WO2020220778A1 - 推荐方法以及装置、设备和介质 - Google Patents

推荐方法以及装置、设备和介质 Download PDF

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Publication number
WO2020220778A1
WO2020220778A1 PCT/CN2020/073758 CN2020073758W WO2020220778A1 WO 2020220778 A1 WO2020220778 A1 WO 2020220778A1 CN 2020073758 W CN2020073758 W CN 2020073758W WO 2020220778 A1 WO2020220778 A1 WO 2020220778A1
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Prior art keywords
sub
requester
user
area
recommendation information
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English (en)
French (fr)
Inventor
林浩
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Beijing ByteDance Network Technology Co Ltd
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Beijing ByteDance Network Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • the embodiments of the present disclosure relate to information processing technology, for example, to a recommendation method, device, device, and medium.
  • the embodiments of the present disclosure provide a recommendation method, device, device, and medium, which simplify the operation of user recommendation and improve the security of user recommendation.
  • an embodiment of the present disclosure provides a recommendation method, which includes:
  • the priority of the preset classification and the weight of the sub-areas in the area under the preset classification determine the user recommendation information of the sub-areas that meets the preset recommendation conditions to obtain user recommendation information corresponding to the requester .
  • a recommendation device which includes:
  • a sub-area determining module configured to determine the sub-area of the requester in the area under the preset classification according to the current location of the requester in the recommendation request;
  • the recommendation information determining module is configured to determine the user recommendation information of the sub-areas that meets the preset recommendation conditions according to the priority of the preset classification and the weight of the sub-areas in the area under the preset classification, so as to obtain the User recommendation information corresponding to the requester.
  • embodiments of the present disclosure also provide a device, which includes:
  • At least one processor At least one processor
  • Memory set to store at least one program
  • the at least one processor When the at least one program is executed by the at least one processor, the at least one processor implements the recommended method as described in any embodiment of the present disclosure.
  • an embodiment of the present disclosure provides a readable medium on which a computer program is stored, and when the program is executed by a processor, the recommendation method as described in any embodiment of the present disclosure is implemented.
  • FIG. 1A shows a flowchart of a recommendation method provided by an embodiment of the present disclosure
  • FIG. 1B shows a schematic diagram of the principle of a recommendation process provided by an embodiment of the present disclosure
  • FIG. 1C shows a schematic diagram of the principle of area division under preset hierarchies in the recommendation method provided by an embodiment of the present disclosure
  • FIG. 2 shows a schematic diagram of the corresponding area of the requesting party under a preset level in the recommendation method provided by an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of another recommendation method provided by an embodiment of the present disclosure
  • FIG. 4 shows a schematic diagram of an interface for a requester to display recommendation information according to an embodiment of the present disclosure
  • FIG. 5 shows a schematic structural diagram of a recommendation device provided by an embodiment of the present disclosure
  • Fig. 6 shows a schematic structural diagram of a device provided by an embodiment of the present disclosure.
  • FIG. 1A shows a flowchart of a recommendation method provided by an embodiment of the present disclosure.
  • the embodiment of the present disclosure may be applicable to a situation in which multiple users request corresponding recommendation information face to face.
  • a recommendation method provided in this embodiment can be executed by the recommendation apparatus provided in the embodiment of the present disclosure, which can be implemented by software and/or hardware, and is integrated in the equipment that executes the method.
  • the device for executing this method may be a server with data processing function.
  • the recommendation method provided in the embodiment of the present disclosure may include the following steps S110 and S110.
  • step S110 according to the current location of the requester in the recommendation request, determine the sub-areas in the corresponding area of the requester under the preset classification.
  • the recommendation method in this embodiment is mainly aimed at multiple users who perform corresponding operations in an application and request other users face-to-face for specific information of the corresponding type in the application.
  • face-to-face refers to participating in this information
  • the recommended multi-users are currently in the same location area set in advance, and the multi-users may be users who have not yet added friends to each other in the application.
  • the requestor in this embodiment refers to any one of the multi-party users who request recommendation information face-to-face, and the recommendation request refers to the requesting party when there is a face-to-face information recommendation requirement, by executing the corresponding The instruction generated by the trigger operation to instruct to obtain the corresponding recommendation information.
  • the trigger operation performed by the requester can be the requester clicking the virtual button in the application that represents the information push function, or it can be an instruction selected by the requester
  • the specific type of operation of the information to be pushed such as when the requester applies for adding a friend by clicking the face-to-face friend follow function, the trigger operation indicates to obtain the friend's account information, etc.
  • different trigger operations can indicate that the requester is currently waiting in the recommendation request Different types of recommended information; for example, if the requester currently needs to request the account information of other users to apply for adding friends, as shown in Figure 1B, the requester will click on the preset face-to-face friend following function in the application.
  • the corresponding virtual button generates a recommendation request for instructing the requesting party to apply for adding a friend and need to obtain friend account information.
  • the current location of the requester will be carried in the recommendation request, and the current location can be subsequently used to determine the corresponding location area.
  • the current location in this embodiment is the latitude and longitude location where the requesting party is currently located, so the corresponding area is the latitude and longitude area.
  • the location regions corresponding to each precision are divided globally according to different latitude and longitude precisions. In this case, different precisions correspond to different preset classifications. The preset classifications indicate the precision of the divided regions.
  • Corresponding sub-areas are preset in the corresponding area under each preset classification.
  • This embodiment does not limit the number of sub-areas in the corresponding area under each preset classification; for example, the geohash principle is adopted in this embodiment
  • the geohash principle is to understand the earth as a two-dimensional plane, and according to different latitude and longitude division precision, the plane’s latitude and longitude is recursively decomposed into multiple sub-blocks of corresponding size.
  • the horizontal line with arrows in 1C represents the longitude coordinates
  • the vertical line with arrows in Figure 1C represents the latitude coordinates
  • the other horizontal and vertical lines respectively represent the division lines of the area, so as to obtain the corresponding sub-blocks under different preset levels.
  • Different latitude and longitude division precisions correspond to different location areas.
  • the location area after division may be the sub-areas in this embodiment, and multiple sub-areas constitute the corresponding area under the preset level, that is, different preset levels.
  • the requester when multiple users request the recommendation information of the corresponding user face-to-face, they will first execute the corresponding trigger operation in the corresponding application, and generate the corresponding recommendation request according to the trigger operation.
  • the requester mainly For the recommendation information corresponding to face-to-face users, the generated recommendation request will carry the current location of the requester so that the current location of the requester can be reported to the corresponding server; as shown in Figure 1B, the requester is generating the corresponding When a recommendation request is made, the recommendation request is sent to the corresponding server.
  • the server obtains the current location of the requester by parsing the recommendation request of the requester, and at the same time determines the composition of the requester under the preset classification based on the current location of the requester For each sub-areas of the corresponding area, the corresponding user recommendation information is obtained by searching for the user in the sub-areas in the corresponding area under the preset classification.
  • the preset The corresponding area under the classification may include multiple divided sub-blocks, thereby improving the accuracy of the recommended information. Therefore, according to the current location of the requester in the recommendation request, determining the sub-areas of the requester in the corresponding area under the preset classification may include: Determine the sub-areas and associated sub-areas of the current location under each preset level as the sub-areas in the corresponding area of the requester under the preset level.
  • the server receives the recommendation request sent by the requester, and then determines the corresponding area under the preset classification According to the current location of the requester carried in the recommendation request, the corresponding sub-block in each sub-block divided under each preset level can be determined by the requester, that is, the current location of the requester.
  • the sub-block of is used as the sub-area in this embodiment, and the corresponding associated sub-area is determined according to the sub-area where it is located.
  • the sub-areas and the associated sub-areas in this embodiment together form the area nine square grid corresponding to the requester, so that the corresponding area of the requester under the preset classification is the sub-region of the requester.
  • the area and the associated sub-areas are composed of a nine-square grid, and the user recommendation information corresponding to the trigger operation of the requesting party is subsequently determined in the nine-square grid.
  • step S120 the user recommendation information of the sub-areas that meets the preset recommendation conditions is determined according to the preset hierarchical priority and the weight of the sub-areas in the area, and the user recommendation information corresponding to the requesting party is obtained.
  • the corresponding area under each preset level includes multiple sub-areas
  • the search order of the sub-areas in the area At this time, the priority of the preset classification is used to indicate the search order of each preset classification, and the weight of the sub-area in the area is used to indicate the search of the sub-areas in the corresponding area under each preset classification order.
  • the recommendation information is the user recommendation information corresponding to the requester in this embodiment; the preset recommendation condition in this embodiment can refer to the number of users found or the number of recommended information found; Set the classification and the search order of the sub-areas in the corresponding area under each preset
  • the preset recommendation condition is not limited, and it may be any condition that meets the recommendation requirement of the requesting party. Therefore, this embodiment can first determine the strength of the relevance between the user recommendation information and the requester according to the priority of the preset classification and the weight of the sub-areas in the area, so as to find the strong relevance to the requester. Users can obtain user recommendation information with high accuracy and strong relevance to improve the accuracy of recommendation.
  • the user recommendation information may include at least one of the following information: a user list and a user recommended content list.
  • the user list refers to a list composed of user account information for the requesting party to add friends
  • the user recommended content list refers to a list containing various works published by the recommended user in the application.
  • the user recommendation information including at least one of the user list and the user recommended content list can be obtained according to the preset hierarchical priority and the weight of the sub-areas in the area.
  • this embodiment can obtain when a user located in the same area as the requesting party is determined according to the priority of the preset classification and the weight of the sub-areas in the area
  • the friend account link of the user, the friend account link is used as the user recommendation information in this embodiment, and recommended to the requesting party, so that the requesting party can directly add the user's friends according to the friend account link; if the requesting party triggers the operation instruction to obtain
  • this embodiment can use a link of a recent work uploaded by a user in the same area as the requesting party as the corresponding user recommendation information, and recommend it to the requesting party, so that the requesting party can directly view the corresponding work through the work link.
  • the work can include various information such as videos and text uploaded by users.
  • the technical solution provided by the embodiments of the present disclosure directly sends user recommendation information to the requesting party based on the current location of the requesting party, so that the requesting party does not need to know the user's account in advance, nor does it require the requesting party to search based on the user's account, which simplifies
  • the operation recommended by the user improves the flexibility and convenience of the recommendation to the requester; in one embodiment, through different preset levels, the accuracy of the region division method corresponding to the requester’s location is adjusted, and according to the preset level
  • the priority and the weight of the sub-regions in the area determine the strength of the relevance between the user recommendation information and the requesting party, thereby obtaining user recommendation information with high accuracy and strong relevance, and improving the accuracy of the recommendation;
  • the user recommendation information corresponding to the requester is the recommendation information of the user in the sub-area of the corresponding area under the preset classification. If the user is located in the sub-area of the corresponding area under the preset classification, the recommendation can be realized without obtaining user
  • Fig. 3 shows a flowchart of another recommendation method provided by an embodiment of the present disclosure. This embodiment gives a detailed introduction to the priority of the preset classification and the weight of the sub-areas in the area.
  • the method in this embodiment may include the following steps S310, S320, and S330.
  • step S310 the priority of each preset classification and the weight of the sub-region in the region are set.
  • the search order of the preset levels is determined by setting the corresponding priority for each preset level, and the corresponding weights are set for each sub-area in the area under different preset levels to determine each preset level
  • the search order of the sub-areas in the corresponding area is to determine the strength of the relevance between the subsequent user recommendation information and the requester, and then the user corresponding to the requester is determined according to the priority of the preset classification and the weight of the sub-areas in the area Recommended information.
  • the priority of the preset classification is generally set according to the level of division accuracy.
  • the sub-area of the current location When setting the weight of the sub-areas in the area, the sub-area of the current location is generally set The weight setting is the highest, and the weight of the associated subregion is set according to the degree of association with the subregion where it is located.
  • the specific setting method of the weight of the subregion is not limited.
  • the location area classification for the global division in this embodiment includes two preset classifications, geohash1 and geohash2; among them, the division accuracy of latitude and longitude of geohash1 is 40*20, and the division accuracy of latitude and longitude of geohash2 is 5*5.
  • the priority of geohash2 is higher than geohash1.
  • the requester's current location can be used to determine the requester's sub-area in the corresponding area under geohash1 and geohash2, which includes the request
  • the sub-region and the associated sub-region of the current location of the square are the region composed of the sub-region and the associated sub-region respectively.
  • the weight of the sub-region is higher than the weight of the associated sub-region.
  • the preset recommendation condition is the set number of friend recommendations
  • the current location in the area corresponding to geohash2 Find the corresponding user in the sub-area, and obtain the friend account link of the user in the sub-area.
  • step by step judgments are made until the set number of friend recommendations is reached; then the friend account link added by the requester in this application is obtained as the user recommendation information corresponding to the requester.
  • step S320 according to the current location of the requester in the recommendation request, determine the sub-areas in the corresponding area of the requester under the preset classification.
  • step S330 the user recommendation information of the sub-areas that meets the preset recommendation conditions is determined according to the preset hierarchical priority and the weight of the sub-areas in the area, and the user recommendation information corresponding to the requesting party is obtained.
  • the technical solution provided by the embodiments of the present disclosure first determines the sub-region and the associated sub-region where the requestor’s current location is located under a preset classification, and determines that it conforms to the preset based on the priority of the preset classification and the weight of the sub-region in the region
  • the user recommendation information in the sub-area of the recommendation conditions does not require the requester to know the user's account in advance, nor does it require the requester to search based on the user's account, which simplifies the user recommendation operation and improves the flexibility and convenience of recommendation to the requester
  • through different preset levels adjust the accuracy of the area division method corresponding to the requester, and determine the relationship between the user recommendation information and the requester based on the preset level and the attributes of the area Therefore, the user recommendation information with high accuracy and strong relevance is obtained, and the accuracy of the recommendation is improved; in one embodiment, the user recommendation information corresponding to the requesting party is the recommendation information of users in the corresponding area under the preset classification.
  • the recommendation can be realized without obtaining the user's specific location information, so as to realize the protection of the user's privacy information, thereby improving the security of the user recommendation.
  • the user recommended information is the user recommended content list, it is possible to realize the information recommendation of the recommended user to the requester without the requesting party paying attention to the recommending user in advance, and there is no need to apply to the recommending user to add friends in advance, which improves the flexibility of recommendation.
  • this embodiment may be A corresponding sub-region list is set for each sub-region in the corresponding region under each preset level, and the sub-region list stores the user recommendation information under each information type corresponding to the user currently located in the sub-region, in order to obtain accurate
  • a timeout mechanism is set for each sub-area list.
  • the above-mentioned recommendation method may also include: within the preset update time period, if the user recommendation information in the sub-area is re-acquired, it will be re-acquired according to Update the user recommendation information of the corresponding sub-area; if the user recommendation information of the sub-area is not re-acquired, clear the user recommendation information of the corresponding sub-area.
  • the sub-areas list of the sub-areas in the corresponding area under each preset level corresponds to a preset update duration.
  • This embodiment monitors each sub-area list in real time. If it is within the preset update duration, A certain sub-area list re-acquires the user recommendation information of the sub-area, that is, if a user rejoins the sub-area and participates in the information recommendation of the requester this time, the re-acquired user recommendation information is stored in the corresponding sub-area In the list of sub-regions, the user recommendation information of the corresponding sub-region is updated according to the re-acquired user recommendation information; if a certain sub-region has not re-acquired the user recommendation information of the sub-region within the preset update time period, it means that the sub-region The area currently does not participate in the information recommendation of the requesting party.
  • the user recommendation information in this sub-area may be the information reported during the previous recommendation.
  • the sub-area corresponding to this sub-area needs to be improved.
  • the user recommendation information currently stored in the area list is deleted, that is, the user recommendation information of the corresponding sub-area is cleared, so that the corresponding user recommendation information can be accurately obtained according to the attribute of the corresponding area under the preset classification by the requester.
  • the user recommendation information with the earliest storage time can be deleted, and new user recommendation information can be added; at the same time, when the user exits On the page corresponding to the "recommendation request" function, the user recommendation information stored in the subarea list corresponding to the subarea of the user's current location can be deleted to ensure the accuracy when another requester requests the corresponding user recommendation information. .
  • the above recommendation method may also include: if the user recommendation information corresponding to the requester is not empty, then the user recommendation information is sent to the requester; if the requester corresponds to If the user recommendation information is empty, a relocation request is sent to the requester so that the requester can obtain the current location again and report it.
  • the user recommendation information corresponding to the requester when the user recommendation information corresponding to the requester is obtained according to the current location of the requester in the recommendation request, it is first judged whether the user recommendation information is empty to determine whether the current location of the requester is accurately located; If the user recommendation information corresponding to the party is not empty, the user recommendation information is directly sent to the requesting party, and the corresponding operation is performed. For example, when the found friend account link is sent to the requesting party, the requesting party directly applies according to the friend account link Add friends; as shown in Figure 4, the user recommendation information displayed by the requesting party can include the avatar, nickname, and personal homepage entry of the friend to be added. At the same time, there is a "follow" button indicating the link of the friend's account.
  • the followed user page will also pop up a pop-up window to show the requester's attention.
  • the followed user can also click the "Follow” button in the pop-up window. You can also close the pop-up window directly.
  • the user recommendation information corresponding to the requester is empty, it means that the current location of the requester does not exist in the sub-areas of the corresponding area under the preset classification.
  • the server directly generates a re The positioning request is sent to the requesting party, instructing the requesting party to re-acquire the current location, and report it to the server, and the server will re-determine the user recommendation information corresponding to the requesting party according to the above-mentioned steps according to the re-reported current location.
  • the current location of the same user at different times may be stored in the sub-region lists corresponding to different sub-regions during relocation, resulting in duplicate locations of the same user and inaccurate user recommendation information. Therefore, when there is a need for relocation, you need to clean up the list of sub-areas that the user does not actually exist.
  • you need to clean up the list of sub-areas that the user does not actually exist.
  • there is only one user’s user recommendation information in the sub-areas list and no new user is added in a short period of time, delete it directly User recommendation information in this word area list.
  • FIG. 5 shows a schematic structural diagram of a recommendation device provided by an embodiment of the present disclosure.
  • the embodiment of the present disclosure may be applicable to a situation where multiple users request corresponding recommendation information face to face.
  • the device may be implemented by software and/or hardware. And integrated in the equipment that executes the method.
  • the recommendation device in the embodiment of the present disclosure may include: a sub-region determination module 510 and a recommendation information determination module 520.
  • the sub-area determining module 510 is configured to determine the sub-area in the corresponding area of the requester under the preset classification according to the current location of the requester in the recommendation request.
  • the recommendation information determining module 520 is configured to determine the user recommendation information of the sub-areas that meets the preset recommendation conditions according to the priority of the preset classification and the weight of the sub-areas in the area, and obtain the user recommendation information corresponding to the requesting party.
  • the technical solution provided by the embodiments of the present disclosure directly sends user recommendation information to the requesting party based on the current location of the requesting party, so that the requesting party does not need to know the user's account in advance, nor does it require the requesting party to search based on the user's account, which simplifies
  • the operation recommended by the user improves the flexibility and convenience of the recommendation to the requester; in one embodiment, through different preset levels, the accuracy of the region division method corresponding to the requester’s location is adjusted, and according to the preset level
  • the priority and the weight of the sub-regions in the area determine the strength of the relevance between the user recommendation information and the requesting party, thereby obtaining user recommendation information with high accuracy and strong relevance, and improving the accuracy of the recommendation;
  • the user recommendation information corresponding to the requester is the recommendation information of the user in the sub-area of the corresponding area under the preset classification. If the user is located in the sub-area of the corresponding area under the preset classification, the recommendation can be realized without obtaining user
  • the aforementioned sub-region determination module 510 may be configured as:
  • the aforementioned recommendation device may further include:
  • the attribute setting module is set to set the priority of each preset classification and the weight of the sub-region in the region.
  • the aforementioned recommendation device may further include:
  • the update module is set to update the user recommendation information of the corresponding sub-area according to the re-acquired user recommendation information if the user recommendation information of the sub-area is re-acquired within the preset update time; if the user recommendation information of the sub-area is not re-acquired Information, the user recommendation information of the corresponding sub-area is cleared.
  • the aforementioned recommendation device may further include:
  • the recommendation module is set to send the user recommendation information to the requester if the user recommendation information corresponding to the requester is not empty;
  • the relocation module is configured to send a relocation request to the requester if the user recommendation information corresponding to the requester is empty, so that the requester can obtain the current location again and report it.
  • the user recommendation information may include at least one of the following information: a user list and a user recommended content list.
  • the current position may be a latitude and longitude position
  • the area may be a latitude and longitude area
  • the recommendation device provided by the embodiment of the present disclosure belongs to the same inventive concept as the recommendation method provided by the foregoing embodiment.
  • the embodiment of the present disclosure has the same characteristics as the foregoing embodiment. The same function.
  • FIG. 6 shows a schematic structural diagram of a device 600 suitable for implementing embodiments of the present disclosure.
  • the devices in the embodiments of the present disclosure may include, but are not limited to, mobile phones, notebook computers, digital broadcast receivers, personal digital assistants (Personal Digital Assistant, PDA), tablet computers (Portable Android Device, PAD), portable multimedia players ( Mobile terminals such as Portable Media Player (PMP), in-vehicle terminals (for example, in-vehicle navigation terminals), and fixed terminals such as digital (Television, TV), desktop computers, etc.
  • PDA Personal Digital Assistant
  • PAD Portable Android Device
  • PMP Portable Media Player
  • in-vehicle terminals for example, in-vehicle navigation terminals
  • fixed terminals such as digital (Television, TV), desktop computers, etc.
  • the device shown in FIG. 6 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.
  • the device 600 may include a processing device (such as a central processing unit, a graphics processor, etc.) 601, which may be loaded from a storage device 608 according to a program stored in a read only memory (Read Only Memory, ROM) 602 Various appropriate actions and processing are executed by programs in Random Access Memory (RAM) 603.
  • the RAM 603 also stores various programs and data required for the operation of the device 600.
  • the processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (Input/Output, I/O) interface 605 is also connected to the bus 604.
  • the following devices can be connected to the I/O interface 605: including input devices 606 such as touch screens, touch pads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; including, for example, liquid crystal displays (LCD) Output devices 607 such as speakers, vibrators, etc.; storage devices 608 such as magnetic tapes, hard disks, etc.; and communication devices 609.
  • the communication device 609 may allow the device 600 to perform wireless or wired communication with other devices to exchange data.
  • FIG. 6 shows a device 600 with various devices, it should be understood that it is not required to implement or have all the devices shown. It may alternatively be implemented or provided with more or fewer devices.
  • an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code configured to execute the method shown in the flowchart.
  • the computer program may be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602.
  • the processing device 601 the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.
  • the aforementioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two.
  • the computer-readable storage medium may be, for example, but not limited to, an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above.
  • Computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable Read Only Memory (Erasable Programmable Read Only Memory, EPROM or flash memory), optical fiber, portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical storage device, magnetic storage device, or any of the above suitable The combination.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein.
  • This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
  • the computer-readable signal medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
  • the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wire, optical cable, radio frequency (RF), etc., or any suitable combination of the above.
  • the above-mentioned computer-readable medium may be included in the above-mentioned device; or it may exist alone without being assembled into the device.
  • the above-mentioned computer-readable medium carries one or more programs.
  • the device When the above-mentioned one or more programs are executed by the device, the device: According to the current location of the requester in the recommendation request, determine the corresponding area of the requester under the preset level According to the preset hierarchical priority and the weight of the sub-region in the region, determine the user recommendation information of the sub-region that meets the preset recommendation conditions, and obtain the user recommendation information corresponding to the requester.
  • the computer program code used to perform the operations of the present disclosure may be written in one or more programming languages or a combination thereof.
  • the above-mentioned programming languages include object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional Procedural programming language-such as "C" language or similar programming language.
  • the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network-including Local Area Network (LAN) or Wide Area Network (WAN)-or it can be connected to an external computer (for example, use an Internet service provider to connect via the Internet).
  • LAN Local Area Network
  • WAN Wide Area Network
  • each block in the flowchart or block diagram can represent a module, program segment, or part of code, and the module, program segment, or part of code contains one or more for realizing the specified logical function Executable instructions.
  • the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two blocks shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or operations Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the units involved in the embodiments described in the present disclosure may be implemented in a software manner, or may be implemented in a hardware manner. Among them, the name of the unit does not constitute a limitation on the unit itself under certain circumstances.

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Abstract

一种推荐方法、装置、设备和介质。其中，该推荐方法包括：根据推荐请求中请求方的当前位置，确定请求方在预设分级下区域中的子区域(S110)；根据预设分级的优先级和预设分级下区域中的子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，以得到与请求方对应的用户推荐信息(S120)。

Description

推荐方法以及装置、设备和介质
本申请要求在2019年04月29日提交中国专利局、申请号为201910354646.2的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本公开实施例涉及信息处理技术，例如涉及一种推荐方法、装置、设备和介质。
背景技术
随着互联网技术的快速发展，对用户账号身份具有关联性的直播、短视频或聊天工具等应用程序也广泛地应用到人们的日常生活中；此时针对同一类应用程序，不同用户间在分享对应信息时，要求用户必须互相关注，否则无法实现信息分享。
相关技术在信息分享前，通常先确定待关注用户的账号，根据该账号在应用程序中搜索出对应的用户关注，进而向关注的用户分享对应的推荐信息，这种方式，要求用户预先知道待关注用户的账号，限制较多，操作繁琐。
发明内容
有鉴于此，本公开实施例提供了一种推荐方法、装置、设备和介质，简化了用户推荐的操作，并且提高用户推荐的安全性。
第一方面，本公开实施例提供了一种推荐方法，该方法包括：
根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域；
根据所述预设分级的优先级和所述预设分级下区域中的子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，以得到与所述请求方对应的用户推荐信息。
第二方面，本公开实施例提供了一种推荐装置，该装置包括：
子区域确定模块，设置为根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域；
推荐信息确定模块，设置为根据所述预设分级的优先级和所述预设分级下 区域中的子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，以得到与所述请求方对应的用户推荐信息。
第三方面，本公开实施例还提供了一种设备，该设备包括：
至少一个处理器；
存储器，设置为存储至少一个程序；
当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如本公开任意实施例中所述的推荐方法。
第四方面，本公开实施例提供了一种可读介质，其上存储有计算机程序，该程序被处理器执行时实现如本公开任意实施例中所述的推荐方法。
附图概述
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图做一简单地介绍，显而易见地，下面描述中的附图是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1A示出了本公开实施例提供的一种推荐方法的流程图；
图1B示出了本公开实施例提供的推荐过程的原理示意图；
图1C示出了本公开实施例提供的推荐方法中预设分级下区域划分的原理示意图；
图2示出了本公开实施例提供的推荐方法中请求方在预设分级下的对应区域的示意图；
图3示出了本公开实施例提供的另一种推荐方法的流程图；
图4示出了本公开实施例提供的请求方显示推荐信息的界面示意图；
图5示出了本公开实施例提供的一种推荐装置的结构示意图；
图6示出了本公开实施例提供的一种设备的结构示意图。
具体实施方式
以下将参照本公开实施例中的附图，通过实施方式清楚、完整地描述本公开的技术方案，显然，所描述的实施例是本公开一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
图1A示出了本公开实施例提供的一种推荐方法的流程图，本公开实施例可适用于多方用户面对面请求对应的推荐信息的情况中。本实施例提供的一种推荐方法可以由本公开实施例提供的推荐装置来执行，该装置可以通过软件和/或硬件的方式来实现，并集成在执行本方法的设备中，在本实施例中执行本方法的设备可以是具备数据处理功能的服务器。
在一实施例中，如图1A所示，本公开实施例中提供的推荐方法可以包括如下步骤S110和步骤S110。
在步骤S110中，根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下对应区域中的子区域。
其中，本实施例中的推荐方法主要针对多方用户通过在某应用程序中执行对应的操作，而面对面请求其他用户在该应用程序中相应类型下的特定信息，此时面对面是指参与本次信息推荐的多方用户当前处于预先设定的同一位置区域内，同时该多方用户可以是在应用程序中彼此当前还未添加好友的用户。在一实施例中，本实施例中的请求方是指面对面请求推荐信息的多方用户中的任意一方，推荐请求是指请求方在存在面对面的信息推荐需求时，通过在应用程序中执行相应的触发操作而生成的用于指示获取对应的推荐信息的指令，此时请求方所执行的触发操作可以是请求方点击应用程序中的表示信息推送功能的虚拟按钮，也可以是请求方选择的指示待推送信息的具体类型的操作，如请求方通过点击面对面好友关注功能来申请添加好友时，该触发操作指示获取好友的账号信息等，在此不同的触发操作可以表示推荐请求中请求方当前待推荐的不同信息类型；示例性的，若请求方当前需要请求其他用户的账号信息，来申请添加好友时，如图1B所示，请求方会在应用程序中点击预先设置的面对面好友关注功能所对应的虚拟按钮，从而生成用于指示请求方申请添加好友而需要获取好友账号信息的推荐请求。
此外，由于本实施例中请求的推荐信息主要针对与请求方处于同一位置区域内用户的信息，因此推荐请求中会携带请求方所处的当前位置，后续可以根据该当前位置判断对应位置区域的用户。此时，本实施例中的当前位置为请求方当前所在的经纬度位置，因此对应的区域则为经纬度区域。同时，本实施例中会按照不同经纬度精度在全球划分各精度下对应的位置区域，此时不同精度对应不同的预设分级，该预设分级表示划分的区域精度大小，同时，本实施例中每一预设分级下对应的区域中预先设定有对应的子区域，本实施例对于每一 预设分级下对应区域中子区域的数量不作限定；示例性的，本实施例中采用geohash原理来对全球位置进行划分，如图1C所示，geohash原理是将地球理解为一个二维平面，并按照不同的经纬度划分精度，将平面的经纬度递归分解成多个对应大小的子块，其中图1C中的带箭头的横线表示经度坐标，图1C中的带箭头的纵线表示纬度坐标，其它横纵线分别表示区域的划分线，从而得到不同预设分级下对应的各个子块，此时不同的经纬度划分精度对应划分不同的位置区域，此时划分后的位置区域可以是本实施例中的子区域，多个子区域构成该预设分级下对应的区域，也就是不同的预设分级下存在对应的子块划分结果；从而将全球的经纬度位置按照不同的精度进行划分，得到经纬度在不同预设分级下与区域中子区域的位置映射关系。
在一实施例中，多方用户在面对面请求对应用户的推荐信息时，首先会在相应的应用程序中执行相应的触发操作，根据该触发操作生成对应的推荐请求，由于本实施例中请求方主要针对面对面用户对应的推荐信息，因此所生成的推荐请求中会携带请求方所处的当前位置，以便向对应的服务端上报请求方的当前位置；如图1B所示，请求方在生成对应的推荐请求时，会将该推荐请求发送给对应的服务端，服务端通过解析请求方的推荐请求，得到该请求方的当前位置，同时根据请求方的当前位置确定请求方在预设分级下构成对应区域的各个子区域，后续通过在预设分级下对应区域中的子区域内查找所在用户，得到对应的用户推荐信息。
示例性的，由于本实施例中每一预设分级下对应的区域由预先根据不同的经纬度划分精度，将全球的经纬度划分为相应大小的子块所对应的多个子区域构成，此时预设分级下对应的区域可以包括多个划分子块，从而提高推荐信息的准确性，因此根据推荐请求中请求方的当前位置，确定请求方在预设分级下对应区域中的子区域，可以包括：确定每一预设分级下当前位置的所在子区域和关联子区域，作为请求方在预设分级下对应区域中的子区域。
在一实施例中，由于本实施例中的子区域是指预设分级下对应划分的各个子块，此时服务端在接收到请求方发送的推荐请求，进而确定预设分级下对应区域中的子区域时，可以根据该推荐请求中携带的请求方的当前位置，确定请求方在每一预设分级下划分的各个子块中对应所处的子块，也就是请求方的当前位置所在的子块，作为本实施例中的所在子区域，同时根据该所在子区域确定对应的关联子区域，该关联子区域可以是指以所在子区域为中心区域且与该 所在子区域相连或相接的周围子块，如图2所示，本实施例中的所在子区域和关联子区域共同组成请求方对应的区域九宫格，使得请求方在预设分级下对应的区域为请求方的所在子区域和关联子区域组成的九宫格，后续在该九宫格中确定与请求方的触发操作对应的用户推荐信息。
在步骤S120中，根据预设分级的优先级和区域中子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，得到请求方对应的用户推荐信息。
其中，由于每一预设分级下对应区域中包括多个子区域，因此在通过查找各个子区域中存在的用户确定对应的用户推荐信息时，首先需要确定预设分级以及每一预设分级下对应区域中子区域的查找顺序，此时预设分级的优先级用于指示各个预设分级的查找顺序，区域中子区域的权重用于指示在每一预设分级下对应区域中子区域的查找顺序。
在一实施例中，在确定请求方在预设分级下对应区域中的子区域后，首先根据预设分级的优先级，确定预设分级的查找顺序，按照优先级的高低对预设分级进行排序，进而在每一预设分级下对应区域的子区域中，按照该预设分级下对应区域中子区域的权重，确定每一预设分级下对应区域中子区域的查找顺序，按照权重的高低对区域中的子区域进行排序，进而依次查找每一预设分级下对应区域中子区域中存在的用户，得到该用户对应的推送信息，进而筛选出符合预设推荐条件的子区域的用户推荐信息，作为本实施例中请求方对应的用户推荐信息；其中，本实施例中该预设推荐条件可以是指查找到的用户数量，也可以是指查找到的推荐信息数量；在根据预设分级和每一预设分级下对应的区域中子区域的查找顺序依次查找各个子区域中存在的用户时，如果当前查找到的子区域中用户数量达到设定的数量，或者查找到的子区域的用户推荐信息达到设定的信息数量，则不再继续对未查找的子区域进行查找，直接将当前查找到的用户推荐信息作为请求方对应的用户推荐信息。本实施例中对预设推荐条件不作限定，可以是任一种满足请求方推荐要求的条件。因此，本实施例可以首先根据预设分级的优先级和区域中子区域的权重确定用户推荐信息与请求方之间关联性的强弱，从而查找到与请求方之间的关联性较强的用户，进而得到高精度下关联性强的用户推荐信息，提高推荐的准确性。
此外，用户推荐信息至少可以包括如下信息之一：用户清单和用户推荐内容清单。在一实施例中，用户清单是指由用户账号信息组成，用于请求方添加好友的清单，用户推荐内容清单是指包含推荐用户在应用程序中发布的各类作 品的清单。本实施例中推荐请求中没有携带待推荐的信息类型时，可以根据预设分级的优先级和区域中子区域的权重，得到至少包括用户清单和用户推荐内容清单之一的用户推荐信息。
示例性的，若请求方的触发操作指示申请添加好友时，本实施例在根据预设分级的优先级和区域中子区域的权重，确定出与请求方位于同一区域内的用户时，可以获取该用户的好友账号链接，将该好友账号链接作为本实施例中的用户推荐信息，推荐给请求方，以便请求方直接根据该好友账号链接添加该用户的好友；若请求方的触发操作指示获取对应的上传作品时，本实施例可以将与请求方位于同一区域内的用户近期上传的作品链接作为对应的用户推荐信息，推荐给请求方，以便请求方直接通过该作品链接查看相应的作品，此时作品可以包括用户上传的视频、文字等各类信息。
本公开实施例提供的技术方案，直接根据请求方所在的当前位置向请求方发送用户推荐信息，从而无需请求方预先知道用户的账号，也无需请求方根据用户的账号进行查找的操作，简化了用户推荐的操作，提高了向请求方推荐的灵活性和便捷性；在一实施例中，通过不同的预设分级，调整请求方对应所处的区域划分方式的精度，并根据预设分级的优先级和区域中子区域的权重，确定用户推荐信息与请求方之间关联性的强弱，从而得到精度高和关联性强的用户推荐信息，提高推荐的精准性；在一实施例中，请求方对应的用户推荐信息为预设分级下对应区域中子区域内的用户的推荐信息，用户位于预设分级下对应区域的子区域内，即可实现推荐，无需获取用户具体的位置信息，实现对用户隐私信息的保护，进而提高了用户推荐的安全性。
图3示出了本公开实施例提供的另一种推荐方法的流程图。本实施例对于预设分级的优先级和区域中子区域的权重，进行详细的介绍。
在一实施例中，如图3所示，本实施例中的方法可以包括如下步骤S310、步骤S320和步骤S330。
在步骤S310中，设置每一预设分级的优先级和区域中子区域的权重。
在一实施例中，通过为每一预设分级设置对应的优先级，来确定预设分级的查找顺序，为不同预设分级下区域中各个子区域设置对应的权重，来确定各预设分级下对应的区域中子区域的查找顺序，以便确定后续的用户推荐信息与请求方之间关联性的强弱，进而根据预设分级的优先级和区域中子区域的权重 确定请求方对应的用户推荐信息。本实施例中在设置预设分级的优先级时，一般根据划分精度的高低来设置预设分级的优先级的高低，在设置区域中子区域的权重时，一般将当前位置的所在子区域的权重设置最高，关联子区域的权重根据其与所在子区域的关联程度来设置，本实施例中对于子区域权重具体的设置方式不作限定。
示例性的，本实施例中对于全球划分的位置区域分级包括两个预设分级geohash1和geohash2；其中，geohash1的经纬度划分精度为40*20，geohash2的经纬度划分精度为5*5，此时设置geohash2的优先级高于geohash1，在获取到推荐请求中请求方的当前位置时，此时可以根据请求方的当前位置确定请求方在geohash1和geohash2下对应区域中的子区域，该子区域包括请求方的当前位置的所在子区域和关联子区域；同时geohash1和geohash2下对应的区域分别为所在子区域和关联子区域组成的区域九宫格，此时设置所在子区域的权重高于关联子区域的权重；此时后续在确定请求方对应的用户推荐信息时，如果请求方的触发操作指示申请添加好友，且预设推荐条件为设定的好友推荐数量，则首先在geohash2对应的区域中当前位置的所在子区域查找对应存在的用户，并获取该所在子区域内用户的好友账号链接，同时判断所在子区域的用户数量是否达到设定的好友推荐数量，若达到则不再继续在关联子区域和geohash1对应的区域中查找，只需将当前在所在子区域查找到的用户的好友账号链接作为对应的用户推荐信息；若未达到设定的好友推荐数量，则根据关联子区域的权重继续在geohash2的关联子区域中查找对应存在的用户；若geohash2下对应区域中查找到的用户数量一直未达到设定的好友推荐数量，则按照在geohash2下对应区域中的查找步骤继续在geohash1下对应的区域中查找，层层递进判断，直至达到设定的好友推荐数量；进而得到请求方本次申请添加的好友账号链接，作为请求方对应的用户推荐信息。
在步骤S320中，根据推荐请求中请求方的当前位置，确定请求方在预设分级下对应区域中的子区域。
在步骤S330中，根据预设分级的优先级和区域中子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，得到请求方对应的用户推荐信息。
本公开实施例提供的技术方案，首先确定在预设分级下请求方的当前位置的所在子区域和关联子区域，并根据预设分级的优先级和区域中子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，无需请求方预先知道用户的 账号，也无需请求方根据用户的账号进行查找的操作，简化了用户推荐的操作，提高了向请求方推荐的灵活性和便捷性；在一实施例中，通过不同的预设分级，调整请求方对应所处的区域划分方式的精度，并根据预设分级和区域的属性，确定用户推荐信息与请求方之间关联性的强弱，从而得到精度高和关联性强的用户推荐信息，提高推荐的精准性；在一实施例中，请求方对应的用户推荐信息为预设分级下对应的区域内的用户的推荐信息，用户位于预设分级下对应的区域内，即可实现推荐，无需获取用户具体的位置信息，实现对用户隐私信息的保护，进而提高了用户推荐的安全性。同时，在用户推荐信息为用户推荐内容清单时，无需请求方预先关注该推荐用户，便可以实现推荐用户向请求方的信息推荐，无需预先向推荐用户申请添加好友，提高了推荐的灵活性。
在上述实施例提供的技术方案的基础上，对于本公开实施例提供的推荐方法所存在的其他情况进行进一步说明。上述推荐方法中，由于每一预设分级下对应的区域由多个子区域组成，为了准确全面的在各个子区域中查找对应存在的用户而不遗漏相应的用户推荐信息，本实施例中可以为每一预设分级下对应区域中的每一子区域设置对应的子区域列表，该子区域列表中存储当前位于该子区域内的用户所对应的各信息类型下的用户推荐信息，为了得到准确的用户推荐信息，本实施例为各子区域列表设定一个超时机制，此时上述推荐方法还可以包括：在预设更新时长内，如果重新获取到子区域的用户推荐信息，则根据重新获取的用户推荐信息更新对应子区域的用户推荐信息；如果未重新获取到子区域的用户推荐信息，则清理对应子区域的用户推荐信息。
在一实施例中，每一预设分级下对应区域中的子区域的子区域列表中对应有预设更新时长，本实施例对各子区域列表进行实时监控，如果在预设更新时长内，某个子区域列表重新获取到该子区域的用户推荐信息，也就是有用户重新加入到该子区域中，参与本次请求方的信息推荐，则将重新获取的用户推荐信息存储到该子区域对应的子区域列表中，从而根据重新获取的用户推荐信息更新对应子区域的用户推荐信息；如果在预设更新时长内，某个子区域一直未重新获取到该子区域的用户推荐信息，说明该子区域当前未参与请求方的信息推荐，此时该子区域中的用户推荐信息可能是前一次推荐时上报的信息，此时为了提高用户推荐信息的实时准确性，需要将该子区域对应的子区域列表中当前存储的用户推荐信息删除，也就是清理对应子区域的用户推荐信息，以便后续根据请求方在预设分级下对应区域的属性准确获取对应的用户推荐信息。
在一实施例中，如果子区域列表在存入新的用户推荐信息时，达到列表的容量上限，则可以将存储时间最早的用户推荐信息删除，而加入新的用户推荐信息；同时在用户退出“推荐请求”功能对应的页面时，可以将该用户当前位置的所在子区域对应的子区域列表中存储的该用户推荐信息删除，以保证后续另一请求方请求对应用户推荐信息时的准确性。
此外，由于请求方定位的精度可能存在误差，使得本实施例中根据预设分级的优先级，以及请求方的当前位置的所在子区域和关联子区域的权重，并未查找到请求方对应的用户推荐信息，因此为了保证请求方推荐的触达率，上述推荐方法中还可以包括：如果请求方对应的用户推荐信息不为空，则将用户推荐信息发送给请求方；如果请求方对应的用户推荐信息为空，则向请求方发送重定位请求，以使请求方重新获取当前位置并上报。
在一实施例中，在根据推荐请求中请求方的当前位置，获取到请求方对应的用户推荐信息时，首先判断用户推荐信息是否为空，以确定请求方的当前位置是否定位准确；如果请求方对应的用户推荐信息不为空，则直接将该用户推荐信息发送给请求方，执行对应的操作，如将查找到的好友账号链接发送给请求方时，请求方直接根据该好友账号链接申请添加好友；如图4所示，请求方显示的用户推荐信息中可以包括待添加好友的头像、昵称以及个人主页的入口等，同时存在表示好友账号链接的“关注”按钮，请求方通过点击该“关注”按钮，来添加对应的好友，此时被关注的用户页面也会对应弹出一个弹窗来显示请求方的关注情况，被关注用户也可以通过点击该弹窗中的“关注”按钮实现互关，也可以直接关闭弹窗。此外，如果请求方对应的用户推荐信息为空，说明请求方的当前位置在预设分级下对应区域的子区域中不存在其他用户，可能是请求方定位出现误差，因此服务端直接生成一个重定位请求发送给请求方，指示请求方重新获取当前位置，并上报给服务端，由服务端根据重新上报的当前位置按照上述步骤重新确定请求方对应的用户推荐信息。此时由于当前位置定位不准确，重定位时可能会将同一用户在不同时刻下的当前位置存储到不同子区域对应的子区域列表中，导致同一用户的位置重复，使得用户推荐信息不准确，因此在存在重定位需求时，需要清理用户实际不存在的子区域列表，此时如果子区域列表中仅存在一个用户的用户推荐信息，且在较短时长内未加入新的用户，则直接删除该字区域列表中的用户推荐信息。
图5示出了本公开实施例提供的一种推荐装置的结构示意图，本公开实施例可适用于多方用户面对面请求对应的推荐信息的情况中，该装置可以通过软件和/或硬件来实现，并集成在执行本方法的设备中。如图6所示，本公开实施例中的推荐装置，可以包括：子区域确定模块510和推荐信息确定模块520。
子区域确定模块510，设置为根据推荐请求中请求方的当前位置，确定请求方在预设分级下对应区域中的子区域。
推荐信息确定模块520，设置为根据预设分级的优先级和区域中子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，得到请求方对应的用户推荐信息。
本公开实施例提供的技术方案，直接根据请求方所在的当前位置向请求方发送用户推荐信息，从而无需请求方预先知道用户的账号，也无需请求方根据用户的账号进行查找的操作，简化了用户推荐的操作，提高了向请求方推荐的灵活性和便捷性；在一实施例中，通过不同的预设分级，调整请求方对应所处的区域划分方式的精度，并根据预设分级的优先级和区域中子区域的权重，确定用户推荐信息与请求方之间关联性的强弱，从而得到精度高和关联性强的用户推荐信息，提高推荐的精准性；在一实施例中，请求方对应的用户推荐信息为预设分级下对应区域中子区域内的用户的推荐信息，用户位于预设分级下对应区域的子区域内，即可实现推荐，无需获取用户具体的位置信息，实现对用户隐私信息的保护，进而提高了用户推荐的安全性。
在一实施例中，上述子区域确定模块510，可以设置为：
确定每一预设分级下所述当前位置的所在子区域和关联子区域，作为所述请求方在预设分级下对应区域中的子区域。
在一实施例中，上述推荐装置，还可以包括：
属性设置模块，设置为设置每一预设分级的优先级和区域中子区域的权重。
在一实施例中，上述推荐装置，还可以包括：
更新模块，设置为在预设更新时长内，如果重新获取到子区域的用户推荐信息，则根据重新获取的用户推荐信息更新对应子区域的用户推荐信息；如果未重新获取到子区域的用户推荐信息，则清理对应子区域的用户推荐信息。
在一实施例中，上述推荐装置，还可以包括：
推荐模块，设置为如果请求方对应的用户推荐信息不为空，则将用户推荐信息发送给请求方；
重定位模块，设置为如果请求方对应的用户推荐信息为空，则向请求方发送重定位请求，以使请求方重新获取当前位置并上报。
在一实施例中，上述用户推荐信息至少可以包括如下信息之一：用户清单和用户推荐内容清单。
在一实施例中，上述当前位置可以为经纬度位置，上述区域可以为经纬度区域。
本公开实施例提供的推荐装置，与上述实施例提供的推荐方法属于同一发明构思，未在本公开实施例中详尽描述的技术细节可参见上述实施例，并且本公开实施例与上述实施例具有相同的功能。
下面参考图6，其示出了适于用来实现本公开实施例的设备600的结构示意图。本公开实施例中的设备可以包括但不限于诸如移动电话、笔记本电脑、数字广播接收器、个人数字助理(Personal Digital Assistant，PDA)、平板电脑(Portable Android Device，PAD)、便携式多媒体播放器(Portable Media Player，PMP)、车载终端(例如车载导航终端)等等的移动终端以及诸如数字(Television，TV)、台式计算机等等的固定终端。图6示出的设备仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图6所示，设备600可以包括处理装置(例如中央处理器、图形处理器等)601，其可以根据存储在只读存储器(Read Only Memory，ROM)602中的程序或者从存储装置608加载到随机访问存储器(Random Access Memory，RAM)603中的程序而执行各种适当的动作和处理。在RAM 603中，还存储有设备600操作所需的各种程序和数据。处理装置601、ROM 602以及RAM 603通过总线604彼此相连。输入/输出(Input/Output，I/O)接口605也连接至总线604。
通常，以下装置可以连接至I/O接口605：包括例如触摸屏、触摸板、键盘、鼠标、摄像头、麦克风、加速度计、陀螺仪等的输入装置606；包括例如液晶显示器(Liquid Crystal Display，LCD)、扬声器、振动器等的输出装置607；包括例如磁带、硬盘等的存储装置608；以及通信装置609。通信装置609可以允许设备600与其他设备进行无线或有线通信以交换数据。虽然图6示出了具有各种装置的设备600，但是应理解的是，并不要求实施或具备所有示出的装置。可以替代地实施或具备更多或更少的装置。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为 计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含设置为执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置609从网络上被下载和安装，或者从存储装置608被安装，或者从ROM 602被安装。在该计算机程序被处理装置601执行时，执行本公开实施例的方法中限定的上述功能。
需要说明的是，本公开上述的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(Erasable Programmable Read Only Memory，EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(Compact Disc Read-Only Memory，CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读信号介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
上述计算机可读介质可以是上述设备中所包含的；也可以是单独存在，而未装配入该设备中。
上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被该设备执行时，使得该设备：根据推荐请求中请求方的当前位置，确定请求方在预设分级下对应区域中的子区域；根据预设分级的优先级和区域中子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，得到请求方对应的用户推荐信息。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(Local Area Network，LAN)或广域网(Wide Area Network，WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，单元的名称在某种情况下并不构成对该单元本身的限定。
以上描述仅为本公开的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本公开中所涉及的公开范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离上述公开构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本公开中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。

Claims (15)

  1. 一种推荐方法，包括：
    根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域；
    根据所述预设分级的优先级和所述预设分级下区域中的子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，以得到与所述请求方对应的用户推荐信息。
  2. 根据权利要求1所述的方法，其中，所述根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域，包括：
    所述预设分级下所述当前位置的所在子区域和关联子区域，作为所述请求方在所述预设分级下区域中的子区域。
  3. 根据权利要求1所述的方法，在根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域之前，还包括：
    设置多个预设分级的每一预设分级的优先级和每一预设分级下区域中子区域的权重。
  4. 根据权利要求1所述的方法，还包括：
    在预设更新时长内，在重新获取到子区域的用户推荐信息的情况下，根据重新获取的用户推荐信息更新所述子区域的用户推荐信息；在未重新获取到子区域的用户推荐信息的情况下，清理所述子区域的用户推荐信息。
  5. 根据权利要求4所述的方法，在确定与所述请求方对应的用户推荐信息之后，还包括：
    在与所述请求方对应的用户推荐信息不为空的情况下，将所述用户推荐信息发送给所述请求方；
    在与所述请求方对应的用户推荐信息为空的情况下，向所述请求方发送重定位请求，以使所述请求方重新获取当前位置并上报。
  6. 根据权利要求1至5任一项所述的方法，其中，所述用户推荐信息包括如下信息至少之一：用户清单和用户推荐内容清单。
  7. 根据权利要求1至5任一项所述的方法，其中，所述当前位置为经纬度位置，所述区域为经纬度区域。
  8. 一种推荐装置，包括：
    子区域确定模块，设置为根据推荐请求中请求方的当前位置，确定所述请求方在预设分级下区域中的子区域；
    推荐信息确定模块，设置为根据所述预设分级的优先级和所述预设分级下区域中的子区域的权重，确定符合预设推荐条件的子区域的用户推荐信息，以得到与所述请求方对应的用户推荐信息。
  9. 根据权利要求8所述的装置，所述子区域确定模块还设置为：
    将所述预设分级下所述当前位置的所在子区域和关联子区域，作为所述请求方在预设分级下区域中的子区域。
  10. 根据权利要求8所述的装置，还包括：
    属性设置模块，设置为设置多个预设分级中的每一预设分级的优先级和每一预设分级下区域中子区域的权重。
  11. 根据权利要求8所述的装置，还包括：
    更新模块，设置为在预设更新时长内，在重新获取到子区域的用户推荐信息的情况下，根据重新获取的用户推荐信息更新所述子区域的用户推荐信息；在未重新获取到子区域的用户推荐信息的情况下，清理子区域的用户推荐信息。
  12. 根据权利要求11所述的装置，还包括：
    推荐模块，设置为在与请求方对应的用户推荐信息不为空的情况下，将用户推荐信息发送给请求方；
    重定位模块，设置为在与请求方对应的用户推荐信息为空的情况下，向请求方发送重定位请求，以使请求方重新获取当前位置并上报。
  13. 根据权利要求8至12任一项所述的装置，所述用户推荐信息包括如下信息至少之一：用户清单和用户推荐内容清单。
  14. 一种设备，包括：
    至少一个处理器；
    存储器，设置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-7中任一项所述的推荐方法。
  15. 一种可读介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现如权利要求1-7中任一项所述的推荐方法。
PCT/CN2020/073758 2019-04-29 2020-01-22 推荐方法以及装置、设备和介质 Ceased WO2020220778A1 (zh)

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