CN112100522A - Method, apparatus, device and medium for retrieving points of interest - Google Patents
Method, apparatus, device and medium for retrieving points of interest Download PDFInfo
- Publication number
- CN112100522A CN112100522A CN202010967344.5A CN202010967344A CN112100522A CN 112100522 A CN112100522 A CN 112100522A CN 202010967344 A CN202010967344 A CN 202010967344A CN 112100522 A CN112100522 A CN 112100522A
- Authority
- CN
- China
- Prior art keywords
- interest
- target
- vector
- interest point
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 239000013598 vector Substances 0.000 claims abstract description 289
- 230000004044 response Effects 0.000 claims abstract description 7
- 230000006399 behavior Effects 0.000 claims description 24
- 230000015654 memory Effects 0.000 claims description 19
- 238000009795 derivation Methods 0.000 claims 1
- 230000008569 process Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/205—Parsing
- G06F40/216—Parsing using statistical methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Life Sciences & Earth Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the disclosure discloses a method, a device, equipment and a medium for retrieving interest points, and relates to the technical field of electronic maps and big data. The specific implementation scheme is as follows: in response to receiving target request information sent by a client, generating a vector query request aiming at a user portrait vector corresponding to user information in the target request information; querying a user portrait vector corresponding to the vector query request from a preset user portrait vector database; inquiring a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; and sending the target interest point vector to the client to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map. The implementation method can obtain a large number of interest points associated with the user portrait vector.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for retrieving points of interest.
Background
More and more users acquire information about a place by querying points of Interest (POI) using an electronic map. The interest points are mainly used for map navigation and positioning.
At present, (1) in a general search scene, a search term input by a user is subjected to semantic analysis and understanding, and an interest point is searched according to the distance from the current position of the user or a set positioning point. (2) In the thematic map scenario, a large number of points of interest are included in the thematic map.
Disclosure of Invention
A method, apparatus, device, and storage medium for retrieving a point of interest are provided.
According to a first aspect, there is provided a method for retrieving a point of interest, which may comprise: in response to receiving target request information sent by a client, generating a vector query request aiming at a user portrait vector corresponding to user information in the target request information; the target request information comprises a name of a target thematic map corresponding to a target scene; querying a user portrait vector corresponding to the vector query request from a preset user portrait vector database; inquiring a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map; and sending the target interest point vector to the client to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map.
According to a second aspect, there is provided an apparatus for retrieving a point of interest, which may comprise: a request generation module configured to generate a vector query request for a user portrait vector corresponding to user information in target request information in response to receiving the target request information sent by the client; the target request information comprises a name of a target thematic map corresponding to a target scene; a first query module configured to query a user portrait vector corresponding to a vector query request from a preset user portrait vector database; the second query module is configured to query a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map; and the vector sending module is configured to send the target interest point vector to the client so as to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map.
According to a third aspect, there is provided an electronic device, which may comprise: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is an exemplary system architecture to which the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for retrieving points of interest in accordance with the present disclosure;
FIG. 3 is a schematic diagram of an application scenario of the method for retrieving points of interest according to the present disclosure;
FIG. 4 is a schematic diagram illustrating one embodiment of an apparatus for retrieving points of interest, according to the present disclosure;
FIG. 5 is a block diagram of an electronic device for implementing a method for retrieving points of interest of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 illustrates an exemplary system architecture 100 to which embodiments of the disclosed method or apparatus for retrieving points of interest may be applied.
As shown in fig. 1, the system architecture 100 may include a client 101, a network 102, a retrieval server 103, and a retrieval thematic map server 104. Network 102 serves as a medium for providing communication links between clients 101 and servers 103 and 104. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may write a search interest point request related to a target scene to the search server 103 through the client 101. The client 101 may be configured in a terminal device (e.g., a computer with a display screen), and the user inputs a retrieval interest point request related to the target scene through an input interface of the terminal device, and the terminal device sends the retrieval interest point request related to the target scene to the retrieval server 103.
The retrieval server 103 may be a server that provides various services, such as a background server that provides support for the client 101. The background server may process the received search interest point request related to the target scene and feed back the corresponding search data to the client 101.
The user can also write target request information to the retrieval thematic map server 104 through the client 101. The client 101 may be configured in a terminal device (e.g., a computer having a display screen), and the user inputs target request information through an input interface of the terminal device, and the terminal device sends the target request information to the retrieval thematic map server 104.
The retrieval thematic map server 104 may be a server that provides various services, such as a background server that provides support to the client 101. The background server may process the received target request information and feed back the corresponding target interest point vector to the client 101.
The terminal device may be hardware or software. When the terminal device is hardware, it may be various electronic devices supporting navigation or mapping applications, including but not limited to smart terminals, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal device is software, the terminal device can be installed in the electronic devices listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
The search server 103 and the search thematic map server 104 may be hardware or software. When the search server 103 and the search thematic map server 104 are hardware, they may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the retrieval server 103 and the retrieval thematic map server 104 are software, they may be implemented as a plurality of pieces of software or software modules for providing distributed services, for example, or as a single piece of software or software module. And is not particularly limited herein. The search server 103 and the search thematic map server 104 may be the same server.
In practice, the method for retrieving the points of interest provided by the embodiment of the present disclosure may be executed by the retrieval thematic map server 104, and the apparatus for retrieving the points of interest may also be disposed in the retrieval thematic map server 104.
It should be understood that the number of clients, networks, and servers in FIG. 1 is merely illustrative. There may be any number of clients, networks, and servers, as desired for an implementation.
In the technical scheme of obtaining the interest points in the prior art, in a general search scene, the interest points are searched out by performing semantic analysis and understanding on search words input by a user and according to the distance from the current position of the user or a set positioning point; alternatively, in the thematic map scenario, a large number of points of interest are included in the thematic map. In a specific scenario, for example, in a hotel, a gourmet, or a travel scenario, a user needs to make a decision according to a large number of points of interest; since the number of the searched interest points is small, the user needs to frequently drag the electronic map in the searching process, click the searching again, and return to the interest point corresponding to the searching, which prolongs the decision time and reduces the user experience. However, the thematic maps are designed and developed independently, the entry of the thematic maps is hidden, and the thematic maps can be accessed by clicking many times by a user. Therefore, each time a new thematic map is developed, version is required; however, a new entrance is opened at the client, the development process is long, the developed thematic maps have no pertinence to different users, and the thematic maps corresponding to all the users are the same.
Referring to fig. 2, fig. 2 illustrates a flow 200 of one embodiment of a method for retrieving points of interest in accordance with the present disclosure. The method for retrieving points of interest comprises the following steps:
step 201: in response to receiving the target request information sent by the client, a vector query request for the user portrait vector corresponding to the user information in the target request information is generated.
In this embodiment, an executing subject (e.g., the retrieval thematic map server 104 shown in fig. 1) of the method for retrieving the interest points may generate a vector query request for the user portrait vector according to the user information in the target request information when receiving the target request information sent by a client (e.g., the client 101 shown in fig. 1), where the vector query request corresponds to the user information in the target request information.
The target request information may be request information for requesting the execution main body to acquire the point of interest from the client. The user information in the target request information may be a device number or a user identifier of the client, where the user identifier may include a user account and user biometric information, where the user account may be a number indicating an identity of the user, and may be, for example, a user ID (identity), a mobile phone number of the user, and the like. The user biometric information may be biometric information indicating an identity of the user, and may be, for example, a fingerprint and/or an iris of the user, where the fingerprint may be set at the client by the user, and the fingerprint may be used to power on and unlock a device where the client is located; the iris can be set by a user at a client, and the iris can be used for starting up and unlocking the equipment where the client is located.
Optionally, the executing entity may first generate a vector query request for a user image vector corresponding to a user account according to the user account; then, the execution body sends a vector query request for the user portrait vector to a preset user portrait vector database, so as to query the user portrait vector corresponding to the vector query request from the preset user portrait vector database.
Optionally, the vector query request may be generated in the following ways:
the first mode is as follows: the vector query request is generated based on the device number and/or the user identification of the client in a direct combination, namely the vector query request comprises the device number and/or the user identification of the client.
In the second mode, different weights are set for the equipment number and the user identification of the client, and then the vector query request is generated by adopting weighted summation.
In a specific example, the device number of the client accounts for 30% of the weight, the user identifier accounts for 70%, and when the similarity is calculated subsequently, the similarity with a certain user portrait vector in a preset user portrait vector database is calculated respectively for the device number and the user identifier of the client; and then, carrying out weighted summation according to the similarity corresponding to the equipment number of the client and the similarity corresponding to the user identification to obtain the similarity between the vector query request and the certain user image vector. The above is only an example, and it is not limited that the device number of the client is weighted by 30% and the user identifier is weighted by 70%.
In this embodiment, before the vector query request is generated in step 201, the retrieval interest point request may also be sent to a retrieval server (for example, the retrieval server 103 shown in fig. 1) when the client responds to the received retrieval interest point request related to the target scene sent by the user, and when the retrieval server determines that the target retrieval word in the retrieval interest point request hits the target topic map, the receiving retrieval server returns the name and the interest point of the target topic map matched with the target retrieval word, thereby implementing the acquisition of the interest point corresponding to the retrieval interest point request related to the target scene.
The target scene may be a scene corresponding to a target search term input by the user in a search bar of the electronic map on the client, for example, if the user inputs a target search term such as "university", "shopping", "restaurant", or "hotel" in the search bar of the electronic map, the corresponding target scene is a "university" scene, a "shopping" scene, a "restaurant" scene, or a "hotel" scene.
The search interest point request related to the target scene may be a request for inputting a "university", "shopping", "restaurant", or "hotel" target search term in a search field of the electronic map, and transmitting the search term to the search server after the user clicks a search tool of the electronic map.
The above-mentioned search server may be configured to, upon receiving the search interest point request, determine whether a target search term in the search interest point request hits the name of the target thematic map based on its internal determination mechanism, for example, after a user inputs a "university" target search term in a search field of an electronic map, click a search tool, send the search interest point request generated based on the "university" target search term to the search server, determine, by the search server, whether the "university" target search term in the search interest point request hits the "university" thematic map, and return the name "university" of the "university" thematic map to the client if the search server determines that the "university" target search term corresponding to the "university" keyword is stored therein.
The target search term may be a keyword input in a search bar of the electronic map, for example, a "university", "shopping", "restaurant", or "hotel" keyword. The target topic map may be named "university" in the "university" topic map. The interest point may be a house, a shop, a mailbox, a bus station, etc. in the geographic information system, and will not be described herein again.
Step 202: a user portrait vector corresponding to the vector query request is queried from a predetermined database of user portrait vectors.
In this embodiment, an executing entity (e.g., the retrieval thematic map server 104 shown in fig. 1) of the method for retrieving the interest points may send a vector query request to a predetermined user portrait vector database, and query a user portrait vector corresponding to the vector query request from the predetermined user portrait vector database. The predetermined user portrait vector database is used for storing user portrait vectors, the user portrait abstracts each concrete information of the user into tags, and the tags are used for concreting the user image. Thus, the user representation may characterize specific information of a multi-dimensional user, e.g., specific information of a user in a dimension of life, work, education, entertainment, etc.
Optionally, before performing step 202, the method for retrieving the point of interest may further include: setting a first preset similarity. The first preset similarity can be set according to expert experience or application scenes; it is within the scope of the present disclosure that the user portrait vector corresponding to the vector query request can be eventually queried from the database of user portrait vectors according to the first predetermined similarity.
After the first preset similarity is set, the vector query request is matched with each user portrait vector in the preset user portrait vector database one by one, for example, the vector query request can be converted into a vector query request in a vector form, and then the similarity between the vector query request in the vector form and each user portrait vector in the preset user portrait vector database is calculated; similarity between the vector query request and the user portrait vector may be determined, for example, based on the magnitude of the distance; wherein, the smaller the distance is, the greater the similarity between the vector query request and the user image vector is.
The method for calculating the similarity may be a method for calculating the similarity in the prior art or a future developed technology, and the disclosure is not limited thereto. For example, the similarity calculation method may be implemented by using a Pearson Correlation (Pearson Correlation) or a cosine similarity method.
In a specific example, for the vector query request generated in step 201 based on the first manner, the calculating the similarity between the vector query request and each user portrait vector in the preset user portrait vector database may include: for example, a similarity between a device number of the client and each user portrait vector in the predetermined user portrait vector database may be calculated, and if the similarity between a certain user portrait vector in the predetermined user portrait vector database and the device number of the client is greater than a predetermined threshold, the user portrait vector may be returned to the execution main body to complete the query of the user portrait vector from the predetermined user portrait vector database.
It should be noted that the vector query request may be a vector query request in a vector form or a vector query request in a non-vector form, and when the vector query request is a vector query request in a non-vector form, the vector query request needs to be converted into a vector query request in a vector form by using TF-IDF (Term Frequency-Inverse file Frequency) or word2 vector.
Step 203: inquiring a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map.
In this embodiment, the execution main body may send the queried user portrait vector and the name of the target thematic map to a preset interest point vector database, so as to obtain a target interest point vector from the preset interest point vector database, where the target interest point vector is matched with the queried user portrait vector and the name of the target thematic map, and return the matched target interest point vector to the execution main body.
The interest point vector database can be used for storing interest point vectors, and the interest point vectors can describe various attributes of interest points by using a multidimensional matrix; such as category attributes of stores, hotels, supermarkets, universities, etc., location attributes along streets, near colleges, etc. The target interest point may be one or more interest point vectors in preset interest point vector data, and the one or more interest point vectors in the preset interest point vector data correspond to the names of the queried user portrait vector and target thematic map.
The target thematic map is any one of a plurality of thematic maps which are classified in advance according to experience or practical application scenes by a person skilled in the art. For example, the thematic map may be a "university" thematic map, a "hotel" thematic map, a "cate" thematic map, an "entertainment" thematic map, and the like. It is to be understood that the above-described thematic drawings are exemplary only, and are not limiting upon the embodiments of the present disclosure, which include only the above-described thematic drawings.
Here, the interest point indicated by the interest point vector in the interest point vector database is associated with the target thematic map indicated by the name of the target thematic map. Optionally, the type of the interest point indicated by the interest point vector in the interest point vector database is the same as the type of the target topic map, for example, the interest point indicated by the interest point vector in the interest point vector database is "a" university, and the target topic map indicated by the name of the target topic map is the "university" topic map at this time, that is, the type of the interest point indicated by the interest point vector in the interest point vector database and the type of the target topic map indicated by the name of the target topic map are both universities.
Optionally, before performing step 203, the method for retrieving the point of interest may further include: and setting a second preset similarity. The setting of the second preset similarity may be set according to expert experience or application scenarios, and the target interest point vector corresponding to the vector query request may be found within the scope of the present disclosure as long as the target interest point vector can be finally queried from the preset user portrait vector database according to the second preset similarity.
After setting the second preset similarity, matching the name and the user portrait vector of the target thematic map with each interest point vector in a preset interest point vector database one by one, for example, calculating the similarity between the name and the user portrait vector of the thematic map and each interest point vector in the preset interest point vector database; for example, the name of the target thematic map and the similarity between the user portrait vector and the point of interest vector may be determined based on the magnitude of the distance.
The method for calculating the similarity may be a method for calculating the similarity in the prior art or a future developed technology, and the disclosure is not limited thereto. For example, the similarity calculation method may be implemented by using a Pearson Correlation (Pearson Correlation) or a cosine similarity method.
It should be noted that, before performing step 203, the method for retrieving the point of interest may further include: and generating an interest point vector query request according to the name of the target thematic map and the user portrait vector. The request for generating the interest point vector may be: and directly combining the name of the target thematic map and the user portrait vector to generate an interest point vector query request, wherein the interest point vector query request is a request comprising the name of the target thematic map and the user portrait vector.
After generating the interest point vector request, a target interest point vector corresponding to the interest point vector request may be queried from a preset interest point vector database based on the interest point vector request.
Step 204: and sending the target interest point vector to the client to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map.
In this embodiment, the execution subject may send the target interest point vector to the client, so that when the client receives the target interest point vector, the target interest point indicated by the target interest point vector is displayed on the screen of the client by using the target thematic map indicated by the name of the target thematic map according to the target interest point vector.
After the point of interest corresponding to the retrieval point of interest request related to the target scene is obtained, the following information can be displayed on a screen of the client: the point of interest corresponding to the retrieve point of interest request associated with the target scene, and the target point of interest indicated by the target point of interest vector.
The method for retrieving the interest points provided by the above embodiments of the present disclosure adopts a combination of extensive retrieval and thematic maps, and obtains the user portrait vector corresponding to the vector query request from the user portrait vector database through the vector query request generated by the user information in the target thematic map; and finally, displaying the target interest points indicated by the target interest point vectors by adopting the target thematic map indicated by the name of the target thematic map, so that the user does not need to perform additional operation while obtaining a large number of interest points, does not depend on publishing and has a short development period. Moreover, the user portrait is introduced, so that the thematic map becomes a personalized thematic map for the user, and the user experience is improved.
In some optional implementations of the present disclosure, the point of interest vector database in step 203 may be determined based on the following steps: determining interest point target data based on the obtained interest point basic data; wherein, the interest point basic data at least comprises: attribute tags for points of interest and user behavior data for points of interest. According to the following parameters of a user portrait set and interest point target data which are matched with the user behavior data aiming at the interest points in a preset time period: displaying the priority and the attribute labels, and generating the interest point vectors in a preset interest point vector database.
In this implementation manner, the execution subject may determine the target data of the interest point based on the obtained basic data of the interest point; wherein, the interest point basic data at least comprises: attribute tags of the interest points and user behavior data for the interest points; then, according to the user portrait set matched with the user behavior data aiming at the interest point in the preset time period and the following parameters of the interest point target data: displaying the priority and the attribute labels, and generating the interest point vectors in a preset interest point vector database.
The point of interest basic data may be data acquired locally or remotely from the execution subject, and the point of interest basic data may be data related to a point of interest without any processing performed thereon. The interest point target data may be data after performing some operation on the interest point basic data, such as sorting, setting priority, and the like.
The attribute tag of the interest point can be used for representing the attribute of the interest point; for example, category attributes, such as university category, hotel category; location attributes, such as a street-present category, a far-from-urban category; the "old font size" and the like. The user behavior data for the interest point may be historical operation data of the user for the interest point, for example, clicking, browsing, and the like for the interest point. Optionally, the number of attribute tags of the interest points may be set according to the specific classification fineness, which is not described herein again.
The user image set in the preset time period may be a data set in which the user operates the electronic map within a preset time, for example, an operation on the electronic map within three 1 month is performed, and an operation on the electronic map within five 1 month is performed.
The display priority can be used for determining whether the interest points can be displayed on the current interface of the client when the user subsequently searches the interest points; for example, during retrieval, the interest point with high display priority is displayed on the current interface of the client, and the interest point with low display priority needs to be viewed by dragging the electronic map. The current interface of the client is an interface displayed when the user searches the interest point for the first time.
In this implementation manner, for different target thematic maps, the interest point vectors corresponding to different types of interest point basic data may be set in an offline or online manner, so as to provide a data source when a subsequent user needs to obtain a large number of interest points.
In some optional implementations of the disclosure, the point of interest base data further includes: scene labels of the points of interest; then, determining the interest point target data based on the obtained interest point basic data may include: firstly, classifying interest point basic data according to scene labels of interest points to obtain different types of interest point basic data; and then setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data aiming at the interest points to obtain the interest point target data.
In this implementation manner, the execution subject may classify the basic data of the interest points according to the scene tags of the interest points included in the basic data of the interest points, and delete the basic data of the interest points that are not matched with the scene tags of the interest points in the basic data of the interest points to obtain the basic data of the interest points of different categories; and then, after obtaining the interest point basic data of different categories, setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data aiming at the interest points, and finally obtaining the interest point target data. The scene tag of the interest point may be a tag corresponding to the target scene, for example, the target scene is a "university" scene, and then the scene tag is a "university" tag.
Optionally, the number of the scene tags of the interest points may be set according to the specific classification fineness, which is not described herein again.
In a specific example, if the user subsequently needs a target thematic map corresponding to another target scene, the above steps may be adopted to generate an interest point vector corresponding to different types of interest point basic data in the other target scene, so as to provide a data source for obtaining interest points in the target thematic map corresponding to the other target scene; alternatively, a plurality of tags are provided, for example, the plurality of tags may correspond to "hotel", "university", "gourmet", and the like, respectively. Based on the steps, the interest point vectors corresponding to the interest point basic data of different categories in the scenes of hotel, university and food can be respectively generated, so that data sources can be provided for the interest points in different target thematic maps, and the operation time is short.
Optionally, the executing entity may generate the interest point vector at regular time.
In a specific example, the interest point vectors are generated every day, so that the preset interest point vector database is updated according to the generated interest point vectors; or before updating the preset interest point vector database, judging whether the preset interest point vector database stores the newly generated interest point vector, if so, not updating the preset interest point vector database; if the information is not stored, the newly generated interest point vector is updated to the preset interest point vector database, so that the diversity of the interest point vector in the preset interest point vector database is ensured, and meanwhile, the updating time is saved.
The method for classifying the basic data of the interest points may be a method for classifying the basic data of the interest points in the prior art or a future developed technology, and the disclosure does not limit this. For example, the method for classifying the basic data of the point of interest may be implemented by classifying the basic data of the point of interest using a supervised model or by setting scene labels to the basic data of the point of interest in advance.
In the implementation mode, data which are not matched with the scene labels of the interest points in the interest point basic data are screened out through the scene labels, so that a high-precision data source is provided for a preset interest point vector database, and a data source is further provided for subsequently acquiring the high-precision interest points.
In some optional implementations of the disclosure, the user behavior data for the point of interest includes at least one of: click quantity; searching quantity; and retrieving the name of the object.
In this implementation manner, before the executing body sets the display priority based on at least one of the click amount, the retrieval amount, and the name of the retrieval object, the method for retrieving the interest point vector may further include: and presetting a priority initial value, and then updating the priority initial value according to the score of the retrieval related information to obtain the priority score so as to judge whether to display the interest point on the current interface of the client preferentially or not according to the priority score.
In this implementation, the click rate may be the number of clicks on the interest point in the electronic map. The retrieval amount may be the number of times the points of interest are retrieved in the electronic map. The name of the retrieval object may be a name corresponding to the interest point, for example, the interest point is university, and then the name of the retrieval object may be "a" university; wherein "A" is the name of university.
In the implementation manner, the click quantity, the retrieval quantity and the name of the retrieval object are fused, so that the display priorities of the basic data of the interest points of different types are accurately set in the following process, and the interest points which can be preferentially displayed on the current interface of the client can be determined according to the display priorities of the interest points when a user searches the interest points in the following process.
After the priority initial value is set, setting the priority includes the following cases:
in the first case: the execution main body can set the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the priority parameter corresponding to the click rate and the priority parameter corresponding to the retrieval amount when the user behavior data of the interest point comprises the click rate and the retrieval amount, and the interest point target data is obtained. Optionally, the priority parameter corresponding to the click rate and the priority parameter corresponding to the retrieval rate are subjected to weighted summation, and the display priority of the interest point basic data of each category in the interest point basic data of different categories is set.
In one particular example, when both the click volume and the search volume are high, then the display priority for the point of interest is also high. For example, if two interest points are very close to each other, it needs to determine whether the user searches for an interest point in the electronic map by priority of the two interest points, and preferentially displays the interest point with high priority on the current interface of the client, so as to recommend the interest point more suitable for the user to the user, thereby improving user experience.
In the implementation mode, the display priorities of the interest point basic data of different categories can be set through the click quantity and the retrieval quantity, so that when a user retrieves the interest points, the interest points with high priorities can be screened from the interest points to be displayed, and the retrieval requirements of the user can be accurately matched.
In the second case: after setting the initial value of the priority, when the user behavior data for the interest point includes a click amount, a retrieval amount, and a name of a retrieval object, the execution main body may set a display priority of the interest point basic data of each category in the interest point basic data of different categories according to the click amount, the retrieval amount, and the name of the retrieval object, to obtain the interest point target data, which may specifically include: judging whether the name of the retrieval object hits a preset high-frequency word or not; when the name of the retrieval object hits a preset high-frequency word, setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the priority parameter corresponding to the click rate, the priority parameter corresponding to the retrieval rate and the priority parameter corresponding to the frequency of the high-frequency word, and obtaining the interest point target data.
In the implementation manner, the display priority of the basic data of the interest point can be further judged according to the click quantity, the retrieval quantity and whether the name of the retrieval object hits the preset high-frequency word, so that the display priorities of the basic data of the interest points of different categories can be subsequently and accurately set, and when a subsequent user retrieves the interest point, the interest point capable of being preferentially displayed on the current interface of the client can be determined according to the display priority of the interest point.
For example, when the click amount and the search amount are the same, the display priority may be set based on whether the name of the point of interest hits the high frequency word, and if the name of the point of interest hits the high frequency word, a higher display priority may be set for the point of interest. The preset high-frequency words can be words with higher searching frequency in the electronic map.
In a specific example, in some regions, such as remote regions, the click rate and the search rate are very small, and if priorities are set according to the click rate and the search rate, many points of interest can be displayed only when the electronic map is enlarged to be very large. In order to avoid the display of the happy points in some areas of the electronic map, whether the name of the retrieval object is a word with high heat degree, such as an "AA" hotel, for a remote area, although the click rate and the retrieval rate are small, if a certain point of interest (one or more points of interest) located in the remote area, such as "AA", hits a preset word with high heat degree, a higher display priority is given to the point of interest, so that the point of interest can be preferentially displayed on the current interface of the client during retrieval, rather than leaving the corresponding area of the remote area on the electronic map open.
Referring to fig. 3, fig. 3 shows a schematic diagram of an application scenario of the method for retrieving points of interest according to the present disclosure.
Step 301: the user initiates a retrieval interest point request related to a target scene at a client side and sends the interest point request to a retrieval server.
In the implementation mode, after the user can input the target search word in the search bar of the electronic map on the client, the user clicks the search tool, and then the client can send the search interest point request related to the target scene to the search server.
The method for sending the search interest point request to the search server may be a method for sending the search interest point request to the search server in the prior art or a future developed technology, and the disclosure is not limited thereto. For example, the method of sending the search interest point request to the search server may be implemented in a wired or wireless manner.
Step 302: the retrieval server returns the interest points corresponding to the retrieval interest points, the retrieval server judges whether the target retrieval words in the retrieval interest point request hit the target thematic map or not, and if the target thematic map is hit, the name of the target thematic map corresponding to the target retrieval words is returned.
In this implementation, the client sends a search interest point request to the search server in step 301, and receives the name of the target thematic map sent by the search server for the search interest point request.
Here, the retrieval data includes the interest point corresponding to the retrieval interest point request and the name of the target thematic map, and the retrieval data is sent to the client by the retrieval server in a wired or wireless manner.
Step 303: after receiving the retrieval data returned by the retrieval server, the client judges whether the retrieval data comprises the name of the target thematic map, and if the retrieval data comprises the name of the target thematic map, the client initiates target request information to a retrieval thematic map server (topic _ server).
Step 304: after receiving the target request information, the retrieval thematic map server topic-server generates a vector query request which corresponds to the user information in the target request information and aims at the user portrait vector, and sends the vector query request to a preset user portrait vector database; wherein the target request information includes a name of a target thematic map corresponding to the target scene.
Step 305: a predetermined user portrait vector database for returning user portrait vectors corresponding to the query vector request for retrieving the topic map server topic-server.
In this implementation, the user portrait vector is stored using a multidimensional matrix, which can reflect user preferences from multiple dimensions.
Step 306: after receiving the user portrait vectors, the retrieval thematic map server topic-server queries a preset interest point vector database by combining the names of the target thematic maps.
And inquiring a target interest point vector corresponding to the inquired user portrait vector and the name of the target thematic map from a preset interest point vector database.
Step 307: the point of interest vector database will return the target point of interest vector corresponding to the queried user portrait vector and the name of the target thematic map (hotel, cate, etc. scenes).
Step 308: and the retrieval thematic map server topic-server sends the target interest point vector to the client after receiving the target interest point vector corresponding to the inquired user portrait vector and the name of the target thematic map.
Step 309, the client displays the target interest point indicated by the target interest point vector and the interest point corresponding to the interest point retrieval request by using the target thematic map indicated by the name of the target thematic map.
As shown in fig. 4, the apparatus 400 for retrieving a point of interest of the present embodiment may include: a request generation module 401, a first query module 402, a second query module 403, and a vector sending module 404. Wherein, the request generating module 401 is configured to generate a vector query request for a user portrait vector corresponding to user information in target request information in response to receiving the target request information sent by the client; the target request information comprises a name of a target thematic map corresponding to a target scene; a first query module 402 configured to query a user portrait vector corresponding to the vector query request from a preset user portrait vector database; a second query module 403, configured to query a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map; a vector sending module 404 configured to send the target point of interest vector to the client to display the target point of interest indicated by the target point of interest vector with the target thematic map indicated by the name of the target thematic map on the screen of the client.
In the present embodiment, in the apparatus 400 for retrieving a point of interest: the specific processing and the technical effects thereof of the request generation module 401, the first query module 402, the second query module 403, and the vector sending module 404 can refer to the related descriptions of step 201 and step 204 in the corresponding embodiment of fig. 2, which are not described herein again. The first query module 402 and the second query module 403 may be the same module or two different modules.
In some optional implementations of this embodiment, the apparatus for retrieving a point of interest further includes: a data determination module (not shown in the figure) configured to determine interest point target data based on the obtained interest point basic data; wherein, the interest point basic data at least comprises: attribute tags of the interest points and user behavior data for the interest points; a vector generation module (not shown in the figure) configured to generate the following parameters of the user portrait set and the interest point target data according to the user portrait set matched with the user behavior data for the interest point within a preset time period: displaying the priority and the attribute labels, and generating the interest point vectors in a preset interest point vector database.
In some optional implementations of this embodiment, the point of interest basic data further includes: scene labels of interest points, and a data determination module, comprising: a data classification unit (not shown in the figure) configured to classify the interest point basic data according to the scene tags of the interest points, so as to obtain different types of interest point basic data; and a data obtaining unit (not shown in the figure) configured to set a display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data for the interest point, so as to obtain the interest point target data.
In some optional implementations of this embodiment, the user behavior data for the point of interest includes at least one of: click quantity; searching quantity; and retrieving the name of the object.
In some optional implementations of this embodiment, the data obtaining unit is further configured to: judging whether the name of the retrieval object hits a preset high-frequency word or not; when the name of the retrieval object hits a preset high-frequency word, setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the priority parameter corresponding to the click rate, the priority parameter corresponding to the retrieval rate and the priority parameter corresponding to the frequency of the high-frequency word, and obtaining the interest point target data.
The present disclosure also provides an electronic device and a readable storage medium according to an embodiment of the present disclosure.
As shown in fig. 5, a block diagram of an electronic device for a method of retrieving a point of interest according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for retrieving points of interest in the embodiments of the present disclosure (e.g., the request generation module 401, the first query module 402, the second query module 403, and the vector transmission module 404 shown in fig. 4). The processor 501 executes various functional applications of the server and data processing, i.e., implements the method for retrieving points of interest in the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 502.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of an electronic device for retrieving the point of interest, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 502 optionally includes memory located remotely from processor 501, which may be connected via a network to an electronic device for retrieving points of interest. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method for retrieving a point of interest may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic apparatus for the method of retrieving the point of interest, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS").
The method, the device, the equipment and the medium for retrieving the interest points of the embodiments of the present disclosure firstly respond to the received target request information sent by the client, and generate a vector query request for a user portrait vector corresponding to the user information in the target request information; the target request information comprises a name of a target thematic map corresponding to a target scene; then, inquiring a user portrait vector corresponding to the vector inquiry request from a preset user portrait vector database; then, inquiring a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map; and finally, sending the target interest point vector to the client so as to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map.
In the process, compared with the background technology that the number of the acquired interest points is small and the interest points are not related to the user portrait due to the processing of acquiring the interest points through the extensive retrieval or the processing of acquiring the interest points based on the thematic map, the user portrait vector corresponding to the vector query request is acquired from the user portrait vector database through the vector query request generated by the user information in the target thematic map by combining the extensive retrieval and the thematic map; and finally, displaying the target interest points indicated by the target interest point vectors by adopting the target thematic map indicated by the name of the target thematic map, so that the user does not need to perform additional operation while obtaining a large number of interest points, does not depend on publishing and has a short development period. Moreover, the user portrait is introduced, so that the thematic map becomes a personalized thematic map for the user, and the user experience is improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (12)
1. A method for retrieving points of interest, the method comprising:
in response to receiving target request information sent by a client, generating a vector query request aiming at a user portrait vector corresponding to user information in the target request information; wherein the target request information includes a name of a target thematic map corresponding to a target scene;
querying a user portrait vector corresponding to the vector query request from a preset user portrait vector database;
inquiring a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map;
and sending the target interest point vector to the client to display the target interest point indicated by the target interest point vector on a screen of the client by adopting the target thematic map indicated by the name of the target thematic map.
2. The method of claim 1, the point of interest vector database determined based on the steps of:
determining interest point target data based on the obtained interest point basic data; wherein the point of interest base data at least comprises: attribute tags of the interest points and user behavior data for the interest points;
according to the user image set matched with the user behavior data aiming at the interest point in the preset time period and the following parameters of the interest point target data: displaying the priority and the attribute labels, and generating the interest point vectors in a preset interest point vector database.
3. The method of claim 2, wherein the point of interest base data further comprises: scene labels of the points of interest;
the determining of the target data of the interest point based on the obtained basic data of the interest point comprises:
classifying the interest point basic data according to the scene labels of the interest points to obtain different types of interest point basic data;
and setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data aiming at the interest points to obtain the interest point target data.
4. The method of claim 2, wherein the user behavior data for a point of interest comprises at least one of:
click quantity; searching quantity; and retrieving the name of the object.
5. The method of claim 4, if the user behavior data for the point of interest comprises: setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data aiming at the interest points to obtain the interest point target data, wherein the display priority comprises the following steps:
judging whether the name of the retrieval object hits a preset high-frequency word or not;
when the name of the retrieval object hits the preset high-frequency word, setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the priority parameter corresponding to the click rate, the priority parameter corresponding to the retrieval amount and the priority parameter corresponding to the frequency of the high-frequency word, and obtaining the interest point target data.
6. An apparatus for retrieving a point of interest, the apparatus comprising:
a request generation module configured to generate a vector query request for a user portrait vector corresponding to user information in target request information in response to receiving the target request information sent by a client; wherein the target request information includes a name of a target thematic map corresponding to a target scene;
a first query module configured to query a user portrait vector corresponding to the vector query request from a preset user portrait vector database;
the second query module is configured to query a target interest point vector from a preset interest point vector database; the target interest point vector corresponds to the inquired user portrait vector and the name of the target thematic map; the interest points indicated by the interest point vectors in the interest point vector database are associated with the target thematic map indicated by the name of the target thematic map;
a vector sending module configured to send the target interest point vector to the client to display the target interest point indicated by the target interest point vector on a screen of the client by using the target thematic map indicated by the name of the target thematic map.
7. The apparatus of claim 6, the apparatus further comprising:
a data determination module configured to determine point of interest target data based on the obtained point of interest basic data; wherein the point of interest base data at least comprises: attribute tags of the interest points and user behavior data for the interest points;
a vector generation module configured to generate a vector according to the user image set matched with the user behavior data for the interest point within a preset time period and the following parameters of the interest point target data: displaying the priority and the attribute labels, and generating the interest point vectors in a preset interest point vector database.
8. The apparatus of claim 7, wherein the point of interest basis data further comprises: scene labels of the points of interest;
the data determination module comprises:
the data classification unit is configured to classify the interest point basic data according to the scene labels of the interest points to obtain different types of interest point basic data;
and the data obtaining unit is configured to set the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the user behavior data aiming at the interest points, so as to obtain the interest point target data.
9. The apparatus of claim 7, wherein the user behavior data for a point of interest comprises at least one of:
click quantity; searching quantity; and retrieving the name of the object.
10. The apparatus of claim 9, wherein the data derivation unit is further configured to:
judging whether the name of the retrieval object hits a preset high-frequency word or not;
when the name of the retrieval object hits the preset high-frequency word, setting the display priority of the interest point basic data of each category in the interest point basic data of different categories according to the priority parameter corresponding to the click rate, the priority parameter corresponding to the retrieval amount and the priority parameter corresponding to the frequency of the high-frequency word, and obtaining the interest point target data.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010967344.5A CN112100522B (en) | 2020-09-15 | 2020-09-15 | Method, device, equipment and medium for searching interest points |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010967344.5A CN112100522B (en) | 2020-09-15 | 2020-09-15 | Method, device, equipment and medium for searching interest points |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112100522A true CN112100522A (en) | 2020-12-18 |
| CN112100522B CN112100522B (en) | 2024-06-14 |
Family
ID=73759614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010967344.5A Active CN112100522B (en) | 2020-09-15 | 2020-09-15 | Method, device, equipment and medium for searching interest points |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112100522B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113190640A (en) * | 2021-05-20 | 2021-07-30 | 拉扎斯网络科技（上海）有限公司 | Method and device for processing point of interest data |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040059705A1 (en) * | 2002-09-25 | 2004-03-25 | Wittke Edward R. | System for timely delivery of personalized aggregations of, including currently-generated, knowledge |
| US8645366B1 (en) * | 2011-12-30 | 2014-02-04 | Google Inc. | Generating recommendations of points of interest |
| US20140040761A1 (en) * | 2012-08-03 | 2014-02-06 | Google Inc. | Providing an update associated with a user-created point of interest |
| US9194716B1 (en) * | 2010-06-18 | 2015-11-24 | Google Inc. | Point of interest category ranking |
| EP2990966A1 (en) * | 2014-09-01 | 2016-03-02 | Mapquest Inc. | Identifying points-of-interest using customized query prediction |
| EP3176736A1 (en) * | 2015-12-04 | 2017-06-07 | Nextop Italia SRL Semplificata | Electronic system and method for travel planning, based on object-oriented technology |
| US9720934B1 (en) * | 2014-03-13 | 2017-08-01 | A9.Com, Inc. | Object recognition of feature-sparse or texture-limited subject matter |
| CN108733747A (en) * | 2018-03-30 | 2018-11-02 | 斑马网络技术有限公司 | Map retrieval method based on cloud computing and its system |
| CN109299351A (en) * | 2018-09-21 | 2019-02-01 | 北京百度网讯科技有限公司 | Content recommendation method and device, electronic equipment and computer-readable medium |
| WO2019030027A1 (en) * | 2017-08-08 | 2019-02-14 | Here Global B.V. | Method, apparatus and computer program product for disambiguation of points-of-interest in a field of view |
| CN110347940A (en) * | 2019-07-17 | 2019-10-18 | 北京百度网讯科技有限公司 | Method and apparatus for optimizing point of interest label |
| CN110782284A (en) * | 2019-10-24 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Information pushing method and device and readable storage medium |
| CN110851726A (en) * | 2019-11-18 | 2020-02-28 | 百度在线网络技术（北京）有限公司 | Interest point selection method and device and electronic equipment |
| CN110929178A (en) * | 2018-09-20 | 2020-03-27 | 阿里巴巴集团控股有限公司 | Information recommendation method and device, terminal device and server |
| CN110941739A (en) * | 2018-09-22 | 2020-03-31 | 北京微播视界科技有限公司 | Media file recommendation method and device, media file server and storage medium |
| CN111310074A (en) * | 2020-02-13 | 2020-06-19 | 北京百度网讯科技有限公司 | Interest point label optimization method and device, electronic equipment and computer readable medium |
-
2020
- 2020-09-15 CN CN202010967344.5A patent/CN112100522B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040059705A1 (en) * | 2002-09-25 | 2004-03-25 | Wittke Edward R. | System for timely delivery of personalized aggregations of, including currently-generated, knowledge |
| US9194716B1 (en) * | 2010-06-18 | 2015-11-24 | Google Inc. | Point of interest category ranking |
| US8645366B1 (en) * | 2011-12-30 | 2014-02-04 | Google Inc. | Generating recommendations of points of interest |
| US20140040761A1 (en) * | 2012-08-03 | 2014-02-06 | Google Inc. | Providing an update associated with a user-created point of interest |
| US9720934B1 (en) * | 2014-03-13 | 2017-08-01 | A9.Com, Inc. | Object recognition of feature-sparse or texture-limited subject matter |
| EP2990966A1 (en) * | 2014-09-01 | 2016-03-02 | Mapquest Inc. | Identifying points-of-interest using customized query prediction |
| EP3176736A1 (en) * | 2015-12-04 | 2017-06-07 | Nextop Italia SRL Semplificata | Electronic system and method for travel planning, based on object-oriented technology |
| WO2019030027A1 (en) * | 2017-08-08 | 2019-02-14 | Here Global B.V. | Method, apparatus and computer program product for disambiguation of points-of-interest in a field of view |
| CN108733747A (en) * | 2018-03-30 | 2018-11-02 | 斑马网络技术有限公司 | Map retrieval method based on cloud computing and its system |
| CN110929178A (en) * | 2018-09-20 | 2020-03-27 | 阿里巴巴集团控股有限公司 | Information recommendation method and device, terminal device and server |
| CN109299351A (en) * | 2018-09-21 | 2019-02-01 | 北京百度网讯科技有限公司 | Content recommendation method and device, electronic equipment and computer-readable medium |
| CN110941739A (en) * | 2018-09-22 | 2020-03-31 | 北京微播视界科技有限公司 | Media file recommendation method and device, media file server and storage medium |
| CN110347940A (en) * | 2019-07-17 | 2019-10-18 | 北京百度网讯科技有限公司 | Method and apparatus for optimizing point of interest label |
| CN110782284A (en) * | 2019-10-24 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Information pushing method and device and readable storage medium |
| CN110851726A (en) * | 2019-11-18 | 2020-02-28 | 百度在线网络技术（北京）有限公司 | Interest point selection method and device and electronic equipment |
| CN111310074A (en) * | 2020-02-13 | 2020-06-19 | 北京百度网讯科技有限公司 | Interest point label optimization method and device, electronic equipment and computer readable medium |
Non-Patent Citations (1)
| Title |
|---|
| 杨晓蕾;李胜;何熊熊;刘桂云;: "基于张量分解的多维信息融合兴趣点推荐算法", 小型微型计算机系统, no. 05 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113190640A (en) * | 2021-05-20 | 2021-07-30 | 拉扎斯网络科技（上海）有限公司 | Method and device for processing point of interest data |
| CN113190640B (en) * | 2021-05-20 | 2023-02-07 | 拉扎斯网络科技（上海）有限公司 | Method and device for processing point of interest data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112100522B (en) | 2024-06-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11397772B2 (en) | Information search method, apparatus, and system | |
| US10885076B2 (en) | Computerized system and method for search query auto-completion | |
| CN110494852B (en) | Intelligent matching automatic completion system | |
| US7769771B2 (en) | Searching a document using relevance feedback | |
| CN106796608B (en) | Contextual search character string synonym automatically generates | |
| CN108701155A (en) | Expert's detection in social networks | |
| CN111949814A (en) | Searching method, searching device, electronic equipment and storage medium | |
| US9684717B2 (en) | Semantic search for business entities | |
| KR20170124581A (en) | Predicting User Needs for Specific Contexts | |
| CN112559901B (en) | Resource recommendation method and device, electronic equipment, storage medium and computer program product | |
| JP7206514B2 (en) | Method for sorting geolocation points, training method for sorting model, and corresponding device | |
| CN111666292A (en) | Similarity model establishing method and device for retrieving geographic positions | |
| JP2015197722A (en) | Document retrieval device, document retrieval method, program, and document retrieval system | |
| CN111460296B (en) | Method and apparatus for updating event sets | |
| CN111241225A (en) | Resident area change judgment method, resident area change judgment device, resident area change judgment equipment and storage medium | |
| US10643142B2 (en) | Search term prediction | |
| CN112000495B (en) | Method, electronic device and storage medium for point of interest information management | |
| CN112100522B (en) | Method, device, equipment and medium for searching interest points | |
| CN112328896A (en) | Method, apparatus, electronic device, and medium for outputting information | |
| WO2018201280A1 (en) | Method and apparatus for query auto-completion | |
| CN112541123A (en) | Map region recommendation method, device, equipment and storage medium | |
| CN113536120B (en) | Point-of-interest recall method and device based on user behavior | |
| CN113595770A (en) | Group click rate estimation method and device, electronic equipment and storage medium | |
| CN112269938A (en) | Information display method and device, electronic equipment and storage medium | |
| CN112860840A (en) | Search processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |