.

In some embodiments, the input of the interest point determination model may be a word segmentation processing result of a search word input by a user and an area where the user is located, and the output is the interest point of the user. In some embodiments, the interest point determination model has a word segmentation function, and can perform word segmentation processing on a search word input by a user. Taking the FastText model as an example, the FastText model has an N-gram word segmentation function for selection, and when the N-gram word segmentation function is started, the FastText model can automatically segment the address search words input by the user. In some embodiments, when the trained interest point is used to determine the model, the input of the model may be a search word input by the user and the area where the user is located (or the code of the area where the user is located), and the output is the interest point of the user.

FIG. 6 is an exemplary flow chart of a method for determining a point of interest via a point of interest determination model according to some embodiments of the present application.

Step 601, obtaining an address search word input by a user and an area where the user is located. In some embodiments, step 601 may be performed by the user information acquisition module 410. Step 601 is similar to step 501 and will not be described herein.

Step 603, performing word segmentation processing on the address search word input by the user. In some embodiments, step 603 may be performed by the point of interest determination module 420. In some embodiments, the determination of the points of interest is implemented by a model, which itself may have the function of word segmentation. For more details on the word segmentation process, reference may be made to fig. 4 and the description thereof, which are not described herein again.

Step 605, based on the word segmentation processing result and the area where the user is located, K destination areas or specific points in each area that the user may be interested in are determined, where K is an integer greater than or equal to 1. In some embodiments, step 605 may be performed by the point of interest determination module 420. Specifically, the word segmentation processing result and the area where the user is located may be input into the interest point determination model, and the model may output K possible destination areas that the user wants to go, such as shanghai, chengdu, and the like, or output specific locations belonging to the K possible destination areas, such as shanghai nan jing road, chengdu city nan jing road. For example, if the interest point determination model is a FastText model, the model may determine, based on the input word segmentation processing result and the region where the user is located, the probability that the user wants to go to each region or a specific location in each region, and determine the top K regions with the highest probability or the specific location in each region as the interest points that the user may be interested in.

In some embodiments, when the K destination areas that the user may be interested in are determined in step 605, step 607 may be further included, where a point of interest search is performed in the corresponding point of interest database of each of the possible destination areas to determine the point of interest that the user may be interested in. In some embodiments, step 607 may be performed by point of interest determination module 420. In some embodiments, the point of interest determination module 420 may recall a list of points of interest from the corresponding point of interest database for each possible destination area, the list including at least one point of interest. The method of recalling the interest point list may be any one of the methods in the prior art. For example, the point of interest determining module 420 may analyze an address search word input by a user, perform rewriting and error correction, perform search in the point of interest database, recall at least one point of interest from the database, and sort the at least one point of interest to obtain the point of interest list. In some embodiments, the point of interest determination module 420 may calculate similarity of the address search term input by the user to a plurality of points of interest. For example, the interest point determining module 420 may determine the text similarity of the address search term input by the user to the plurality of interest points through a text similarity calculation model. The text similarity calculation model may include calculating one or more arbitrary combinations of 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. In some embodiments, the interest point determining module 420 may also determine similarity between the address search term input by the user and the interest point in other manners, which is not limited in this application. For example, the interest point determining module 420 may determine similarity of the address search term input by the user to a plurality of interest points through a semantic model.

It should be noted that the above description of the point of interest determination method 600 is merely for convenience of description and is not intended to limit the present application to the scope of the illustrated embodiments. It will be understood by those skilled in the art that, having the benefit of the teachings of this method, any combination of steps may be used or any steps may be added or deleted without departing from such teachings. For example, in some embodiments, step 603 may be omitted, i.e., the address term entered by the user is not subject to word segmentation. For another example, in some embodiments, step 607 may be omitted if step 605 determines that it is a particular location in the areas that may be of interest to the user.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) effectively identifying the cross-regional intention of the user according to the address search word input by the user and the region where the user is located; (2) cross-region interest point query service can be provided; (3) the accuracy of the prediction result of the interest point is high; (4) the passenger trip experience is improved. 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, Visual Basic, Fortran 2003, Perl, COBOL 2002, 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 server. 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 have been discussed in the foregoing disclosure by way of example, it should 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 server 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 and 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.

Claims

1. A method for point of interest determination, comprising:

acquiring an address search word input by a user and an area where the user is located;

determining at least one interest point according to the address search word input by the user and the area where the user is located; wherein the at least one interest point comprises an interest point in a region different from the region in which the user is located.

2. The method of point of interest determination as claimed in claim 1, the method further comprising:

processing the address search word input by the user and the area where the user is located by using an interest point determination model, and further determining at least one interest point;

the interest point determination model is obtained by training using historical cross-regional order information; the historical cross-region order information comprises address retrieval words input by a historical user, a region where the historical user is located and interest points of the historical user, wherein the region where the historical user is located when the historical user initiates the historical cross-region order and the region where the interest points of the historical user are located are different regions.

3. The method of claim 2, wherein the point of interest determination model is a FastText model.

4. The method of point of interest determination as claimed in claim 2, wherein the method further comprises:

And performing word segmentation processing on the address search word input by the user, wherein the interest point determining model determines the at least one interest point based on a word segmentation processing result and the area where the user is located.

5. The method of claim 4, wherein the word segmentation processing of the address search word input by the user comprises:

and performing word segmentation processing on the address search word input by the user by using an N-element grammar model.

6. A point of interest determination system, comprising: the system comprises a user information acquisition module and an interest point determination module;

the user information acquisition module is used for acquiring address search words input by a user and an area where the user is located;

the interest point determining module is used for determining at least one interest point according to the address search word input by the user and the area where the user is located; wherein the at least one interest point comprises an interest point in a region different from the region in which the user is located.

7. The system of claim 6, wherein the interest point determination module is configured to process the address search term input by the user and the area where the user is located by using an interest point determination model to determine at least one interest point;

The interest point determination model is obtained by training using historical cross-regional order information; the historical cross-region order information comprises address retrieval words input by a historical user, a region where the historical user is located and interest points of the historical user, wherein the region where the historical user is located when the historical user initiates the historical cross-region order is different from the region where the interest points selected by the historical user are located.

8. The point of interest determination system of claim 7, wherein the point of interest determination model is a FastText model.

9. The point of interest determination system of claim 7, wherein the point of interest determination module is further to:

and performing word segmentation processing on the address search word input by the user, and determining the at least one interest point by using the interest point determination model based on a word segmentation processing result and the area where the user is located.

10. The point of interest determination system of claim 9, wherein the point of interest determination module is further to:

11. An apparatus for point of interest determination, the apparatus comprising at least one processor and at least one memory;

The at least one memory is for storing computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the operations of any of claims 1-5.

12. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform operations according to any one of claims 1 to 5.