A method and apparatus for information recommendation

Abstract

The present invention discloses a method and device for information recommendation, relating to the field of computer technology. A specific embodiment of the method includes: determining at least one location data group according to a plurality of received initial location data, and determining the core location data of each location data group; determining a recommended area corresponding to each location data group according to the core location data of each location data group; generating recommendation information according to the recommended area and the service object to be recommended. This method greatly reduces the amount of calculation in the information recommendation process, improves the system performance, and enhances the timeliness of recommendation and user experience.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method and device for information recommendation. Background Art

[0002] In some business forms, geographical location is a very important reference attribute, such as e-commerce, real estate, education, etc. In order to enable users to find the most suitable business object in a short time, for example, for recommending a house, the business object to be recommended is a house.

[0003] Currently, the recommendation method based on geographical location mainly generates recommendation information according to a geographical location and a preset fixed distance threshold, and then determines the relationship between the distance and a preset fixed distance threshold (such as 10 km), and further determines the target item to be recommended. For example, only items smaller than the threshold can be recommended, so as to implement a simple geographical location-based recommendation algorithm. For the case of multiple location data, it is necessary to calculate the distance between each location data and the business object to be recommended, and then further screen out the business objects to be recommended. This not only has a very large amount of calculation, affects the performance of the recommendation, but also affects the timeliness of the recommendation and damages the user experience. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and device for information recommendation, which can greatly reduce the amount of calculation in the information recommendation process, improve the system performance, and enhance the timeliness of the recommendation and the user experience.

[0005] To achieve the above object, according to one aspect of the embodiments of the present invention, a method for information recommendation is provided.

[0006] The method for information recommendation according to the embodiments of the present invention includes: determining at least one location data group according to the received multiple initial location data, and determining the core location data of each location data group; determining the recommended area corresponding to the location data group according to the core location data of each location data group; generating recommendation information according to the recommended area and the business object to be recommended.

[0007] Optionally, the step of determining at least one location data group according to the received multiple initial location data and determining the core location data of each location data group includes: receiving multiple initial location data and the attribute information of the location data; the attribute information at least includes a classification label and a priority; grouping the multiple initial location data according to the classification label to obtain at least one location data group; screening out the core location data of the location data group from the initial location data of each location data group according to the priority.

[0008] Optionally, the steps of determining at least one location data group according to the received multiple initial location data and determining the core location data of each location data group include: performing clustering processing on the received multiple initial location data to obtain at least one location data group; determining the clustering center location of each location data group, and the clustering center location is the core location data.

[0009] Optionally, the steps of determining the recommended area corresponding to each location data group according to the core location data of each location data group include: for each location data group, determining the distance set of the location data group; the distance set includes the distances between all the initial location data in the location data group and its core location data; determining the recommended area corresponding to the location data group according to the distance set of each location data group.

[0010] Optionally, the steps of determining the recommended area corresponding to each location data group according to the distance set of each location data group include: for each distance in the distance set, determining whether the distance is less than the first threshold ε;

[0011] If it is less than, then a circular area is determined with the core location data corresponding to the distance as the center and the sum of the distance and the preset second threshold D as the radius, and the circular area is the recommended area corresponding to the distance set where the distance is located;

[0012] Otherwise, two circular areas are determined with the core location data and the initial location data corresponding to the distance as the centers and the second threshold D as the radius respectively, and the two circular areas are the recommended areas corresponding to the distance set where the distance is located.

[0013] Optionally, before determining whether each distance in the distance set is less than the first threshold ε, it further includes: determining the first threshold ε according to the following formula:

[0014] where D is the second threshold.

[0015] To achieve the above object, according to another aspect of the embodiments of the present invention, an information recommendation device is provided.

[0016] The information recommendation device according to the embodiments of the present invention includes:

[0017] A core location determination module, configured to determine at least one location data group according to the received multiple initial location data, and determine the core location data of each location data group;

[0018] A recommended area determination module, configured to determine the recommended area corresponding to each location data group according to the core location data of each location data group;

[0019] An information generation module, configured to generate recommendation information according to the recommended area and the business object to be recommended.

[0020] Optionally, the core position determination module is further configured to receive a plurality of initial position data and attribute information of the position data; the attribute information at least includes a classification label and a priority; group the plurality of initial position data according to the classification label to obtain at least one position data group; and screen out the core position data of the position data group from the initial position data of each position data group according to the priority.

[0021] Optionally, the core position determination module is further configured to perform clustering processing on the received plurality of initial position data to obtain at least one position data group; and determine the clustering center position of each position data group, where the clustering center position is the core position data.

[0022] Optionally, the recommended area determination module is further configured to, for each position data group, determine the distance set of the position data group; the distance set includes the distances between all the initial position data in the position data group and its core position data; and determine the recommended area corresponding to the position data group according to the distance set of each position data group.

[0023] Optionally, the recommended area determination module is further configured to, for each distance in the distance set, determine whether the distance is less than a first threshold ε;

[0024] If it is less than, a circular area is determined with the core position data corresponding to the distance as the center and the sum of the distance and a preset second threshold D as the radius, and the circular area is the recommended area corresponding to the distance set where the distance is located;

[0025] Otherwise, two circular areas are determined with the core position data and the initial position data corresponding to the distance as the centers and the second threshold D as the radius respectively, and the two circular areas are the recommended areas corresponding to the distance set where the distance is located.

[0026] Optionally, the recommended area determination module is further configured to determine the first threshold ε according to the following formula:

[0027] where D is the second threshold.

[0028] To achieve the above object, according to another aspect of the embodiments of the present invention, an electronic device is provided.

[0029] The electronic device according to the embodiments of the present invention includes: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the information recommendation method of any one of the above.

[0030] To achieve the above object, according to another aspect of the embodiments of the present invention, there is provided a computer-readable medium having a computer program stored thereon, characterized in that when the program is executed by a processor, it implements the information recommendation method of any one of the above.

[0031] One embodiment of the above invention has the following advantages or beneficial effects: grouping a plurality of received location data, and for each divided location data group, determining a core location data. And, according to the core location data of each location data group, determining a recommended area corresponding to the location data group, and then screening out business objects to be recommended from within the recommended area corresponding to each location data group. In this process, it is not necessary to calculate the distance between each location data and the business object to be recommended. Therefore, the amount of calculation in the information recommendation process is greatly reduced, the system performance is improved, and the timeliness of recommendation and user experience are enhanced.

[0032] The further effects of the above non-conventional optional manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention. Among them:

[0034] Figure 1 is a schematic diagram of the main process of the information recommendation method according to the embodiments of the present invention;

[0035] Figure 2 is a schematic diagram of determining a recommended area according to the embodiments of the present invention;

[0036] Figure 3 is a schematic diagram of determining a recommended area according to the embodiments of the present invention;

[0037] Figure 4 is a theoretical schematic diagram of setting a first threshold according to the embodiments of the present invention;

[0038] Figure 5 is a schematic diagram of the information recommendation method according to the embodiments of the present invention;

[0039] Figure 6 is a schematic diagram of the main modules of the information recommendation device according to the embodiments of the present invention;

[0040] Figure 7 is an exemplary system architecture diagram to which the embodiments of the present invention can be applied;

[0041] Figure 8 is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0043] In the prior art, for location-based recommendation methods, recommendation information is mainly generated based on a geographical location and a preset fixed distance threshold. For example:

[0044] First, convert the location data to be referenced (such as the user's location or the company's location) into longitude and latitude information (x1, y1), and determine the longitude and latitude information (x2, y2) of the business object to be recommended. Calculate through the following formula:

[0045]

[0046] where ρ is the Euclidean distance between the point (x2, y2) and the point (x1, y1).

[0047] Through the calculation of the above formula, the distance ρ between the business object to be recommended and the user's location can be obtained.

[0048] Then, determine the relationship between the distance and a preset fixed distance threshold (such as 10 km), and further determine the target item to be recommended.

[0049] This method not only has a very large amount of calculation, affecting the performance of the recommendation, but also affects the timeliness of the recommendation, degrading the user experience.

[0050] Figure 1 is a schematic diagram of the main process of the information recommendation method according to an embodiment of the present invention. As Figure 1 shown, the information recommendation method according to an embodiment of the present invention mainly includes:

[0051] Step S101: Determine at least one location data group based on the received multiple initial location data, and determine the core location data of each location data group. The received multiple initial location data can be obtained from a database according to the user's input requirements, or directly input by the user. To avoid having to determine the distance between the business object to be recommended and each initial location data during subsequent calculations, the multiple initial location data can be grouped, with each group being a location data group. And, to further reduce the amount of calculation, the core location data of each location data group can be determined, and then the business object to be recommended can be determined based on the core location data subsequently.

[0052] Specifically, multiple initial position data and attribute information of the position data are received. The attribute information at least includes a classification label and a priority. The attribute information of the position data can be obtained through a client or docked with a label system to obtain the attribute information of the user's position data. After receiving the above data, the multiple initial position data are grouped according to the classification label to obtain at least one position data group. Then, according to the priority, the core position data of the position data group is screened out from the initial position data of each position data group. The core position can refer to the position with the highest priority under the position data group. For different business forms, the core position is different.

[0053] As described in the following table, according to classification labels A and B, multiple initial position data: company location, company business district location, company subway location, residential location, residential business district location, residential subway location are divided into two groups, namely: Group A {company location, company business district location, company subway location} and Group B {residential location, residential business district location, residential subway location}. Assuming that the priority is enhanced according to 3, 2, 1, the core position data of the above two position data groups can be the company location and the residential location respectively.

[0054]

[0055] Alternatively, clustering processing is performed on the received multiple initial position data to obtain at least one position data group. Then, the clustering center position of each position data group is determined, and the clustering center position is the core position data. Among them, the methods adopted for clustering processing include k-means clustering or bisecting k-means clustering algorithms. For the position data group determined by this method, it is considered that the clustering center position of this position data group is the core position data with the highest priority. And, this core center position is not necessarily the received initial position data.

[0056] Step S102: Determine the recommended area corresponding to each position data group according to the core position data of each position data group. Specifically, for each position data group, a distance set of the position data group is determined. The distance set includes the distances between all the initial position data in the position data group and its core position data. For example, in the above example, for the distance set of Group A, it includes the distance X11 between the company business district location and the company location, and the distance X12 between the company subway location and the company location. According to the distance set of each position data group, the recommended area corresponding to the position data group is determined. For each position data group, one corresponding recommended area can be determined, thereby improving the accuracy of the recommendation.

[0057] Figure 2 It is a schematic diagram for determining the recommended area according to an embodiment of the present invention; Figure 3 It is a schematic diagram for determining the recommended area according to an embodiment of the present invention.

[0058] In the process of determining the recommended area corresponding to each position data group according to the distance set of each position data group, for each distance in the distance set, it is judged whether the distance is less than the first threshold ε. The first threshold ε can be set according to business requirements or calculated. For example, the core position data of a certain position data group is O, and any initial position data in this position data group is represented as P. The distance between O and P is X kilometers (km). It is judged whether this X is less than the first threshold ε. For different judgment results, different decision-making methods for determining the recommended area can be selected. If it is less than, as Figure 3 shown, taking the core position data O corresponding to the distance X as the center and the sum of this distance and the preset second threshold D (i.e., D + X) as the radius to determine a circular area, and this circular area is the recommended area corresponding to the distance set where this distance is located. At this time, the recommended distance corresponding to this position data group is as Figure 3 the area enclosed by the dotted line in. Otherwise, as Figure 2 shown, respectively taking the core position data O and the initial position data P corresponding to this distance as the centers and the second threshold D as the radii to determine two circular areas, and these two circular areas are the recommended areas corresponding to the distance set where this distance is located. Among them, the second threshold D is a specific number, which means that items within a radius of D km are recommended to the user based on a specific geographical location, that is, if the Euclidean distance from the business object to be recommended to a certain position is less than D, it is recommended, and if it is greater than or equal to D, it is not recommended.

[0059] The above circular areas do not limit the present invention. After judging the magnitude relationship between the distance X and the first threshold ε, a rectangular area can be determined according to the core position O, the initial position data P corresponding to X, as well as the second threshold D and the distance X.

[0060] Figure 4 is a theoretical schematic diagram of setting the first threshold according to an embodiment of the present invention.

[0061] And, before judging whether each distance in the distance set is less than the first threshold ε, the first threshold ε is determined according to the following formula: where D is the second threshold. As Figure 4 shown, respectively taking the core position O and the initial position data P as the centers and the second threshold D as the radii to determine circular areas, and the overlapping area of these two circular areas is S. Taking the core position O as the center and D + X as the radius to determine a third circular area. Compared with the above two circular areas, the area by which the third circular area exceeds the above two circular areas is S'. If expanding the recommended range will introduce errors, the error area is S', and if the range is not expanded and calculated independently for each position, the overlapping area is S. So when the two areas are equal, it is the balance point between advantages and disadvantages. If S = S', then π(D + X)2 -πD 2 -(πD 2 -S) = S, and finally solve for X to obtain the value of ε. That is:

[0062]

[0063] Step S103: Generate recommendation information based on the recommended area and the business objects to be recommended. Calculate the distance between the core location and the locations of the business objects to be recommended, and generate an initial list of recommended objects. Then, based on the recommended area determined above, de-duplicate, sort, and filter the initial list of recommended objects, and finally form a complete location-based recommendation list to be given to the user. That is, according to the location data of the business objects to be recommended obtained, based on the recommended area determined above, filter out the business objects located in the recommended area, and generate list information to be sent to the customer.

[0064] In the embodiment of the present invention, first, a plurality of received location data are grouped, and for each separated location data group, a core location data is determined. Moreover, according to the core location data of each location data group, the recommended area corresponding to the location data group is determined, and then, from within the recommended area corresponding to each location data group, the business objects to be recommended are filtered out. In this process, it is not necessary to calculate the distance between each location data and the business objects to be recommended. Therefore, the amount of calculation in the information recommendation process is greatly reduced, the system performance is improved, and the timeliness of the recommendation and the user experience are enhanced.

[0065] Figure 5 is a schematic diagram of the information recommendation method according to the embodiment of the present invention, as Figure 5 shown, the information recommendation method according to the embodiment of the present invention includes:

[0066] Step S501: Connect to the location tag system to obtain the classification tags and priorities of a plurality of initial location data. For example, the classification tags obtained from the location tag system include: classification tag 1, classification tag 2, classification tag 3,..., classification tag n, where n is the number of classification tags.

[0067] Step S502: Group the initial location data according to the classification tags and priorities, and determine the core location data of each group. For example, for the location data group corresponding to classification tag 1, its priorities are 11, 12, 13,..., and the initial location data with a priority of 11 is determined as the core location data of this group.

[0068] Step S503: Calculate the distance X between the initial location data (excluding the core location data) and the core location data in each location data group respectively. This distance is the Euclidean distance between two location data.

[0069] Step S504: Determine whether X is less than the first threshold ε. If not less, proceed to step S505; otherwise, proceed to step S506.

[0070] Step S505: Respectively, with the core position data and the initial position data corresponding to the distance X as separate centers, and the second threshold D as the radius, determine two circular regions. These two circular regions are the recommended regions corresponding to the distance set where the distance is located, and subsequently recommend items within a range of D km in radius for each of the two regions to the user.

[0071] Step S506: With the core position data corresponding to the distance X as the center, and with D + X as the radius, determine a circular region. This circular region is the recommended region corresponding to the distance set where the distance is located. Subsequently, recommend items within a range of (D + X) km in radius centered on the core position to the user.

[0072] Step S507: Generate recommendation information. In the embodiments of the present invention, calculate the distance between the core position and the position of the business object to be recommended, and generate an initial list of recommended objects. And, based on the recommended regions determined above, perform deduplication, sorting, and filtering on the initial list of recommended objects, and finally form a complete location-based recommendation list to be given to the user.

[0073] The prior art can meet the basic requirements for solving services with only one geographical location feature. However, in some special services, the geographical location may contain a lot of location data, such as residential location, work location, current location, etc. If for such a large number of location data sets, an Euclidean distance calculation is performed with the business object to be recommended once, the calculation amount will be very large, affecting the performance and timeliness of the recommendation, and damaging the user experience.

[0074] In the embodiments of the present invention, by grouping the initial position data, then screening the business objects to be recommended based on the core position data therein, and, according to the core position data of each position data group, determining the recommended region corresponding to the position data group, and further screening out the business objects to be recommended from within the recommended region corresponding to each position data group, the distance between each position data and the business object to be recommended does not need to be calculated in this process. Based on the recommendation logic for multiple geographical locations, the impact on the system performance caused by distance calculation is reduced, and thus the timeliness calculation speed of the recommendation is improved. On the other hand, compared with the prior art where some business objects to be recommended are screened out according to a set fixed distance threshold, the embodiments of the present invention can dynamically set the radius for selecting the recommended region, ensuring that the most suitable and relatively comprehensive items are recommended to the user, ensuring the coverage rate of the recommendation system, and being able to guarantee the performance without affecting the final recommendation effect.

[0075] Figure 6 is a schematic diagram of the main modules of the device for information recommendation according to the embodiments of the present invention, asFigure 6 As shown in Figure 6 , the information recommendation device 600 according to an embodiment of the present invention includes a core position determination module 601, a recommended area determination module 602, and an information generation module 603.

[0076] The core position determination module 601 is configured to determine at least one set of position data and determine the core position data of each set of position data according to a plurality of received initial position data.

[0077] The core position determination module is further configured to receive a plurality of initial position data and attribute information of the position data; the attribute information at least includes a classification label and a priority; group the plurality of initial position data according to the classification label to obtain at least one set of position data; and screen out the core position data of the set of position data from the initial position data of each set of position data according to the priority.

[0078] The core position determination module is further configured to perform clustering processing on the received plurality of initial position data to obtain at least one set of position data; and determine the clustering center position of each set of position data, and the clustering center position is the core position data.

[0079] The recommended area determination module 602 is configured to determine the recommended area corresponding to each set of position data according to the core position data of each set of position data. The recommended area determination module is further configured to, for each set of position data, determine the distance set of the set of position data; the distance set includes the distances between all the initial position data in the set of position data and its core position data; and determine the recommended area corresponding to the set of position data according to the distance set of each set of position data.

[0080] The recommended area determination module is further configured to, for each distance in the distance set, determine whether the distance is less than a first threshold ε. If it is less than, a circular area is determined with the core position data corresponding to the distance as the center and the sum of the distance and a preset second threshold D as the radius, and the circular area is the recommended area corresponding to the distance set where the distance is located. Otherwise, two circular areas are determined with the core position data and the initial position data corresponding to the distance as the centers and the second threshold D as the radii, and the two circular areas are the recommended areas corresponding to the distance set where the distance is located. The recommended area determination module is further configured to determine the first threshold ε according to the following formula: where D is the second threshold.

[0081] The information generation module 603 is configured to generate recommendation information according to the recommended area and the service object to be recommended.

[0082] In an embodiment of the present invention, multiple received location data are first grouped, and for each divided location data group, a core location data is determined. Further, according to the core location data of each location data group, a recommended area corresponding to the location data group is determined, and then business objects to be recommended are filtered out from within the recommended area corresponding to each location data group. In this process, it is not necessary to calculate the distance between each location data and the business object to be recommended. Therefore, the amount of calculation in the information recommendation process is greatly reduced, the system performance is improved, and the timeliness of recommendation and user experience are enhanced.

[0083] Figure 7 FIG. 700 shows an exemplary system architecture to which the method or apparatus for information recommendation according to an embodiment of the present invention can be applied.

[0084] As Figure 7 shown, the system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 is used to provide a medium for communication links between the terminal devices 701, 702, 703 and the server 705. The network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0085] Users can use the terminal devices 701, 702, 703 to interact with the server 705 through the network 704 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 701, 702, 703, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0086] The terminal devices 701, 702, 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.

[0087] The server 705 may be a server providing various services, such as a background management server (for example only) that supports shopping websites browsed by users using the terminal devices 701, 702, 703. The background management server may analyze and process data such as product information query requests received, and feedback the processing results to the terminal devices.

[0088] It should be noted that the method for information recommendation provided by the embodiments of the present invention is generally executed by the server 705. Correspondingly, the apparatus for information recommendation is generally provided in the server 705.

[0089] It should be understood, Figure 7The numbers of the terminal devices, networks, and servers in [the description] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0090] Reference is made below to Figure 8 , which shows a schematic structural diagram of a computer system 800 of a terminal device suitable for implementing the embodiments of the present invention. Figure 8 The terminal device shown is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

[0091] As Figure 8 shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage section 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the system 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0092] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0093] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, the above functions defined in the system of the present invention are executed.

[0094] It should be noted that the computer-readable medium shown in the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or by a combination of dedicated hardware and computer instructions.

[0096] The modules involved in the embodiments of the present invention can be implemented in software or in hardware. The described modules can also be provided in a processor. For example, it can be described as: a processor includes a core position determination module, a recommended area determination module, and an information generation module. Among them, the names of these modules do not constitute a limitation to the module itself in some cases. For example, the core position determination module can also be described as "a module that determines at least one set of position data based on multiple received initial position data and determines the core position data of each set of position data".

[0097] As another aspect, the present invention also provides a computer-readable medium. The computer-readable medium can be included in the device described in the above embodiments; or it can exist independently and is not assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device includes: determining at least one set of position data based on multiple received initial position data and determining the core position data of each set of position data; determining the recommended area corresponding to each set of position data based on the core position data of each set of position data; and generating recommendation information based on the recommended area and the business object to be recommended.

[0098] In the embodiments of the present invention, first, multiple received position data are grouped, and for each divided set of position data, a core position data is determined. And, based on the core position data of each set of position data, the recommended area corresponding to each set of position data is determined. Then, the business object to be recommended is screened from within the recommended area corresponding to each set of position data. In this process, it is not necessary to calculate the distance between each position data and the business object to be recommended. Therefore, the amount of calculation in the information recommendation process is greatly reduced, the system performance is improved, and the timeliness of recommendation and user experience are enhanced.

[0099] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for information recommendation, characterized in that, including: determining at least one position data group according to the received multiple initial position data, and determining the core position data of each position data group; determining the recommended area corresponding to the position data group according to the core position data of each position data group; generating recommendation information according to the recommended area and the business object to be recommended; The step of determining the recommended area corresponding to the position data group according to the core position data of each position data group includes: for each position data group, determining the distance set of the position data group; the distance set includes the distances between all the initial position data in the position data group and its core position data; for each distance in the distance set, determining whether the distance is less than the first threshold ε; if it is less than, determining a circular area with the core position data corresponding to the distance as the center and the sum of the distance and the preset second threshold D as the radius, and this circular area is the recommended area corresponding to the distance set where the distance is located; otherwise, determining two circular areas with the core position data and the initial position data corresponding to the distance as the centers and the second threshold D as the radius respectively, and these two circular areas are the recommended areas corresponding to the distance set where the distance is located.

2. The method according to claim 1, characterized in that, The step of determining at least one position data group according to the received multiple initial position data, and determining the core position data of each position data group includes: receiving multiple initial position data and the attribute information of the position data; the attribute information at least includes a classification label and a priority; grouping the multiple initial position data according to the classification label to obtain at least one position data group; screening out the core position data of the position data group from the initial position data of each position data group according to the priority.

3. The method according to claim 1, characterized in that, The step of determining at least one position data group according to the received multiple initial position data, and determining the core position data of each position data group includes: performing clustering processing on the received multiple initial position data to obtain at least one position data group; determining the clustering center position of each position data group, and the clustering center position is the core position data.

4. The method according to claim 1, characterized in that, Before determining whether each distance in the distance set is less than the first threshold ε, it further includes: determining the first threshold ε according to the following formula: Where D is the second threshold value mentioned above.

5. An apparatus for information recommendation, characterized in that, including: a core position determination module, configured to determine at least one position data group according to the received multiple initial position data, and determine the core position data of each position data group; a recommended area determination module, configured to determine the recommended area corresponding to the position data group according to the core position data of each position data group; an information generation module, configured to generate recommendation information according to the recommended area and the business object to be recommended; the recommended area determination module is further configured to: for each position data group, determine the distance set of the position data group; the distance set includes the distances between all the initial position data in the position data group and its core position data; For each distance in the distance set, determine whether the distance is less than a first threshold ε; if it is less than, then with the core position data corresponding to the distance as the center and the sum of the distance and a preset second threshold D as the radius, determine a circular area, and this circular area is the recommended area corresponding to the distance set where the distance is located; otherwise, respectively with the core position data and the initial position data corresponding to the distance as the centers and the second threshold D as the radius, determine two circular areas, and these two circular areas are the recommended areas corresponding to the distance set where the distance is located.

6. An electronic device, characterized in that, Including: One or more processors; A storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-4.

7. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1-4.

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