Point of interest retrieval method, apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a point of interest retrieval method and device, electronic equipment and storage medium, relates to the field of artificial intelligence, in particular to the field of intelligent transportation. The implementation scheme is: obtaining a plurality of candidate points of interest corresponding to a query request of a user; for any candidate point of interest in the plurality of candidate points of interest: fusing query information, client information and point of interest information of the candidate point of interest to generate fused text information of the candidate point of interest and the query request; fusing at least one numerical association information to generate fused numerical information of the candidate point of interest and the query request; fusing the fused text information and the fused numerical information to generate fused information for representing the semantic matching degree of the candidate point of interest and the query request; and determining at least one target point of interest corresponding to the query request from the plurality of candidate points of interest based on the fused information of each candidate point of interest in the plurality of candidate points of interest.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more particularly to the field of intelligent transportation technology, specifically to a point of interest retrieval method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of mobile terminals and computer technology, map application software (APP) has gradually become one of the indispensable tools for people's daily travel. POI (Point of Interest) search is one of the core functions of map APP. Users can search for relevant POIs by entering keywords. The map APP filters the POIs based on the user's input keywords and finally presents the search results and corresponding POI information to the user.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for retrieving points of interest.

[0005] According to one aspect of this disclosure, a method for retrieving points of interest (POIs) is provided, comprising: acquiring a plurality of candidate POIs corresponding to a user's query request, wherein the query request includes query information and the user's client information; for any candidate POI among the plurality of candidate POIs: fusing the query information, the client information, and the POI information of the candidate POI to generate fused text information of the candidate POI and the query request; fusing at least one numerical association information to generate fused numerical information of the candidate POI and the query request, wherein each of the at least one numerical association information is a numerical information used to represent the degree of association between the candidate POI and the query request; fusing the fused text information and the fused numerical information to generate fused information used to represent the degree of semantic matching between the candidate POI and the query request; and determining at least one target POI corresponding to the query request from the plurality of candidate POIs based on the fused information of each candidate POI among the plurality of candidate POIs.

[0006] According to one aspect of this disclosure, an interest point retrieval device is provided, comprising: an acquisition module configured to acquire a plurality of candidate interest points corresponding to a user's query request, wherein the query request includes query information and the user's client information; a generation module configured to perform the following operations for any candidate interest point among the plurality of candidate interest points: fusing the query information, the client information, and interest point information of the candidate interest point to generate fused text information of the candidate interest point and the query request; fusing at least one numerical association information to generate fused numerical information of the candidate interest point and the query request, wherein each of the at least one numerical association information is numerical information used to represent the degree of association between the candidate interest point and the query request; fusing the fused text information and the fused numerical information to generate fused information used to represent the degree of semantic matching between the candidate interest point and the query request; and a determination module configured to determine at least one target interest point corresponding to the query request from the plurality of candidate interest points based on the fused information of each candidate interest point among the plurality of candidate interest points.

[0007] According to one aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described point-of-interest retrieval method.

[0008] According to one aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform the above-described point-of-interest retrieval method.

[0009] According to one aspect of this disclosure, a computer program product is provided, including computer program instructions, wherein the computer program instructions, when executed by a processor, implement the above-described point-of-interest retrieval method.

[0010] According to one or more embodiments of this disclosure, the retrieval efficiency and accuracy of point-of-interest (POI) searches can be improved.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown;

[0014] Figure 2 A flowchart of a point-of-interest (POI) retrieval method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A schematic diagram of the structure of a point-of-interest retrieval model according to some embodiments of the present disclosure is shown;

[0016] Figure 4 A structural block diagram of a point-of-interest (POI) retrieval device according to embodiments of the present disclosure is shown; and

[0017] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0020] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0021] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0022] Map POI search has a wide range of applications in daily life. For example, POI search can be used to quickly locate specific types of places near a destination, thereby meeting various user needs.

[0023] In existing technologies, POI retrieval is typically divided into three processes: coarse ranking, fine ranking, and re-ranking. Each process filters data based on search information. This multi-level sorting method is often time-consuming, resulting in low retrieval efficiency. To reduce time consumption, the complexity of the model is usually reduced, but this can lead to problems such as insufficient model computing power and poor expressive ability, causing some data to be truncated, thus resulting in low accuracy of retrieval results.

[0024] To address the aforementioned issues, this disclosure provides a point-of-interest (POI) retrieval method that improves both the efficiency and accuracy of retrieval results.

[0025] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0026] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes one or more client devices 101, 102, 103, 104, 105 and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105 and 106 can be configured to execute one or more applications.

[0027] In embodiments of this disclosure, client devices 101, 102, 103, 104, 105, and 106, as well as server 120, may run one or more services or software applications that enable the execution of point-of-interest retrieval methods.

[0028] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual and virtual environments. In some embodiments, these services may be provided as web-based services or cloud services, such as to users of client devices 101, 102, 103, 104, 105, and / or 106 under a Software as a Service (SaaS) model.

[0029] exist Figure 1In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 can sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0030] Client devices 101, 102, 103, 104, 105, and / or 106 can provide interfaces that enable users of the client devices to interact with them. The client devices can also output information to the user through these interfaces. Although Figure 1 Only six client devices are described, but those skilled in the art will understand that this disclosure can support any number of client devices.

[0031] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, in-vehicle devices, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices. These computer devices can run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems; or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablets, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing various applications, such as various internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0032] Network 110 can be any type of network well known to those skilled in the art, and can use any of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.) to support data communication. By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, a token ring network, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, Wi-Fi), and / or any combination of these and / or other networks.

[0033] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0034] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0035] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105 and / or 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105 and / or 106.

[0036] In some implementations, server 120 can be a server for a distributed system or a server integrated with blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, designed to address the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0037] System 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. Databases 130 may reside in various locations. For example, a database used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 130 may be of different types. In some embodiments, the database used by server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0038] In some embodiments, one or more of the databases 130 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0039] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0040] According to some embodiments, client devices 101, 102, 103, 104, 105 and / or 106 can send query requests for points of interest to server 120 via network 110. Server 120 can generate corresponding query results based on the received query requests according to the point of interest retrieval method of this disclosure. The server can obtain point of interest information of candidate points of interest from database 130.

[0041] According to other embodiments, the client device (e.g., client device 101) may also generate corresponding query results based on the query request according to the point of interest retrieval method of the present disclosure embodiments.

[0042] Specifically, client devices 101-106 or server 120 can execute the point of interest retrieval method of this disclosure embodiment.

[0043] According to embodiments of this disclosure, a point-of-interest (POI) retrieval method is provided. Figure 2 A flowchart of a point-of-interest (POI) retrieval method 200 according to an embodiment of this disclosure is shown. The entity performing each step of method 200 may be a client (e.g., Figure 1 The client devices 101, 102, 103, 104, 105, and 106 shown can also be servers (e.g., Figure 1 The server 120 shown Figure 1 Other servers not shown in the image).

[0044] like Figure 2 As shown, method 200 includes steps S210-S230.

[0045] In step S210, multiple candidate points of interest corresponding to the user's query request are obtained, wherein the query request includes query information and user client information.

[0046] In step S220, for any candidate interest point among the multiple candidate interest points, perform the following steps S221-S223:

[0047] In step S221, the query information, client information, and interest point information of candidate interest points are fused to generate fused text information of candidate interest points and query request.

[0048] In step S222, at least one numerical correlation information is fused to generate fused numerical information of candidate points of interest and query request, wherein each of the at least one numerical correlation information is numerical information used to represent the degree of correlation between candidate points of interest and query request.

[0049] In step S223, the fused text information and fused numerical information are fused to generate fused information that represents the degree of semantic matching between the candidate points of interest and the query request.

[0050] In step S230, based on the fusion information of each candidate interest point among multiple candidate interest points, at least one target interest point corresponding to the query request is determined from the multiple candidate interest points.

[0051] According to embodiments of this disclosure, for multiple candidate points of interest, their textual and numerical information are respectively fused with the query request, resulting in more accurate fused textual and numerical information. Subsequently, the target point of interest is determined from the multiple candidate points of interest based on the fused information, thereby improving both the accuracy of the point of interest retrieval results and the retrieval efficiency.

[0052] The following details each step of method 200.

[0053] In step S210, multiple candidate points of interest corresponding to the user's query request are obtained, wherein the query request includes query information and user client information.

[0054] According to some embodiments, when a user touches or clicks on the search box of the map APP, it is considered that the user has sent a query request. The query request sent by the user includes corresponding query information and client information. The query information can be understood as the keywords entered by the user for querying points of interest. The query information can be text information directly entered by the user or voice information entered by the user. The voice information can be converted into text information through methods such as voice recognition.

[0055] The client information is the information of the client device used by the user. According to some embodiments, the client information includes at least one of the following: the operating system of the client device used by the user or the first city where the client device is located. Since there are differences in the input methods of the input methods of different operating systems and the positioning accuracy of the navigation device, there will also be corresponding differences in the matching methods of the retrieval results and the query information. The input method can be to display the text after converting pinyin into characters in the search box, that is, only the converted Chinese characters are displayed in the search box. For example, when entering "North" in the search box, the retrieval results include "Beijing...", etc.; it can also be to display the entire process of converting pinyin into characters in the search box. For example, when "b" is displayed in the search box, the retrieval results include "Beijing...", etc.

[0056] According to some embodiments, the database pre-stores the point-of-interest information of multiple points of interest. The point-of-interest information can include at least one of the following: the name, address, label, or the second city where the point of interest is located. Among them, the second city can be the same as the first city or different from the first city. The label can indicate the category of the point of interest, such as a park, an office building, a shopping mall, a hospital, etc. The rich point-of-interest information can comprehensively reflect the characteristics of the point of interest, thereby improving the accuracy of the retrieval results.

[0057] According to some embodiments, according to the matching degree of the query information, client information, and point-of-interest information in the query request, a preliminary retrieval of the points of interest can be performed to obtain multiple candidate points of interest corresponding to the query request. For example, a point of interest whose name contains part or all of the query information and whose location city is the same as the client device can be used as a candidate point of interest.

[0058] In step S220, the following operation steps S221 - S223 are performed for any one of the multiple candidate points of interest:

[0059] In step S221, the query information, client information, and point-of-interest information of the candidate point of interest are fused to generate fused text information of the candidate point of interest and the query request.

[0060] According to some embodiments, the query information, client information, and point-of-interest information are all text-type information. That is, the query information, client information, and point-of-interest information respectively include one or more characters.

[0061] According to some embodiments, fused text information can be generated by simply concatenating and weighting query information, client information, and point-of-interest information.

[0062] According to other embodiments, fused text information can also be generated by inputting query information, client information, and interest point information of candidate interest points into a preset text fusion model. Accordingly, the fused text information can be represented in vector form.

[0063] In this disclosure, any suitable model can be selected as the text fusion model. For example, the Ernie-GeoL model can be selected as the text fusion model. The Ernie-GeoL model has high robustness to representing geographic description text and can efficiently establish the association between text and geographic location, thereby improving the accuracy of fused text information and point-of-interest retrieval results.

[0064] In step S222, at least one numerical correlation information is fused to generate fused numerical information of candidate points of interest and query request, wherein each of the at least one numerical correlation information is numerical information used to represent the degree of correlation between candidate points of interest and query request.

[0065] According to some embodiments, at least one of the numerical association information includes at least one of the following: the distance between the user and the candidate point of interest, the matching degree between the query information and the point of interest information, or the user's user tag, which is constructed based on the user's historical behavior data.

[0066] According to some embodiments, the distance between a user and candidate points of interest (POIs) can be calculated based on their location coordinates. Typically, the query request includes the user's location coordinates (i.e., the location coordinates of the user's client device), and the POI information includes the location coordinates of the POIs. The matching degree between the query information and the POI information can be obtained by calculating the text matching degree between them. For example, the text matching degree can be determined by calculating the literal similarity between the two, or the two can be input into a preset matching model to obtain the corresponding text matching degree. User profiles can be constructed based on the user's historical behavior data. A user profile can include at least one user tag, which can indicate the user's preference for POIs, such as parks, office buildings, shopping malls, etc. User tags can reflect the correlation between POIs and the user. For example, users who frequently navigate to parks will have POIs in the park category ranked higher in the search results determined by their user tags.

[0067] According to some embodiments, fused numerical information can be generated by simply splicing or weighting at least one numerical correlation information.

[0068] According to other embodiments, at least one numerical correlation information can also be input into a preset numerical fusion model to generate fused numerical information. Accordingly, the fused numerical information can be represented in vector form.

[0069] In this disclosure, any suitable model can be selected as the numerical fusion model. The numerical fusion model can be the same as or different from the text fusion model. For example, an RBF (Radial Basis Function Neural Network) neural network can be selected as the numerical fusion model. RBF neural networks have strong nonlinear fitting capabilities, can map arbitrarily complex nonlinear relationships, and can comprehensively represent multiple numerical features and establish correlations between them, thereby improving the accuracy of fused numerical information and interest point retrieval results.

[0070] In step S223, the fused text information and fused numerical information are fused to generate fused information that represents the degree of semantic matching between the candidate points of interest and the query request.

[0071] According to some embodiments, fused information can be generated by simply concatenating or weighting the fused textual and numerical information. Alternatively, the fused textual and numerical information can be input into a preset neural network model to generate fused information, which can be represented as a vector.

[0072] In this disclosure, based on the characteristics of textual and numerical information, the textual and numerical information associated with the query request and candidate points of interest are fused and represented respectively, so that the generated fused information is more accurate, thereby improving the accuracy of the retrieval results.

[0073] In step S230, based on the fusion information of each candidate interest point among multiple candidate interest points, at least one target interest point corresponding to the query request is determined from the multiple candidate interest points.

[0074] According to some embodiments, based on fused information, the matching degree between candidate points of interest and the query request is determined; based on the matching degree between each candidate point of interest and the query request, the candidate points of interest are ranked to obtain a ranking result; and based on the ranking result, at least one target point of interest is determined. Directly ranking and determining the retrieval results based on the matching degree between all candidate points of interest and the query request can effectively shorten the retrieval process and improve the efficiency of point of interest retrieval.

[0075] According to some embodiments, the matching degree between candidate interest points and query requests can be determined by inputting fusion information into a trained fully connected layer, or a correspondence table between fusion information and matching degree can be pre-set, and the matching degree between candidate interest points and query requests can be determined by looking up the table.

[0076] According to some embodiments, the text fusion model, numerical fusion model, and fully connected layer mentioned above can be jointly trained. For example, the text fusion model, numerical fusion model, and fully connected layer can be combined into an interest point retrieval model. By training the interest point retrieval model, trained text fusion model, numerical fusion model, and fully connected layer can be obtained. The interest point retrieval model can be trained, for example, based on sample users' query requests, the list of interest points recommended to sample users, and the clicks of sample users on interest points in the list. The matching degree between interest points clicked by sample users and sample query requests can be marked as 1, and the matching degree between interest points not clicked by sample users and sample query requests can be marked as 0.

[0077] Figure 3 A schematic diagram of the structure of a point-of-interest (POI) retrieval model according to some embodiments of this disclosure is shown. For example... Figure 3 As shown, the point of interest retrieval model includes a text fusion model 310, a numerical fusion model 320, and a fully connected layer 330. The functions and roles of each part of the point of interest retrieval model will be described below in conjunction with method 200.

[0078] In step S221, text information 340 related to the query request and candidate points of interest is input into the text fusion model 310 to generate fused text information. The text information 340 includes query information, client information, and point of interest information of the candidate points of interest.

[0079] In step S222, at least one numerical association information 350 is input into the numerical fusion model 320 to generate fused numerical information. The at least one numerical association information 350 includes at least one of the following: the distance between the user and candidate points of interest, the matching degree between query information and point of interest information, or the user's user tag.

[0080] In step S223, the fused text information and fused numerical information are fused to generate fused information 360, which represents the degree of semantic matching between the candidate points of interest and the query request.

[0081] In step S230, the fused information 360 is input into the fully connected layer 330 to determine the matching degree between candidate interest points and the query request. Based on the matching degree between multiple candidate interest points and the query request, the candidate interest points can be ranked, and the final ranking result is used as the retrieval result for interest point retrieval. Furthermore, the top K (i.e., topK, where K is an integer greater than or equal to 1) candidate interest points ranked highest in the retrieval result can be recommended to the user as target interest points.

[0082] According to embodiments of this disclosure, a point-of-interest (POI) retrieval device is provided. Figure 4 A structural block diagram of a point-of-interest (POI) retrieval device 400 according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 400 includes an acquisition module 410, a generation module 420, and a determination module 430.

[0083] The acquisition module 410 is configured to acquire multiple candidate points of interest corresponding to a user's query request, wherein the query request includes query information and user client information.

[0084] The generation module 420 is configured to perform the following operations for any candidate point of interest among a plurality of candidate points of interest: fusing query information, client information, and point of interest information of the candidate point of interest to generate fused text information of the candidate point of interest and the query request; fusing at least one numerical association information to generate fused numerical information of the candidate point of interest and the query request, wherein each of the at least one numerical association information is numerical information used to represent the degree of association between the candidate point of interest and the query request; and fusing the fused text information and the fused numerical information to generate fused information used to represent the degree of semantic matching between the candidate point of interest and the query request.

[0085] The determination module 430 is configured to determine at least one target interest point corresponding to the query request from multiple candidate interest points based on the fusion information of each candidate interest point among multiple candidate interest points.

[0086] According to some embodiments, the generation module 420 includes: a first generation unit configured to input query information, client information and interest point information of candidate interest points into a preset text fusion model to generate fused text information.

[0087] According to some embodiments, the generation module 420 includes: a second generation unit configured to input at least one numerical correlation information into a preset numerical fusion model to generate fused numerical information.

[0088] According to some embodiments, the determining module 430 includes: a first determining unit configured to determine the matching degree between candidate points of interest and the query request based on fusion information; a sorting unit configured to sort the multiple candidate points of interest based on the matching degree between each candidate point of interest and the query request to obtain a sorting result; and a second determining unit configured to determine at least one target point of interest based on the sorting result.

[0089] According to some embodiments, the first determining unit is further configured to input fused information into a trained fully connected layer to determine the matching degree between candidate interest points and query requests.

[0090] According to some embodiments, client information includes at least one of the following: the operating system of the client device used by the user or the first city where the client device is located, and point of interest information includes at least one of the following: the name, address, tag, or second city where the candidate point of interest is located.

[0091] According to some embodiments, at least one of the numerical association information includes at least one of the following: the distance between the user and the candidate point of interest, the matching degree between the query information and the point of interest information, or the user's user tag, which is constructed based on the user's historical behavior data.

[0092] It should be understood that Figure 4 The various modules or units of the apparatus 400 shown can be connected to the reference. Figure 2 The steps in method 200 described correspond to each other. Therefore, the operations, features, and advantages described above for method 200 also apply to apparatus 400 and its included modules and units. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0093] Although specific functions have been discussed with reference to specific modules above, it should be noted that the functions of the various modules discussed in this article can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module.

[0094] It should also be understood that this article can describe various technologies in the general context of software and hardware components or program modules. The above regarding... Figure 4The various modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuit. For example, in some embodiments, one or more of modules 410-430 can be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes a processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and / or one or more components of other circuitry) and may optionally execute received program code and / or include embedded firmware to perform functions.

[0095] According to embodiments of the present disclosure, an electronic device is also provided, including: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the point-of-interest retrieval method of the present disclosure embodiments.

[0096] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the point-of-interest retrieval method of embodiments of the present disclosure is also provided.

[0097] According to embodiments of this disclosure, a computer program product is also provided, including computer program instructions that, when executed by a processor, implement the point-of-interest retrieval method of embodiments of this disclosure.

[0098] refer to Figure 5 The present invention describes a structural block diagram of an electronic device 500 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0099] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0100] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 507 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, hard disk and optical disk. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, 802.11 devices, Wi-Fi devices, WiMAX devices, cellular communication devices, and / or the like.

[0101] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0107] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0108] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0109] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of this disclosure is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for retrieving points of interest, comprising: Obtain multiple candidate points of interest corresponding to a user's query request, wherein the query request includes query information and the user's client information, wherein the client information is the information of the client device used by the user; For any one of the plurality of candidate interest points: The query information, the client information, and the interest point information of the candidate interest points are fused together to generate fused text information of the candidate interest points and the query request. At least one numerical correlation information is fused to generate fused numerical information of the candidate point of interest and the query request, wherein each of the at least one numerical correlation information is a numerical information used to represent the degree of correlation between the candidate point of interest and the query request. The fused text information and the fused numerical information are fused using concatenation or weighted summation to generate fused information representing the semantic matching degree between the candidate points of interest and the query request. The fused text information, the fused numerical information, and the fused information are represented as vectors. The fusion information of each of the plurality of candidate interest points is input into a trained fully connected layer to determine at least one target interest point corresponding to the query request from the plurality of candidate interest points.

2. The method according to claim 1, wherein, The step of fusing the query information, the client information, and the interest point information of the candidate interest points to generate fused text information of the candidate interest points and the query request includes: The query information, the client information, and the interest point information of the candidate interest points are input into a preset text fusion model to generate the fused text information.

3. The method according to claim 1 or 2, wherein, The step of fusing at least one numerical correlation information to generate fused numerical information between the candidate point of interest and the query request includes: The at least one numerical correlation information is input into a preset numerical fusion model to generate the fused numerical information.

4. The method according to claim 1, wherein, The step of inputting the fusion information of each of the plurality of candidate interest points into a trained fully connected layer to determine at least one target interest point corresponding to the query request from the plurality of candidate interest points includes: The fused information is input into a trained fully connected layer to determine the matching degree between the candidate interest points and the query request; Based on the matching degree between each of the plurality of candidate points of interest and the query request, the plurality of candidate points of interest are sorted to obtain a sorting result; and Based on the sorting results, at least one target point of interest is determined.

5. The method according to claim 1, wherein, The client information includes at least one of the following: the operating system of the client device used by the user or the first city where the client device is located. The point of interest information includes at least one of the following: the name, address, tag, or second city where the candidate point of interest is located.

6. The method according to claim 1, wherein, The at least one numerical association information includes at least one of the following: the distance between the user and the candidate point of interest, the matching degree between the query information and the point of interest information, or the user's user tag, wherein the user tag is constructed based on the user's historical behavior data.

7. An interest point retrieval device, comprising: The acquisition module is configured to acquire multiple candidate points of interest corresponding to a user's query request, wherein the query request includes query information and the user's client information, wherein the client information is the information of the client device used by the user; The generation module is configured to perform the following operation for any one of the plurality of candidate interest points: The query information, the client information, and the interest point information of the candidate interest points are fused together to generate fused text information of the candidate interest points and the query request. At least one numerical correlation information is fused to generate fused numerical information of the candidate point of interest and the query request, wherein each of the at least one numerical correlation information is a numerical information used to represent the degree of correlation between the candidate point of interest and the query request. The fused text information and the fused numerical information are fused using concatenation or weighted summation to generate fused information representing the semantic matching degree between the candidate points of interest and the query request. The fused text information, the fused numerical information, and the fused information are represented as vectors. The determination module is configured to input the fusion information of each of the plurality of candidate interest points into a trained fully connected layer to determine at least one target interest point corresponding to the query request from the plurality of candidate interest points.

8. The apparatus according to claim 7, wherein, The generation module includes: The first generation unit is configured to input the query information, the client information, and the interest point information of the candidate interest points into a preset text fusion model to generate the fused text information.

9. The apparatus according to claim 7 or 8, wherein, The generation module includes: The second generation unit is configured to input the at least one numerical correlation information into a preset numerical fusion model to generate the fused numerical information.

10. The apparatus according to claim 7, wherein, The determining module includes: The first determining unit is configured to input the fused information into a trained fully connected layer to determine the matching degree between the candidate interest points and the query request; A sorting unit is configured to sort the plurality of candidate points of interest based on the matching degree between each candidate point of interest and the query request, to obtain a sorting result; and The second determining unit is configured to determine the at least one target point of interest based on the sorting result.

11. The apparatus according to claim 7, wherein, The client information includes at least one of the following: the operating system of the client device used by the user or the first city where the client device is located. The point of interest information includes at least one of the following: the name, address, tag, or second city where the candidate point of interest is located.

12. The apparatus according to claim 7, wherein, The at least one numerical association information includes at least one of the following: the distance between the user and the candidate point of interest, the matching degree between the query information and the point of interest information, or the user's user tag, wherein the user tag is constructed based on the user's historical behavior data.

13. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising computer program instructions, wherein, The computer program instructions, when executed by a processor, implement the method of any one of claims 1-6.

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