Information recommendation method, device, storage medium and electronic device

Abstract

This specification discloses a method, device, storage medium and electronic device for information recommendation, which obtains travel data of each user, determines the location of each user in different time periods based on the travel data, determines the travel directed graph of each user based on the location of each user in different time periods, determines the visit probability of each user to each preset area based on the travel directed graph of each user, and determines the commercial entity scale information required for constructing a commercial entity in each preset area based on the visit probability of each user, and recommends information based on the commercial entity scale information.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a method, device, storage medium, and electronic device for information recommendation. Background Art

[0002] Nowadays, with the continuous development of the economy, the scale of commercial complexes is also growing. Commercial complexes are based on a complex of buildings and combine commercial retail, hotel and catering, comprehensive entertainment and other functions to form a multifunctional, efficient, complex and unified complex. The emergence of commercial complexes has made people's lives more convenient.

[0003] When building commercial complexes, existing technologies primarily consider the construction of single-format commercial complexes, which can result in recommended commercial complexes whose scales don't fully meet the needs of nearby residents. Therefore, determining the appropriate scale for commercial complexes and recommending information is a pressing issue. Summary of the Invention

[0004] This specification provides a method, device, storage medium, and electronic device for information recommendation to partially solve the above-mentioned problems existing in the prior art.

[0005] This manual adopts the following technical solutions:

[0006] This specification provides a method for information recommendation, including:

[0007] Obtain travel data for each user;

[0008] Determining the location of each user at different time periods based on the travel data;

[0009] Determine a travel directed graph for each user based on the location of each user at different time periods, wherein for each user, the travel directed graph is used to represent the order in which the user arrives at each location;

[0010] Determining, based on the travel directed graph of each user, the probability of each user visiting each preset area, wherein the preset area is an area where a commercial entity needs to be built;

[0011] Determining the scale of a commercial entity required for constructing a commercial entity in each preset area based on the probability of each user visiting each preset area;

[0012] Information recommendation is performed based on the business entity size information.

[0013] Optionally, determining a travel directed graph for each user based on the location of each user at different time periods may include:

[0014] For each user, based on the user's travel data, determine the locations reached by the user during a preset nighttime period and the locations reached during a preset daytime period;

[0015] Constructing nodes corresponding to the locations reached by the user during the preset nighttime period, and constructing nodes corresponding to the locations reached by the user during the preset daytime period;

[0016] Each node corresponding to the location reached by the user during the night time period is pointed to each node corresponding to the location reached by the user during the day time period, so as to determine a travel directed graph of the user.

[0017] Optionally, determining the visit probability of each user to each preset area according to the travel directed graph of each user specifically includes:

[0018] Obtaining a preset distribution equation, wherein the preset distribution equation contains an unknown first parameter;

[0019] For each user, determining the visit probability of the user arriving at each of the preset areas according to the user's travel directed graph;

[0020] Inputting the access probability of each user arriving at each preset area into the preset distribution equation to determine the first parameter;

[0021] The access probability of each user visiting each preset area is determined based on the determined first parameter and the access probability of each user reaching each preset area.

[0022] Optionally, for each user, determining the visit probability of the user arriving at each preset area according to the user's travel directed graph specifically includes:

[0023] For each preset area, determine the number of directed edges involved in the user's visit to the preset area based on the user's travel directed graph;

[0024] The visit probability of the user visiting the preset area is determined according to the ratio of the number of directed edges involved in the user's visit to the preset area to the number of all directed edges in the user's travel directed graph.

[0025] Optionally, based on the visit probability of each user visiting each preset area, determining the commercial entity scale information required for constructing a commercial entity in each preset area specifically includes:

[0026] Obtaining a preset recommendation equation, wherein the preset recommendation equation contains the first parameter and an unknown second parameter;

[0027] Obtaining the number of existing merchants in the target area, and determining the second parameter of the preset recommendation equation based on the first parameter and the number of existing merchants in the target area;

[0028] Determining the number of merchants required in each preset area based on the visit probability of each user visiting each preset area and the recommendation equation for determining the second parameter;

[0029] Based on the determined number of businesses required in each preset area, the commercial entity scale information required when constructing a commercial entity in each preset area is determined.

[0030] This specification provides an information recommendation device, including:

[0031] Acquisition module, used to obtain travel data of each user;

[0032] A first determining module is used to determine the location of each user in different time periods based on the travel data;

[0033] A second determining module is configured to determine a travel directed graph of each user based on the location of each user at different time periods, wherein for each user, the travel directed graph of the user is used to represent the order in which the user arrives at each location;

[0034] A third determining module is configured to determine, based on the travel directed graph of each user, the probability of each user visiting each preset area, wherein the preset area is an area where a commercial entity needs to be built;

[0035] A fourth determining module is configured to determine the scale of a commercial entity required for constructing a commercial entity in each preset area based on the probability of each user visiting each preset area;

[0036] The recommendation module is used to recommend information based on the business entity scale information.

[0037] Optionally, the second determination module is specifically used to determine, for each user, based on the user's travel data, the various locations arrived at by the user during a preset night time period and the various locations arrived at during a preset day time period; respectively construct nodes corresponding to the various locations arrived at by the user during the preset night time period, and respectively construct nodes corresponding to the locations arrived at by the user during the preset day time period; and respectively point the various nodes corresponding to the locations arrived at by the user during the night time period to the various nodes corresponding to the locations arrived at by the user during the day time period, so as to determine the user's travel directed graph.

[0038] Optionally, the third determination module is specifically used to obtain a preset distribution equation, wherein the preset distribution equation contains an unknown first parameter; for each user, based on the user's travel directed graph, determine the visit probability of the user arriving at each preset area; input the visit probability of each user arriving at each preset area into the preset distribution equation to determine the first parameter; based on the determined first parameter and the visit probability of each user arriving at each preset area, determine the visit probability of each user visiting each preset area.

[0039] Optionally, the third determination module is also used to determine, for each preset area, the number of directed edges involved in the user's visit to the preset area based on the user's travel directed graph; and determine the probability of the user visiting the preset area based on the ratio of the number of directed edges involved in the user's visit to the preset area to the number of all directed edges in the user's travel directed graph.

[0040] Optionally, the fourth determination module is specifically used to obtain a preset recommendation equation, wherein the preset recommendation equation contains the first parameter and an unknown second parameter; obtain the number of existing merchants in the target area, and determine the second parameter of the preset recommendation equation based on the first parameter and the number of existing merchants in the target area; determine the number of merchants required in each preset area based on the recommendation equation determined by the second parameter according to the visit probability of each user visiting each preset area; and determine the commercial entity scale information required when constructing a commercial entity in each preset area based on the determined number of merchants required in each preset area.

[0041] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned information recommendation method.

[0042] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned information recommendation method when executing the program.

[0043] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0044] The information recommendation method provided in this manual determines the location of each user in different time periods based on the travel data of each user, and determines the travel directed graph of each user based on the location of each user in different time periods, and then determines the visit probability of each user to each preset area based on the travel directed graph of each user; and based on the visit probability of each user to each preset area, determines the commercial entity scale information required for constructing a commercial entity in each preset area, and then performs information recommendation.

[0045] As can be seen from the above method, when recommending information based on commercial entity size information, this application determines the commercial entity size information by the probability of each user visiting each preset area. This fully considers the impact of pedestrian traffic on the size of the commercial entity when constructing the commercial entity. Furthermore, when determining the probability of each user visiting each preset area, this application determines it by using each user's travel directed graph. This travel directed graph can fully reflect the user's location at different times and the order in which each user arrives at each location, accurately reflecting each user's visit to each preset area. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The exemplary embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation of this specification. In the drawings:

[0047] Figure 1 A flowchart of a method for recommending information provided in this specification;

[0048] Figure 2 It is a travel directed graph of a single user provided in this specification;

[0049] Figure 3 A schematic diagram of a recommended device structure for providing information in this manual;

[0050] Figure 4 This manual provides a corresponding Figure 1 Schematic diagram of the structure of the electronic equipment. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of this specification more clear, the following will clearly and completely describe the technical solutions of this specification in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this specification.

[0052] The technical solutions provided by the embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0053] Figure 1 A flowchart of a method for recommending information provided in this specification includes the following steps:

[0054] S100: Acquire travel data of each user.

[0055] S102: Determine the location of each user in different time periods based on the travel data.

[0056] A commercial complex is a multifunctional, efficient, complex, and unified complex based on a complex of buildings, combining retail, hotel and restaurant, and comprehensive entertainment. Today, with the continuous development of the economy, more and more commercial complexes are being established to fully meet people's living needs.

[0057] An information recommendation method provided in this specification can recommend information for planning the construction of commercial entities based on the commercial entity scale information required when constructing a commercial entity in each preset area, wherein the preset area is the area where the commercial entity needs to be constructed.

[0058] The execution subject of information recommendation in this specification can be a server or an electronic device such as a desktop computer, laptop computer, etc. For the sake of convenience, the following only uses the server as the execution subject to illustrate the information recommendation method provided in this specification.

[0059] When building a commercial complex, the number of visitors is an important factor affecting the scale of the commercial complex. Generally speaking, the scale of a commercial complex built in an area with more traffic is larger than that built in an area with less traffic.

[0060] The server can obtain the travel data of each user, such as the mobile phone signaling data of each user, etc. By obtaining the travel data of each user, it can determine the access status of each user in each preset area, and then determine the commercial entity scale information required to build a commercial entity in each preset area. The server can then recommend information based on the commercial entity scale information.

[0061] After obtaining each user's travel data, the server can determine each user's location at different time periods based on the travel data. Specifically, the server can arrange each user's travel data in the order of "user name, date, time, location" to determine each user's location at different time periods.

[0062] S104: Determine a travel directed graph of each user based on the location of each user in different time periods, wherein for each user, the travel directed graph of the user is used to represent the order in which the user arrives at various locations.

[0063] For each user, based on the user's travel data over the past period, the server can divide the user's travel time into a preset night time period and a preset day time period according to the preset day-night interval. For example, the server can obtain the user's travel data on January 1 and January 2, and set the time period from 21:00 on January 1 to 7:00 on January 2 as the preset night time period, and the time period from 7:00 on January 2 to 21:00 on January 2 as the preset day time period.

[0064] For each user, based on the user's travel data, the server can determine the locations reached by the user during the preset night time period and the locations reached during the preset daytime time period, and construct the nodes corresponding to the locations reached by the user during the preset night time period, as well as the nodes corresponding to the locations reached by the user during the preset daytime time period, to construct a travel directed graph for the user.

[0065] It is worth noting that, for the preset night time period and the preset day time period, the server may further divide the preset night time period into multiple night intervals and divide the preset day time period into multiple day intervals according to preset time intervals.

[0066] For example, for the preset daytime time period in the user's travel data obtained by the server on January 2, if the preset time interval is 1 hour, the server can divide the time period from 7:00 to 21:00 on January 2 into 14 day intervals, where 7:00 to 8:00 is one day interval and 8:00 to 9:00 is another day interval. Similarly, for the preset nighttime time period in the user's travel data, the server can also divide the preset nighttime time period into 10 night intervals.

[0067] Furthermore, for each user, the server can determine the locations that the user arrives at in each night interval and the locations that the user arrives at in each day interval, and respectively construct the nodes corresponding to the locations that the user arrives at in each night interval, and construct the nodes corresponding to the locations that the user arrives at in each day interval, thereby constructing a travel directed graph for the user.

[0068] For ease of understanding, Figure 2The user's travel directed graph provided in this specification. After constructing the nodes corresponding to the user's locations reached at different time periods based on the user's travel data, the server can point each node corresponding to the user's location reached during the night time period to each node corresponding to the user's location reached during the day time period to determine the user's travel directed graph.

[0069] For the user's travel directed graph, the travel directed graph can fully reflect the user's location at different times and the order in which each user arrives at each location, thereby accurately reflecting the user's visit to each preset area.

[0070] S106: Determine the visit probability of each user to each preset area based on the travel directed graph of each user, wherein the preset area is an area where a commercial entity needs to be built.

[0071] After determining the user's travel directed graph, the server can determine the visit probability of each user visiting each preset area based on the travel directed graph of each user. Specifically, the server can obtain a preset distribution equation: Among them, D ij is the number of directed edges of all users visiting the jth location in the i-th preset area in the past period of time, p(D ij ) is the probability of each user visiting the i-th preset area, and the preset distribution equation contains the first parameter α with unknown value. i and β i .

[0072] It is worth noting that for each preset area, based on the user's travel directed graph, the server can determine the number of directed edges involved in the user's visit to the preset area, and determine the user's visit probability p(D ij ).

[0073] Specifically, p(D ij ) is D ij Range (D ij ,2D ij The server may input the probability of each user reaching each preset area into a preset distribution equation to determine the specific value of the first parameter.

[0074] For example, for a user's travel directed graph, the server can determine that the number of nodes corresponding to the locations the user arrived at during the preset nighttime time period is 10, and these 10 nodes all point to the nodes corresponding to the locations the user arrived at during the adjacent daytime time period from 7:00 to 8:00. For the node with a daily interval of 7:00 to 8:00, the number of directed edges involved in the node is 10, and the number of all directed edges in the travel directed graph is 140. Then, the server can determine the ratio of the number of directed edges involved in the locations visited by the user during the daily interval to the number of all directed edges in the user's travel directed graph, that is, the ratio of 10 to 140, and then input the obtained ratio into the preset distribution equation to determine the specific value of the first parameter.

[0075] The server may determine the access probability of each user visiting each preset area according to the determined first parameter and the access probability of each user reaching each preset area.

[0076] S108: Determine the commercial entity scale information required for constructing a commercial entity in each preset area according to the access probability of each user visiting each preset area.

[0077] There are many ways to determine the commercial entity scale information required for constructing a commercial entity in each preset area based on the access probability of each user to each preset area. This specification does not limit the method for determining the commercial entity scale information.

[0078] For example, assuming that the access probability is positively correlated with the number of businesses in the preset area, then after determining the access probability of each user to visit each preset area, the server can determine the number of businesses in the preset area based on the positive correlation between the access probability and the number of businesses in the preset area, and then determine the commercial entity scale information required when constructing a commercial entity in the preset area.

[0079] Of course, after determining the probability of each user visiting each preset area and the α in the first parameter i After that, the server can also obtain the preset recommendation equation: The commercial scale information required for building a commercial complex in each preset area is determined by the preset recommendation equation. i is the number of merchants in the i-th preset area, and γ, θ, and δ are second parameters with unknown values.

[0080] In this specification, the server can determine the number of businesses required in the preset area to indicate the scale of the commercial entity required when building a commercial entity in each preset area. Specifically, the server can obtain the number of existing businesses in the target area, and calculate the scale of the commercial entity based on the α in the first parameter that has been determined. iThe specific value of the second parameter in the preset recommendation equation is determined based on the number of existing businesses in the target area. The target area can be an area where a commercial entity has been built.

[0081] Furthermore, the server can determine the number of businesses required in each preset area based on the probability of each user visiting each preset area and the recommendation equation that determines the second parameter. The number of businesses required in each preset area can be used to represent the scale of the commercial entity required for building a commercial entity in each preset area. The greater the number of businesses required in each preset area, the larger the scale of the commercial entity required for building a commercial entity in each preset area.

[0082] S110: Recommend information based on the business entity scale information.

[0083] The server can recommend information to some entities that perform commercial entity planning based on the determined commercial entity scale information required when constructing a commercial entity in each preset area.

[0084] As can be seen from the above method, when recommending information based on commercial entity size information, this application determines the commercial entity size information by the probability of each user visiting each preset area. This fully considers the impact of pedestrian traffic on the size of the commercial entity when constructing the commercial entity. Furthermore, when determining the probability of each user visiting each preset area, this application determines it by using each user's travel directed graph. This travel directed graph can fully reflect the user's location at different times and the order in which each user arrives at each location, accurately reflecting each user's visit to each preset area.

[0085] The above are one or more implementation methods of this specification. Based on the same idea, this specification also provides corresponding information and recommended devices, such as Figure 3 shown.

[0086] Figure 3 A schematic diagram of a device recommended for providing information in this manual, including:

[0087] An acquisition module 300 is used to acquire travel data of each user;

[0088] A first determining module 302 is configured to determine the location of each user at different time periods based on the travel data;

[0089] The second determining module 304 is configured to determine a travel directed graph of each user based on the location of each user at different time periods, wherein for each user, the travel directed graph of the user is used to represent the order in which the user arrives at various locations;

[0090] The third determining module 306 is configured to determine the visit probability of each user to visit each preset area based on the travel directed graph of each user, wherein the preset area is an area where a commercial entity needs to be built;

[0091] The fourth determining module 308 is configured to determine the scale of a commercial entity required for constructing a commercial entity in each preset area based on the probability of each user visiting each preset area;

[0092] The recommendation module 310 is used to recommend information based on the business entity size information.

[0093] Optionally, the second determination module 304 is specifically used to determine, for each user, the locations arrived at by the user in a preset night time period and the locations arrived at in a preset daytime time period based on the travel data of the user; respectively construct nodes corresponding to the locations arrived at by the user in the preset night time period, and respectively construct nodes corresponding to the locations arrived at by the user in the preset daytime time period; and respectively point the nodes corresponding to the locations arrived at by the user in the night time period to the nodes corresponding to the locations arrived by the user in the daytime time period, so as to determine the travel directed graph of the user.

[0094] Optionally, the third determination module 306 is specifically used to obtain a preset distribution equation, wherein the preset distribution equation contains an unknown first parameter; for each user, based on the user's travel directed graph, determine the visit probability of the user arriving at each preset area; input the visit probability of each user arriving at each preset area into the preset distribution equation to determine the first parameter; based on the determined first parameter and the visit probability of each user arriving at each preset area, determine the visit probability of each user visiting each preset area.

[0095] Optionally, the third determination module 306 is also used to determine, for each preset area, the number of directed edges involved in the user's visit to the preset area based on the user's travel directed graph; and determine the probability of the user visiting the preset area based on the ratio of the number of directed edges involved in the user's visit to the preset area to the number of all directed edges in the user's travel directed graph.

[0096] Optionally, the fourth determination module 308 is specifically used to obtain a preset recommendation equation, wherein the preset recommendation equation contains the first parameter and an unknown second parameter; obtain the number of existing merchants in the target area, and determine the second parameter of the preset recommendation equation based on the first parameter and the number of existing merchants in the target area; determine the number of merchants required in each preset area based on the recommendation equation determined by the second parameter according to the visit probability of each user visiting each preset area; and determine the commercial entity scale information required when constructing a commercial entity in each preset area based on the determined number of merchants required in each preset area.

[0097] This specification also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 A method of providing information recommendations.

[0098] This manual also provides Figure 4 The one shown corresponds to Figure 1 Schematic diagram of the electronic equipment. Figure 4 As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The information described is the recommended method.

[0099] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0100] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using a hardware module. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0101] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0102] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0103] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0104] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0108] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0109] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0110] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0111] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0112] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0114] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0115] The foregoing is merely an example of the present invention and is not intended to limit the present invention. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for information recommendation, characterized in that: include: Obtain travel data for each user; Determining the location of each user at different time periods based on the travel data; Determine a travel directed graph for each user based on the location of each user at different time periods, wherein for each user, the travel directed graph is used to represent the order in which the user arrives at each location; Obtain a preset distribution equation, wherein the preset distribution equation contains an unknown first parameter; for each user, determine the user's access probability of arriving at each preset area based on the user's travel directed graph; input the access probability of each user arriving at each preset area into the preset distribution equation to determine the first parameter; and determine the access probability of each user visiting each preset area based on the determined first parameter and the access probability of each user arriving at each preset area; wherein, for each preset area and each user, determine the number of directed edges involved in the user's visit to the preset area based on the user's travel directed graph; and determine the user's access probability of visiting the preset area based on the ratio of the number of directed edges involved in the user's visit to the preset area to the number of all directed edges in the user's travel directed graph; the preset area is an area where a commercial entity needs to be constructed; Determining the scale of a commercial entity required for constructing a commercial entity in each preset area based on the probability of each user visiting each preset area; Information recommendation is performed based on the business entity size information.

2. The method according to claim 1, wherein Based on the location of each user at different time periods, determine each user's travel directed graph, specifically including: For each user, based on the user's travel data, determine the locations reached by the user during a preset nighttime period and the locations reached during a preset daytime period; Constructing nodes corresponding to the locations reached by the user during the preset nighttime period, and constructing nodes corresponding to the locations reached by the user during the preset daytime period; Each node corresponding to the location reached by the user during the night time period is pointed to each node corresponding to the location reached by the user during the day time period, so as to determine a travel directed graph of the user.

3. The method according to claim 1, wherein Determining the commercial entity scale information required for constructing a commercial entity in each preset area based on the visit probability of each user visiting each preset area, specifically including: Obtaining a preset recommendation equation, wherein the preset recommendation equation contains the first parameter and an unknown second parameter; Obtaining the number of existing merchants in the target area, and determining the second parameter of the preset recommendation equation based on the first parameter and the number of existing merchants in the target area; Determining the number of merchants required in each preset area based on the visit probability of each user visiting each preset area and the recommendation equation for determining the second parameter; Based on the determined number of businesses required in each preset area, the commercial entity scale information required when constructing a commercial entity in each preset area is determined.

4. An information recommendation device, characterized in that: include: Acquisition module, used to obtain travel data of each user; A first determining module is used to determine the location of each user in different time periods based on the travel data; A second determining module is configured to determine a travel directed graph of each user based on the location of each user at different time periods, wherein for each user, the travel directed graph of the user is used to represent the order in which the user arrives at each location; A third determination module is configured to obtain a preset distribution equation, wherein the preset distribution equation contains an unknown first parameter, and for each user, determine the visit probability of the user arriving at each preset area based on the user's travel directed graph, input the visit probability of each user arriving at each preset area into the preset distribution equation to determine the first parameter, and determine the visit probability of each user visiting each preset area based on the determined first parameter and the visit probability of each user arriving at each preset area, wherein, for each preset area and each user, determine the number of directed edges involved in the user's visit to the preset area based on the user's travel directed graph, and determine the visit probability of the user visiting the preset area based on the ratio of the number of directed edges involved in the user's visit to the preset area to the number of all directed edges in the user's travel directed graph, wherein the preset area is an area where a commercial entity needs to be constructed; A fourth determining module is configured to determine the scale of a commercial entity required for constructing a commercial entity in each preset area based on the probability of each user visiting each preset area; The recommendation module is used to recommend information based on the business entity scale information.

5. The device according to claim 4, characterized in that The second determining module is specifically configured to determine, for each user, based on the user's travel data, the locations reached by the user during a preset nighttime period and the locations reached during a preset daytime period; Nodes corresponding to the locations reached by the user during the preset nighttime period are respectively constructed, as are nodes corresponding to the locations reached by the user during the preset daytime period; and each node corresponding to the location reached by the user during the nighttime period is respectively pointed to each node corresponding to the location reached by the user during the daytime period, so as to determine a travel directed graph of the user.

6. The device according to claim 4, characterized in that The fourth determination module is specifically used to obtain a preset recommendation equation, wherein the preset recommendation equation contains the first parameter and an unknown second parameter; obtain the number of existing merchants in the target area, and determine the second parameter of the preset recommendation equation based on the first parameter and the number of existing merchants in the target area; determine the number of merchants required in each preset area based on the recommendation equation determined by the second parameter according to the visit probability of each user visiting each preset area; and determine the commercial entity scale information required when constructing a commercial entity in each preset area based on the determined number of merchants required in each preset area.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

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