Merchant recommendation methods, devices and computer-readable storage media

Abstract

This application provides a merchant recommendation method, apparatus, and computer-readable storage medium, relating to the field of computer technology, which can improve the accuracy of merchant recommendations based on new users. The method includes: acquiring merchant attribute information and user attribute information; the merchant attribute information includes the merchant's location information, merchant identification information, merchant category information, and merchant rating information; the user attribute information includes one or more first traffic data points corresponding to an application, the user's residential location information, and the user's workplace location information; determining a matching degree set between the merchant and the user based on the merchant attribute information and the user attribute information; the matching degree set includes one or more matching degrees; if there is a matching degree in the matching degree set greater than a first preset threshold, sending a push message to the user; the push message includes the merchant's identification information.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to merchant recommendation methods, apparatus and computer-readable storage media. Background Technology

[0002] Currently, with the increasing prevalence of computer and network applications and the growing diversity of business types across different sectors, recommending suitable merchants to users has become increasingly important.

[0003] Existing technical solutions typically recommend merchants based on historical behavioral data between users and merchants. However, existing merchant recommendation methods are more accurate when based on existing users who have interacted with merchants, but less accurate when based on new users who have not interacted with merchants. Summary of the Invention

[0004] This application provides a merchant recommendation method, apparatus, and computer-readable storage medium that can improve the accuracy of merchant recommendations based on new users.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a merchant recommendation method is provided, comprising: obtaining merchant attribute information and user attribute information; the merchant attribute information includes the merchant's location information, merchant identification information, merchant category information, and merchant rating information; the user attribute information includes one or more first traffic data points corresponding to the application, the user's residential location information, and the user's workplace location information; determining a set of matching degrees between the merchant and the user based on the merchant attribute information and the user attribute information; the set of matching degrees includes one or more matching degrees; and sending a push message to the user when there is a matching degree in the set of matching degrees greater than a first preset threshold; the push message includes the merchant's merchant identification information.

[0007] Based on this scheme, by acquiring the attribute information of merchants and users, and determining that the compatibility between the merchant and user is greater than a first preset threshold, a push message corresponding to the merchant is sent to the user. Compared with existing schemes, this application's scheme recommends merchants based on their attribute information, without requiring prior interaction between the user and the merchant, thus improving the accuracy of merchant recommendations for new users.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, determining the set of compatibility between merchants and users based on the attribute information of merchants and the attribute information of users specifically includes: determining a set of first tags for users based on one or more first traffic data of users, the first tag set including one or more first tag information; and determining the set of compatibility between merchants and users based on the attribute information of merchants, the first tag set, and the attribute information of users.

[0009] Based on this scheme, it is possible to determine the set of compatibility between merchants and users based on the merchant's attribute information and the user's attribute information.

[0010] In conjunction with the first aspect, in some embodiments of the first aspect, determining a user's first tag set based on one or more first traffic data specifically includes: performing logarithmic processing on one or more first traffic data to obtain one or more second traffic data; performing normalization processing on one or more second traffic data to obtain one or more third traffic data; using the application category information corresponding to the fourth traffic data as tag information in the first tag set; the fourth traffic data being traffic data greater than a second preset threshold among one or more third traffic data.

[0011] Based on this scheme, it is possible to determine a user's first tag set based on one or more of the user's first traffic data.

[0012] In conjunction with the first aspect, in some embodiments of the first aspect, determining the matching degree set between merchants and users based on merchant attribute information, a first tag set, and user attribute information specifically includes: performing word segmentation processing on the merchant's evaluation information in the merchant's attribute information to obtain a second tag set for the merchant; the second tag set includes one or more second tag information, and the second tag set also includes merchant category information; determining the matching degree set based on the location information in the merchant's attribute information, the second tag set, the first tag set, the user's residential location information, the user's workplace location information, and one or more first traffic data of the user corresponding to the application; the matching degree in the matching degree set satisfies the following relationship:

[0013]

[0014] Among them, R us M represents the degree of compatibility. cw F represents the word vector distance between tag information c in the first tag set and tag information w in the second tag set. cThis represents the fourth traffic data corresponding to tag information c. d1 represents the distance between the user's workplace and the merchant's location, and d2 represents the distance between the user's residence and the merchant's location.

[0015] Based on this scheme, it is possible to determine the set of compatibility between merchants and users based on the merchant's attribute information, the first tag set, and the user's attribute information.

[0016] Secondly, a merchant recommendation device is provided to implement the merchant recommendation method described in the first aspect. The merchant recommendation device includes modules, units, or means corresponding to the above method. These modules, units, or means can be implemented in hardware, software, or by hardware executing corresponding software. The hardware or software includes one or more modules or units corresponding to the above functions.

[0017] In conjunction with the second aspect, in some embodiments of the second aspect, the merchant recommendation device includes: a transceiver module and a processing module; the transceiver module is used to acquire merchant attribute information and user attribute information; the merchant attribute information includes merchant location information, merchant identification information, merchant category information, and merchant rating information, and the user attribute information includes one or more first traffic data corresponding to the application, user residential location information, and user workplace location information; the processing module is used to determine a matching degree set between the merchant and the user based on the merchant attribute information and the user attribute information; the matching degree set includes one or more matching degrees; the transceiver module is further used to send a push message to the user if there is a matching degree in the matching degree set that is greater than a first preset threshold; the push message includes the merchant identification information of the merchant.

[0018] In conjunction with the second aspect, in some embodiments of the second aspect, the processing module is specifically used for: determining a first tag set for a user based on one or more first traffic data of the user, the first tag set including one or more first tag information; and determining a set of compatibility between the merchant and the user based on the merchant's attribute information, the first tag set, and the user's attribute information.

[0019] In conjunction with the second aspect, in some embodiments of the second aspect, the processing module is further configured to determine a first tag set for a user based on one or more first traffic data, specifically including: performing logarithmic processing on one or more first traffic data to obtain one or more second traffic data; performing normalization processing on one or more second traffic data to obtain one or more third traffic data; using the category information of the application corresponding to the fourth traffic data as tag information in the first tag set; the fourth traffic data being traffic data greater than a second preset threshold among one or more third traffic data.

[0020] In conjunction with the second aspect, in some embodiments of the second aspect, the processing module is further configured to determine a matching degree set between the merchant and the user based on the merchant's attribute information, the first tag set, and the user's attribute information. Specifically, this includes: performing word segmentation on the merchant's evaluation information in the merchant's attribute information to obtain a second tag set for the merchant; the second tag set includes one or more second tag information items, and the second tag set also includes the merchant's category information; determining the matching degree set based on the location information in the merchant's attribute information, the second tag set, the first tag set, the user's residential location information, the user's workplace location information, and one or more first traffic data items corresponding to the application; the matching degree in the matching degree set satisfies the following relationship:

[0021]

[0022] Among them, R us M represents the degree of compatibility. cw F represents the word vector distance between tag information c in the first tag set and tag information w in the second tag set. c This represents the fourth traffic data corresponding to tag information c. d1 represents the distance between the user's workplace and the merchant's location, and d2 represents the distance between the user's residence and the merchant's location.

[0023] Thirdly, a merchant recommendation apparatus is provided, comprising: at least one processor and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method provided by the first aspect and any possible implementation thereof.

[0024] Fourthly, a computer-readable storage medium is provided, wherein when instructions in the computer-readable storage medium are executed by a processor of a merchant recommendation device, the merchant recommendation device is enabled to perform the method provided in the first aspect and any possible implementation thereof.

[0025] Fifthly, a computer program product containing instructions is provided that, when run on a computer, enables the computer to perform the methods provided in the first aspect and any possible implementation thereof.

[0026] In a sixth aspect, a chip system is provided, comprising: a processor and an interface circuit; the interface circuit being configured to receive a computer program or instructions and transmit them to the processor; the processor being configured to execute the computer program or instructions to cause the chip system to perform the methods provided in the first aspect and any possible embodiments thereof.

[0027] The technical effects of any one of the second to sixth aspects can be found in the technical effects of the different embodiments of the first aspect described above, and will not be repeated here. Attached Figure Description

[0028] Figure 1 This application provides an architectural diagram of a merchant recommendation system.

[0029] Figure 2 A flowchart illustrating a merchant recommendation method provided in this application;

[0030] Figure 3 A flowchart illustrating yet another merchant recommendation method provided in this application;

[0031] Figure 4 A flowchart illustrating yet another merchant recommendation method provided in this application;

[0032] Figure 5 A flowchart illustrating yet another merchant recommendation method provided in this application;

[0033] Figure 6 A schematic diagram of a merchant recommendation device provided in this application;

[0034] Figure 7 A schematic diagram of another merchant recommendation device provided in this application. Detailed Implementation

[0035] In the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0036] Furthermore, to facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0037] In this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0038] It is understood that the term "embodiment" used throughout the specification means that a specific feature, structure, or characteristic related to an embodiment is included in at least one embodiment of this application. Therefore, various embodiments throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It is understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0039] It is understood that in this application, "when," "if," and "if" all refer to the corresponding processing that will be carried out under certain objective circumstances, and are not limited to a specific time, nor do they require that there must be a judgment action when implemented, nor do they imply any other limitations.

[0040] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the apparatus given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.

[0041] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments and implementation methods of the various embodiments in this application, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the implementation methods of the various embodiments are consistent and can be mutually referenced. The technical features in different embodiments and between the implementation methods of the various embodiments can be combined according to their inherent logical relationships to form new embodiments, implementation methods, implementation methods, or implementation approaches. The following embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0042] Figure 1 This is a schematic diagram of the architecture of a merchant recommendation system provided in this application. The technical solutions of the embodiments of this application can be applied to... Figure 1 The merchant recommendation system shown is as follows: Figure 1As shown, the merchant recommendation system 10 includes a merchant recommendation device 11 and an electronic device 12.

[0043] The merchant recommendation device 11 is directly or indirectly connected to the electronic device 12. This connection can be wired or wireless, and this application embodiment does not limit this.

[0044] The merchant recommendation device 11 can be used to receive data from the electronic device 12.

[0045] Electronic device 12 can be used to send data to merchant recommendation device 11.

[0046] It should be noted that the merchant recommendation device 11 and the electronic device 12 can be independent devices or integrated into the same device; this application does not make any specific limitation in this regard.

[0047] When the merchant recommendation device 11 and the electronic device 12 are integrated into the same device, the communication method between the merchant recommendation device 11 and the electronic device 12 is the same as the communication method between the modules within the device. In this case, the communication process between the two is the same as that when the merchant recommendation device 11 and the electronic device 12 are independent of each other.

[0048] In the following specific embodiments provided in this application, the merchant recommendation device 11 and the electronic device 12 are described as being set up independently of each other.

[0049] In practical applications, the merchant recommendation method provided in this application embodiment can be applied to the merchant recommendation device 11, or to the devices included in the merchant recommendation device 11.

[0050] The merchant recommendation method provided in this application embodiment will be described below with reference to the accompanying drawings, taking the application of the merchant recommendation method to the merchant recommendation device 11 as an example.

[0051] Figure 2 A flowchart illustrating a merchant recommendation method provided in this application is shown below. Figure 2 As shown, the method includes the following steps:

[0052] S201, The merchant recommendation device obtains the attribute information of merchants and the attribute information of users.

[0053] The merchant's attribute information includes the merchant's location information, the merchant's identification information, the merchant's category information, and the merchant's rating information. The user's attribute information includes one or more of the user's first traffic data corresponding to the application, the user's residential location information, and the user's workplace location information.

[0054] It should be noted that the merchant's location information can be the merchant's latitude and longitude. For example, the merchant's location information can be (102.678285, 41.300724), or it can be (112.678285, 43.300724). This application does not impose any restrictions on this.

[0055] The merchant's identification information can be the name of the merchant. For example, the merchant's identification information can be "First Swimming Pool" or "Second Noodle Shop". This application does not restrict this.

[0056] The merchant category information can be a classification of the merchant. For example, the merchant category information can be swimming, or it can be Sichuan cuisine, or it can be film and television. This application does not impose any restrictions on this.

[0057] Merchant reviews can be user ratings of the merchant. For example, a merchant's review could be "Swimming is very comfortable" or "The braised noodles are delicious." This application does not impose any restrictions on this.

[0058] One or more first traffic data corresponding to an application can be the traffic consumed by the user in the application. For example, the user consumes 10 megabytes of traffic in the first application, or the user consumes 100 megabytes of traffic in the second application. This application does not limit this.

[0059] The user's residential location information can be the latitude and longitude of base stations accessed for more than 20 days within a consecutive 30-day period from 02:00 to 04:00. For example, the user's residential location information can be (212.678285, 53.300724), or it can be (312.678285, 47.300724). This application does not impose any restrictions on this.

[0060] The user's work location information can be the latitude and longitude of base stations accessed for more than 20 days within a consecutive 30-day period between 10:00-11:00 or 15:00-16:00. For example, the user's work location information can be (222.678285, 23.300724), or it can be (372.678285, 42.300724). This application does not impose any restrictions on this.

[0061] As one possible implementation method, combined Figure 1 The merchant recommendation device receives a message from the electronic device 12, which includes the merchant's attribute information and the user's attribute information.

[0062] As another possible implementation, for the merchant's attribute information, the merchant recommendation device constructs the request structure and request header of the merchant platform's Uniform Resource Locator (URL) connection, sends request messages to the merchant platform through a crawler program, and then receives the response messages from the merchant platform, and obtains the merchant's attribute information from the response messages through regular expressions.

[0063] For user attribute information, the merchant recommendation device obtains the application feature library and the user's call detail record (CDR) data via the S1-U interface or N3 interface. From the CDR data, it extracts the user's host, URL, and user_agent fields. Using the application identifier in the host, URL, and user_agent fields and the application feature library, it determines the application's category information. The traffic corresponding to the host, URL, and user_agent fields is used as the first traffic data for that application. The application feature library includes the correspondence between application category information and application identifiers. The merchant recommendation device then filters the CDR data via the S1-MME interface or N2 interface for base stations that have been accessed for more than 20 days out of a continuous 30-day period between 02:00 and 04:00. The latitude and longitude of these base stations are used as the user's residential location information. Similarly, it filters the CDR data for base stations that have been accessed for more than 20 days out of a continuous 30-day period between 10:00 and 11:00 or between 15:00 and 16:00. The latitude and longitude of these base stations are used as the user's work location information.

[0064] It should be noted that call detail record (CDR) data can be external data representation (XDR) CDR data.

[0065] When the call detail record (CDR) data is XDR CDR data, the merchant recommendation device outputs the original bitstream in the core network interface link to the Deep Packet Inspection (DPI) device through optical splitting, mirroring, and aggregation. The DPI device decodes, synthesizes, associates various protocols, and backfills user information to obtain XDR CDR data.

[0066] DPI is a packet-based deep inspection technology that adds application protocol identification, packet content inspection, and deep decoding to the traditional Internet Protocol (IP) packet inspection technology.

[0067] S202, The merchant recommendation device determines the set of compatibility between merchants and users based on the attribute information of merchants and users.

[0068] The fit set includes one or more fits.

[0069] As one possible implementation, the merchant recommendation device determines the user's first tag set based on the user's attribute information, and determines the matching degree set between the merchant and the user based on the first tag set, the merchant's attribute information, and the user's attribute information.

[0070] It should be noted that the specific details of this possible implementation can be found in the following sections, and will not be repeated here.

[0071] S203. If there is a matching degree greater than the first preset threshold in the matching degree set, the merchant recommendation device sends a push message to the user.

[0072] The push notifications include the merchant's identifier information.

[0073] It should be noted that the first preset threshold can be 70%, or the first preset threshold can be 80%, and this application does not limit it.

[0074] Push notifications can also include merchant offers, such as coupons.

[0075] As one possible implementation, taking a first preset threshold of 70% as an example, if the set of matching degrees includes two matching degrees, namely 60% and 80%, and 80% is greater than 70%, then the merchant recommendation device sends a push message to the user.

[0076] Based on this scheme, by acquiring the attribute information of merchants and users, and determining that the compatibility between the merchant and user is greater than a first preset threshold, a push message corresponding to the merchant is sent to the user. Compared with existing schemes, this application's scheme recommends merchants based on their attribute information, without requiring prior interaction between the user and the merchant, thus improving the accuracy of merchant recommendations for new users.

[0077] The above is a general description of the scheme of this application. The scheme of this application will be further described below with reference to the accompanying drawings.

[0078] In one design, Figure 3 A flowchart illustrating yet another merchant recommendation method provided in this application, such as... Figure 3 As shown, S202 provided in the specific implementation scheme of this application specifically includes:

[0079] S301, The merchant recommendation device determines the user's first tag set based on one or more of the user's first traffic data.

[0080] The first tag set includes one or more first tag information.

[0081] It should be noted that the first tag information can be the category information of the application. For example, the first tag information can be medical and health, or it can be sports and fitness, or it can be travel information. This application does not limit it in this way.

[0082] As one possible implementation, the merchant recommendation device uses the category information of the application corresponding to traffic data that meets preset conditions in one or more first traffic data sets as the first tag information in the first tag set.

[0083] It should be noted that the specific details of this possible implementation can be found in the following sections, and will not be repeated here.

[0084] S302, The merchant recommendation device determines the set of compatibility between merchants and users based on the merchant's attribute information, the first tag set, and the user's attribute information.

[0085] As one possible implementation, the merchant recommendation device performs word segmentation on the merchant's category information and the merchant's evaluation information in the merchant's attribute information to obtain the merchant's second tag set. Based on the merchant's attribute information, the second tag set, the first tag set, and the user's attribute information, it determines the set of matching degree between the merchant and the user.

[0086] It should be noted that the specific details of this possible implementation can be found in the following sections, and will not be repeated here.

[0087] Based on this scheme, it is possible to determine the set of compatibility between merchants and users based on the merchant's attribute information and the user's attribute information.

[0088] In one design, Figure 4 A flowchart illustrating yet another merchant recommendation method provided in this application, such as... Figure 4 As shown, S301 provided in the specific implementation scheme of this application specifically includes:

[0089] S401, The merchant recommendation device performs logarithmic processing on one or more first traffic data to obtain one or more second traffic data.

[0090] As one possible implementation, if the number of first traffic data is 2, the merchant recommendation device performs logarithmic processing on the 2 first traffic data to obtain 2 second traffic data.

[0091] It should be noted that the specific scheme for the merchant recommendation device to perform logarithmic processing on one or more first traffic data can refer to existing schemes, and will not be elaborated here.

[0092] S402, The merchant recommendation device normalizes one or more second traffic data to obtain one or more third traffic data.

[0093] As one possible implementation, if there are two second traffic data points, the merchant recommendation device will normalize the two second traffic data points to obtain two third traffic data points.

[0094] It should be noted that the specific scheme for the merchant recommendation device to normalize one or more second traffic data can refer to existing schemes, and will not be elaborated here.

[0095] For example, Table 1 below is a schematic table of third traffic data provided in this application.

[0096] Table 1: Schematic Table of Third Flow Data

[0097]

[0098] As shown in Table 1, Table 1 includes third-party traffic data for 9 users. Taking user 1 as an example, user 1's third-party traffic data corresponding to applications with the category information of medical and health is 0.875524, the third-party traffic data corresponding to applications with the category information of video is 0.809964, and the third-party traffic data corresponding to applications with the category information of sports and fitness, college students, women, marriage and dating, travel information, parenting, games, and finance is 0.

[0099] S403, The merchant recommendation device uses the category information of the application corresponding to the fourth traffic data as the tag information in the first tag set.

[0100] The fourth traffic data is traffic data that is greater than the second preset threshold from one or more third traffic data.

[0101] It should be noted that the second preset threshold can be 0.7, or the second preset threshold can also be 0.8, and this application does not limit it.

[0102] As one possible implementation, referring to Table 1, taking user 1 as an example, if the second preset threshold is 0.8, the merchant recommendation device will take the third traffic data corresponding to the application with category information of medical and health and the third traffic data corresponding to the application with category information of video as the fourth traffic data respectively, and take medical and health and video as the tag information in the first tag set.

[0103] Based on this scheme, it is possible to determine a user's first tag set based on one or more of the user's first traffic data.

[0104] In one design, Figure 5 A flowchart illustrating yet another merchant recommendation method provided in this application, such as... Figure 5 As shown, S302 provided in the specific implementation scheme of this application specifically includes:

[0105] S501, The merchant recommendation device performs word segmentation on the merchant's evaluation information in the merchant's attribute information to obtain the merchant's second tag set.

[0106] The second tag set includes one or more second tag information, and also includes merchant category information.

[0107] As one possible implementation, the merchant recommendation device extracts feature words from the merchant's review information based on the jieba toolkit, performs word segmentation on the feature words to obtain multiple word segmentation information, takes the noun with the most frequency in the multiple word segmentation information as the second label in the second label set, and takes the merchant's category information as the second label in the second label set to obtain the second label set.

[0108] S502, the merchant recommendation device determines a matching set based on the merchant's attribute information, location information, second tag set, first tag set, user's residential location information, user's workplace location information, and one or more first traffic data of the user corresponding to the application.

[0109] Among them, the location information, second tag set, first tag set, user's residential location information, user's workplace location information, one or more of the user's first traffic data corresponding to the application, and the matching degree in the matching degree set of the merchant's attribute information satisfy the following relationship:

[0110]

[0111] Among them, R us M represents the degree of compatibility. cw F represents the word vector distance between tag information c in the first tag set and tag information w in the second tag set. c This represents the fourth traffic data corresponding to tag information c. d1 represents the distance between the user's workplace and the merchant's location, and d2 represents the distance between the user's residence and the merchant's location.

[0112] It should be noted that the tag information c can be any tag information in the first tag set, and the tag information w can be any tag information in the second tag set.

[0113] The merchant recommendation device needs to determine the word vector distance between each tag information in the first tag set and each tag information in the second tag set. For example, if the first tag set includes 2 tag information and the second tag set includes 2 tag information, the merchant recommendation device determines 4 word vector distances. Furthermore, the merchant recommendation device determines 4 fits in the fit set.

[0114] For M cw The merchant recommendation device vectorizes the tag information c and tag information w according to the word2Vec model to obtain vector c and vector w, and then determines the word vector distance between vector c and vector w using the cosine distance formula.

[0115] The Word2Vec model is based on deep learning and represents a single word as a point in a high-dimensional vector space by training on a text dataset. The Word2Vec model mainly includes two training modes: skip-gram and continuous bag-of-words (CBOW). Skip-gram predicts the context using the current word, while CBOW predicts the current value using the context. To improve training speed, two common acceleration methods are negative sampling and hierarchical softmax. Negative sampling directly reduces negative samples through sampling, while hierarchical softmax reduces the complexity from O(n) to O(log n). This invention prioritizes Skip-gram with Negative Sampling (SGNS) for word vector modeling and uses Baidu Encyclopedia data for training the Word2Vec model.

[0116] Based on this scheme, it is possible to determine the set of compatibility between merchants and users based on the merchant's attribute information, the first tag set, and the user's attribute information.

[0117] In one design, after S203, the merchant recommendation device can also acquire interaction messages between the user and the merchant, and optimize the first tag information based on the interaction messages, which are the user's consumption behavior data at the merchant.

[0118] It should be noted that the specific scheme for optimizing the first tag information based on the interactive message in this design method can refer to existing schemes, and this application will not elaborate on it further.

[0119] Based on this solution, the accuracy of merchant recommendations can be improved by optimizing the first tag information according to the interactive messages.

[0120] The above primarily describes the solution provided by the embodiments of this application from the perspective of the merchant recommendation device executing the merchant recommendation method. To achieve the above functions, the merchant recommendation device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] This application embodiment can divide the merchant recommendation device into functional modules according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation. Furthermore, "module" here can refer to an application-specific integrated circuit (ASIC), a circuit, a processor and memory executing one or more software or firmware programs, integrated logic circuits, and / or other devices that can provide the above functions.

[0122] When using functional module division Figure 6 A schematic diagram of a merchant recommendation device is shown. Figure 6 As shown, the merchant recommendation device 60 includes a transceiver module 601 and a processing module 602.

[0123] In some embodiments, the merchant recommendation device 60 may further include a storage module ( Figure 6 (Not shown in the image) is used to store program instructions and data.

[0124] The transceiver module 601 is used to acquire merchant attribute information and user attribute information. The merchant attribute information includes the merchant's location information, merchant identification information, merchant category information, and merchant rating information. The user attribute information includes one or more first traffic data points corresponding to the application, the user's residential location information, and the user's workplace location information. The processing module 602 is used to determine the matching degree set between the merchant and the user based on the merchant attribute information and the user attribute information. The matching degree set includes one or more matching degrees. The transceiver module 601 is also used to send a push message to the user if there is a matching degree in the matching degree set that is greater than a first preset threshold. The push message includes the merchant's merchant identification information.

[0125] Optionally, the processing module 602 is specifically used to: determine the user's first tag set based on one or more first traffic data of the user, the first tag set including one or more first tag information; and determine the matching degree set between the merchant and the user based on the merchant's attribute information, the first tag set, and the user's attribute information.

[0126] Optionally, the processing module 602 is further configured to determine a user's first tag set based on one or more first traffic data, specifically including: performing logarithmic processing on one or more first traffic data to obtain one or more second traffic data; performing normalization processing on one or more second traffic data to obtain one or more third traffic data; using the application category information corresponding to the fourth traffic data as tag information in the first tag set; the fourth traffic data is traffic data greater than a second preset threshold among one or more third traffic data.

[0127] Optionally, the processing module 602 is further configured to determine a matching degree set between the merchant and the user based on the merchant's attribute information, the first tag set, and the user's attribute information. Specifically, this includes: performing word segmentation on the merchant's evaluation information in the merchant's attribute information to obtain a second tag set for the merchant; the second tag set includes one or more second tag information items, and also includes the merchant's category information; determining the matching degree set based on the location information in the merchant's attribute information, the second tag set, the first tag set, the user's residential location information, the user's workplace location information, and one or more first traffic data items corresponding to the application; the matching degree in the matching degree set satisfies the following relationship:

[0128]

[0129] Among them, R usM represents the degree of compatibility. cw F represents the word vector distance between tag information c in the first tag set and tag information w in the second tag set. c This represents the fourth traffic data corresponding to tag information c. d1 represents the distance between the user's workplace and the merchant's location, and d2 represents the distance between the user's residence and the merchant's location.

[0130] All relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0131] When the functions of the above modules are implemented in hardware... Figure 7 A schematic diagram of a merchant recommendation device is shown. Figure 7 As shown, the merchant recommendation device 70 includes a processor 701, a memory 702, and a bus 703. The processor 701 and the memory 702 can be connected via the bus 703.

[0132] Processor 701 is the control center of merchant recommendation device 70. It can be a single processor or a collective term for multiple processing elements. For example, processor 701 can be a general-purpose central processing unit (CPU) or other general-purpose processors. Among them, the general-purpose processor can be a microprocessor or any conventional processor.

[0133] As one embodiment, processor 701 may include one or more CPUs, for example Figure 7 CPU 0 and CPU 1 are shown in the diagram.

[0134] The memory 702 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0135] As one possible implementation, the memory 702 can exist independently of the processor 701. The memory 702 can be connected to the processor 701 via a bus 703 and is used to store instructions or program code. When the processor 701 calls and executes the instructions or program code stored in the memory 702, it can implement the merchant recommendation method provided in the embodiments of this application.

[0136] In another possible implementation, the memory 702 can also be integrated with the processor 701.

[0137] Bus 703 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0138] It should be pointed out that, Figure 7 The structure shown does not constitute a limitation on the merchant recommendation device 70. Except... Figure 7 In addition to the components shown, the merchant recommendation device 70 may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0139] As an example, combined Figure 6 The transceiver module 601 and processing module 602 in the merchant recommendation device 60 perform the same functions as... Figure 7 The processor 701 in it has the same function.

[0140] Optional, such as Figure 7 As shown, the merchant recommendation device 70 provided in this application embodiment may also include a communication interface 704.

[0141] Communication interface 704 is used to connect with other devices via a communication network. This communication network can be Ethernet, a wireless access network, a wireless local area network (WLAN), etc. Communication interface 704 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0142] In one possible implementation, the communication interface 704 in the merchant recommendation device 70 provided in this application embodiment can also be integrated into the processor 701, and this application embodiment does not specifically limit this.

[0143] As a possible product form, the merchant recommendation device of this application embodiment can also be implemented using one or more field programmable gate arrays (FPGAs), programmable logic devices (PLDs), controllers, state machines, gate logic, discrete hardware components, any other suitable circuits, or any combination of circuits capable of performing the various functions described throughout this application.

[0144] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional units is used as an example. In practical applications, the above functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0145] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed, causes a computer to perform the various steps in the method flow shown in the above method embodiments.

[0146] Embodiments of this application provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the various steps in the method flow shown in the above-described method embodiments.

[0147] This application provides a chip system, including: a processor and an interface circuit; the interface circuit is used to receive computer programs or instructions and transmit them to the processor; the processor is used to execute the computer programs or instructions so that the chip system performs each step in the method flow shown in the above method embodiments.

[0148] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing, or any other form of computer-readable storage medium in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in a purpose-specific ASIC. In the embodiments of this application, the 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.

[0149] Since the merchant recommendation device, computer-readable storage medium, and computer program product provided in this embodiment can be applied to the merchant recommendation method provided in this embodiment, the technical effects they can achieve can also be referred to the above method embodiments. The embodiments of this application will not be repeated here.

[0150] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, the disclosure, and the appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0151] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A merchant recommendation method characterized by comprising: The method comprises: obtaining attribute information of a merchant and attribute information of a user; the attribute information of the merchant comprises location information of the merchant, identification information of the merchant, category information of the merchant and evaluation information of the merchant, and the attribute information of the user comprises one or more first traffic data of the user corresponding to an application program, residence location information of the user and working location information of the user; determining a first label set of the user according to the one or more first traffic data of the user, the first label set comprising one or more first label information; performing word segmentation processing on the evaluation information of the merchant in the attribute information of the merchant to obtain a second label set of the merchant; the second label set comprises one or more second label information, and the second label set further comprises the category information of the merchant; determining a fit degree set according to the location information in the attribute information of the merchant, the second label set, the first label set, the residence location information of the user, the working location information of the user, the one or more first traffic data of the user corresponding to the application program; the fit degree set comprises one or more fit degrees; the location information in the attribute information of the merchant, the second label set, the first label set, the residence location information of the user, the working location information of the user, the one or more first traffic data of the user corresponding to the application program and the fit degrees in the fit degree set satisfy the following relationship: wherein, denotes the degree of fit, denotes the word vector distance between the label information c in the first label set and the label information w in the second label set, denotes the fourth traffic data corresponding to the label information c, d1 denotes the distance between the working location of the user and the location of the merchant, and d2 denotes the distance between the residence location of the user and the location of the merchant. in the case where there is a fit degree greater than a first preset threshold in the fit degree set, sending a push message to the user; the push message comprises the merchant identification information of the merchant.

2. The method of claim 1, wherein, The determination of the first label set of the user according to the one or more first traffic data specifically comprises: performing logarithmic processing on the one or more first traffic data to obtain one or more second traffic data; performing normalization processing on the one or more second traffic data to obtain one or more third traffic data; taking category information of an application program corresponding to fourth traffic data as label information in the first label set; the fourth traffic data is traffic data greater than a second preset threshold in the one or more third traffic data.

3. A merchant recommendation device, characterized by, The merchant recommendation device comprises a transceiver module and a processing module. The transceiver module is configured to obtain attribute information of a merchant and attribute information of a user; the attribute information of the merchant comprises location information of the merchant, identification information of the merchant, category information of the merchant and evaluation information of the merchant, and the attribute information of the user comprises one or more first traffic data of the user corresponding to an application program, residence location information of the user and working location information of the user; The processing module is configured to determine a first label set of the user according to the one or more first traffic data of the user, the first label set comprising one or more first label information; The processing module is further configured to perform word segmentation processing on the evaluation information of the merchant in the attribute information of the merchant to obtain a second label set of the merchant; the second label set includes one or more second label information, and the second label set further includes category information of the merchant. The processing module is further configured to determine a fitting degree set according to the location information in the attribute information of the merchant, the second label set, the first label set, the residence location information of the user, the work location information of the user, and one or more first traffic data corresponding to the application program of the user; the fitting degree set includes one or more fitting degrees; and the location information in the attribute information of the merchant, the second label set, the first label set, the residence location information of the user, the work location information of the user, the one or more first traffic data corresponding to the application program of the user, and the fitting degrees in the fitting degree set satisfy the following relationship: wherein, denotes the fitness degree, denotes the word vector distance between the label information c in the first label set and the label information w in the second label set, denotes the fourth traffic data corresponding to the label information c, d1 denotes the distance between the working location of the user and the location of the merchant, and d2 denotes the distance between the residence location of the user and the location of the merchant. The transceiver module is further configured to send a push message to the user if there is a fitting degree greater than a first preset threshold in the fitting degree set; and the push message includes merchant identifier information of the merchant.

4. The merchant recommending apparatus according to claim 3, characterized by The processing module is further configured to determine a first label set of the user according to one or more first traffic data, and specifically includes: performing logarithmic processing on the one or more first traffic data to obtain one or more second traffic data; performing normalization processing on the one or more second traffic data to obtain one or more third traffic data; taking category information of an application program corresponding to fourth traffic data as label information in the first label set; and the fourth traffic data is traffic data greater than a second preset threshold in the one or more third traffic data.

5. A business recommendation device characterized by comprising: The merchant recommendation device includes a processor coupled with a memory, and the memory is configured to store programs or instructions, which, when executed by the processor, cause the merchant recommendation device to perform the method in any one of claims 1 to 2.

6. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer programs or instructions, when executed, cause a computer to perform the method in any one of claims 1 to 2. The computer programs or instructions, when executed, cause a computer to perform the method in any one of claims 1 to 2.

Images