Information recommendation method and device, computer equipment, storage medium and product

Abstract

The invention provides an information recommendation method and device, computer equipment, a storage medium and a product, and relates to the technical field of big data and artificial intelligence. The information recommendation method comprises the steps that in response to a first instruction input by at least one user, user portraits corresponding to all the users are generated, and the user portraits comprise first information and second information; at least one piece of category information of at least one user is determined based on the service type, each piece of category information is used for representing a user group, and the user group comprises at least one user; determining at least one piece of target category information from the at least one piece of category information; and based on first information of at least one user in at least one user group corresponding to the at least one piece of target category information, generating first recommendation information corresponding to the at least one user group, and displaying the first recommendation information to the corresponding user group.

Description

Technical Field

[0001] The present disclosure relates to the technical field of big data and artificial intelligence, and more specifically, to information recommendation methods, devices, computer equipment, storage media and products. Background Art

[0002] At present, when people handle financial business in the bank's business hall, due to the limited number of tellers, concentrated service time, a wide variety of business types, and complex processes, the branch business is generally handled one by one by calling numbers. Therefore, there are usually many customers waiting in the waiting area of ​​the branch, resulting in longer or shorter blank service time.

[0003] Based on this, in order to rationally utilize the customer resources in the waiting area and achieve precision marketing, the background server usually makes one-to-one product recommendations to customers. Although this recommendation method achieves precision marketing, each recommendation requires complex calculations and matching, which not only consumes a lot of computing resources, resulting in high computing costs and long response times; but also when processing multiple user requests, there may be insufficient concurrency capabilities, resulting in low operating efficiency; and a large amount of user data needs to be stored, which occupies a large storage space of the server. Summary of the invention

[0004] In view of this, the present disclosure provides an information recommendation method, apparatus, computer equipment, storage medium and product.

[0005] According to a first aspect of the present disclosure, there is provided an information recommendation method, comprising: generating user portraits corresponding to respective users in response to first instructions respectively input by at least one user, the user portraits comprising first information and second information, the first information being used to represent attribute information of the user, and the second information being used to represent the business type of a pending business corresponding to the user; determining at least one category information of at least one user based on the business type, each category information being used to represent a user group, and the user group comprising at least one user; determining at least one target category information from at least one category information; generating first recommendation information corresponding to at least one user group based on first information of at least one user in at least one user group corresponding to the at least one target category information, and displaying the first recommendation information to the corresponding user group.

[0006] According to an embodiment of the present disclosure, the information recommendation method also includes: matching the first recommendation information with the second recommendation information, the second recommendation information being preset recommendation information displayed in advance to the user; in response to the first recommendation information being different from the second recommendation information, determining difference information between the first recommendation information and the second recommendation information; updating the second recommendation information based on the difference information, and displaying the updated second recommendation information.

[0007] According to an embodiment of the present disclosure, the information recommendation method also includes: when the number of determined target category information is at least two, determining the recommendation priority of user groups corresponding to at least two target category information respectively; based on the recommendation priority, controlling the display parameters of the first recommended information, the display parameters including display time, display interval, display speed and display order.

[0008] According to an embodiment of the present disclosure, determining the recommendation priority of user groups corresponding to at least two target category information includes: obtaining the number of users of at least two user groups corresponding to at least two target category information; and determining the recommendation priority of user groups corresponding to at least two target category information based on the number of users of at least two user groups.

[0009] According to an embodiment of the present disclosure, the information recommendation method also includes: obtaining the crowd density of the target area; in response to the crowd density being greater than or equal to a preset threshold, displaying the first recommendation information to the corresponding user; or, in response to the crowd density being less than the preset threshold, displaying the second recommendation information to the user.

[0010] According to an embodiment of the present disclosure, obtaining the crowd density of the target area includes: obtaining an image of the target area; and identifying the image to determine the crowd density of the target area.

[0011] According to an embodiment of the present disclosure, determining at least one target category information from at least one category information includes: obtaining the number of users in a user group corresponding to each category information; and determining at least one target category information based on the number of users.

[0012] According to an embodiment of the present disclosure, based on first information of at least one user in at least one user group corresponding to at least one target category information, first recommendation information corresponding to at least one user group is generated, including: based on each target category information and the first information of at least one user in the user group corresponding to each category information, corresponding first feature information is generated, the first feature information is used to represent the label of the recommended product corresponding to each target category information; in response to matching the first feature information and the second feature information, first recommendation information corresponding to at least one user group is generated, the second feature information is used to represent the label of the product to be recommended pre-stored in the database.

[0013] The second aspect of the present disclosure provides an information recommendation device, including: a portrait module, which is used to generate user portraits corresponding to each user in response to a first instruction input by at least one user, the user portraits including first information and second information, the first information is used to represent the attribute information of the user, and the second information is used to represent the business type of the pending business corresponding to the user; a processing module, which is used to determine at least one category information of at least one user based on the business type, each category information is used to represent a user group, and the user group includes at least one user; and determine at least one target category information from the at least one category information; a recommendation information generation module, which is used to generate first recommendation information corresponding to at least one user group based on the first information of at least one user in the at least one user group corresponding to the at least one target category information, and display the first recommendation information to the corresponding user group.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram of information recommendation according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 2 A flowchart of an information recommendation method according to Embodiment 1 of the present disclosure is schematically shown;

[0020] Figure 3 A flowchart of an information recommendation method according to a second embodiment of the present disclosure is schematically shown;

[0021] Figure 4 A flowchart of an information recommendation method according to Embodiment 3 of the present disclosure is schematically shown;

[0022] Figure 5A flowchart of an information recommendation method according to a fourth embodiment of the present disclosure is schematically shown;

[0023] Figure 6 A flowchart of an information recommendation method according to a fifth embodiment of the present disclosure is schematically shown;

[0024] Figure 7 A flowchart of an information recommendation method according to a sixth embodiment of the present disclosure is schematically shown;

[0025] Figure 8 A flowchart of an information recommendation method according to Embodiment 7 of the present disclosure is schematically shown;

[0026] Fig. 9 The structure block diagram of the information recommendation device according to the embodiment of the present disclosure is schematically shown;

[0027] Fig.10 The following schematically shows a block diagram of the device structure of a processing module according to an embodiment of the present disclosure;

[0028] Fig.11 The following schematically shows a block diagram of the device structure of a recommendation information generation module according to an embodiment of the present disclosure;

[0029] Fig.12 A block diagram of an electronic device suitable for implementing an information recommendation method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0030] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known systems and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0031] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0032] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0033] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0034] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0035] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0036] Figure 1 The application scenario diagram of information recommendation according to an embodiment of the present disclosure is schematically shown.

[0037] like Figure 1 As shown, according to this embodiment 100 may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a fourth terminal device 104, a network 105, and a server 106. The network 105 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, the fourth terminal device 104, and the server 106. The network 105 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0038] The first terminal device 101 may be a ticket calling machine, and the user may generate a ticket by providing a task request through the first terminal device 101, indicating the basis for the user to queue. The user may use the first terminal device 101 to interact with the server 106 through the network 105.

[0039] The second terminal device 102 may be a camera and an infrared detection device, which is used to detect the density of human traffic in the target area. Meanwhile, the second terminal device 102 interacts with the server 106 via the network 105 .

[0040] The third terminal device 103 may be a handheld terminal, which is used to receive information sent by the server 106 and interact with the server 106 through the network 105 .

[0041] The fourth terminal device 104 may be an electronic promotional screen for displaying the generated recommendation information and interacting with the server 106 via the network 105 .

[0042] The server 106 may be a server that provides various services, such as a background management server (only as an example) that processes information input by the user based on the first terminal device 101. The background management server may analyze and process the received user request and other data, and feed back the processing results to the terminal device.

[0043] It should be noted that the information recommendation method provided in the embodiment of the present disclosure can generally be executed by the server 106. Accordingly, the information recommendation device provided in the embodiment of the present disclosure can generally be set in the server 106. The information recommendation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 106 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103, the fourth terminal device 104 and / or the server 106. Accordingly, the information recommendation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 106 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103, the fourth terminal device 104 and / or the server 105.

[0044] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0045] Figure 2 The flowchart of the information recommendation method according to the first embodiment of the present disclosure is schematically shown.

[0046] like Figure 2 As shown, the information recommendation method of this embodiment includes operations S210 to S230, and the transaction processing method can be executed according to the following operations.

[0047] In operation S210, in response to a first instruction inputted by at least one user, user portraits corresponding to respective users are generated, the user portraits including first information and second information, the first information being used to represent attribute information of the user, and the second information being used to represent a business type of a pending business corresponding to the user.

[0048] In an embodiment of the present disclosure, before obtaining the user's information, the user's consent or authorization may be obtained. For example, before operation S210, a request to obtain the user's information may be issued to the user. When the user agrees or authorizes that the user's information may be obtained, the above operation S210 is performed.

[0049] In operation 210, the attribute information refers to the user's personal information, including the user's age attribute, gender attribute, customer type such as corporate customer or individual customer, customer channel such as self-service machine or counter. The second information is used to indicate the business type of the pending business corresponding to the user, such as account business, deposit business, loan business, credit card and cross-border business.

[0050] In operation S220, at least one category information of at least one user is determined based on the service type, each category information is used to represent a user group, and the user group includes at least one user.

[0051] Classify the users into at least one category of information based on the business type of each user. For example, user A needs to apply for a loan business, user B needs to apply for a credit card business, user C needs to apply for a deposit business, user D needs to apply for a loan business, user E needs to apply for a deposit business, and user F needs to apply for a loan business. Then users AF can be divided into: the first category information indicates that the loan business includes user A, user D, and user F; the second category information indicates that the credit card business includes user B; the third category information indicates that the deposit business includes user C and user E.

[0052] In operation S230, at least one target category information is determined from the at least one category information.

[0053] Based on the above classification of user AF into first category information, second category information and third category information, for example, the first category information and the third category information may be determined as target category information according to a preset rule, or the first category information may be determined as target category information.

[0054] In operation S240, first recommendation information corresponding to at least one user group is generated based on first information of at least one user in at least one user group corresponding to at least one target category information, and the first recommendation information is displayed to the corresponding user group.

[0055] The first recommendation information may be generated information such as deposit products and loan products.

[0056] On the basis of determining the target category information, for example, determining the first category information as the target category information, where user A, user D and user F are all young people, and the customer type is individual customers, based on the first category information being a loan business, recommendation information for small credit loan products with instant disbursement can be generated.

[0057] In a possible embodiment, if the third category information is also determined as the target category information, user C and user E are both elderly people, and the third category information is a deposit business, recommendation information of low-risk deposit products and playing anti-fraud promotional videos can be generated for the third category information.

[0058] An embodiment of the present disclosure provides an information recommendation method, which divides users into groups according to business types, generates recommendation information for each group, rather than calculating it for each user individually, which can improve the efficiency of information recommendation, significantly reduce the consumption of server computing resources and improve the server operation efficiency. In addition, the target user group to be recommended is determined based on the generated user group, which can achieve large-scale coverage, and only the data of the target user group needs to be stored, which reduces the storage space of the server. At the same time, the recommendation strategy can be quickly generated based on the attributes of the group, which reduces the response time. In addition, the generated recommendation information can be displayed to the user in a visual way, which can improve the user experience.

[0059] Figure 3 The flowchart of the information recommendation method according to the second embodiment of the present disclosure is schematically shown.

[0060] like Figure 3 As shown, the information recommendation method of this embodiment includes operations S310 to S330, and the transaction processing method can be executed according to the following operations.

[0061] In operation S310, first recommendation information is matched with second recommendation information, where the second recommendation information is preset recommendation information that is pre-displayed to a user.

[0062] The pre-displayed preset recommendation information may be recommendation information generated based on historical data or preset rules. For example, if multiple users handle deposit business in a certain period of time, the pre-set recommendation information may be a time deposit product, and the pre-set time deposit products will be displayed in a rolling manner.

[0063] By comparing the first recommendation information with the second recommendation information, the difference between the first recommendation information and the second recommendation information can be found.

[0064] In operation S320, in response to the first recommendation information being different from the second recommendation information, difference information between the first recommendation information and the second recommendation information is determined.

[0065] In some possible embodiments, the difference information between the first recommendation information and the second recommendation information may be: the types of recommended products of the first recommendation information and the second recommendation information are different, that is, the first recommendation information and the second recommendation information have nothing in common, such as the first recommendation information includes product a and product b, and the second recommendation information includes product c and product d, where a, b, c and d are different from each other.

[0066] It is also possible that the first recommendation information and the second recommendation information contain some of the same product categories, such as the first recommendation information contains product a and product b, and the second recommendation information includes product a and product c.

[0067] It is also possible that both the first recommendation information and the second recommendation information include product a and product b, but in the first recommendation information, the display order of product a and product b is a, b, and the display parameters, such as display time, are t1 and t2, respectively; in the second recommendation information, the display order of product a and product b is b, a. At this time, the display order of product a and product b is different.

[0068] Alternatively, both the first recommendation information and the second recommendation information include product a and product b, and the display order of the two products a and product b in the first recommendation information and the second recommendation information is the same, but the display parameters are different, such as display time.

[0069] In operation S330, the second recommendation information is updated based on the difference information, and the updated second recommendation information is displayed.

[0070] In some possible embodiments, for example, the types of recommended products in the first recommendation information and the second recommendation information are different, such as the first recommendation information includes product a and product b, and the second recommendation information includes product c and product d, then the difference information is product a and product b and the display parameters of product a and product b. In this case, it is necessary to update all the second recommendation information to the first recommendation information.

[0071] It may be that the first recommendation information and the second recommendation information contain some of the same product categories, such as the first recommendation information contains product a and product b, and the second recommendation information includes product a and product c, then the difference information is product b, and the display parameters of product a and product b. In this case, only product b and the display parameters of product a and product b need to be updated, reducing the information update of product a.

[0072] Alternatively, the first recommendation information and the second recommendation information both include product a and product b, but the display parameters of product a and product b in the first recommendation information and the second recommendation information are different. In this case, only the display parameters of product a and product b need to be updated.

[0073] In the implementation of the present disclosure, the difference information determined in operation S320 is used to update the difference information determined with the first recommended information on the basis of the second recommended information to determine the updated second recommended information. Through this information recommendation method, only part of the recommended information needs to be updated, which reduces the amount of data processing, improves the response speed of information recommendation, and reduces the waste of resources, avoiding the waste of resources caused by updating all the recommended information when there is partial or complete consistency.

[0074] Figure 4 The flowchart of the information recommendation method according to the third embodiment of the present disclosure is schematically shown.

[0075] like Figure 4 As shown, the information recommendation method of this embodiment includes operations S410 to S420, and the transaction processing method can be executed according to the following operations.

[0076] The information recommendation method also includes:

[0077] In operation S410 , when the number of the determined target category information is at least two, the recommendation priorities of the user groups respectively corresponding to the at least two target category information are determined.

[0078] In operation S420, display parameters of the first recommendation information are controlled based on the recommendation priority, the display parameters including display time, display interval, display speed, and display order.

[0079] In an embodiment of the present disclosure, based on the determined at least two target category information, the priority of the corresponding user group is determined according to the determined category information.

[0080] The priority may be the order of recommendation. In some possible embodiments, the first category information and the third category information in the above operation S230 may be determined as target category information, wherein the first category information indicates that the loan business includes user A, user D, and user F. The third category information indicates that the deposit business includes user C and user E. The priority of the corresponding customer group is determined based on the two target category information, for example, the recommendation information generated for the customer group corresponding to the first category information with the priority is recommended first.

[0081] According to the determined recommendation priority, the display parameters of the first recommended information are controlled to ensure that the high-priority information is displayed more prominently, thereby improving the response speed of information recommendation and user experience. For example, the high-priority information is displayed for a longer time, with a shorter display interval, at a faster display speed, and at a higher display order.

[0082] Figure 5 The flowchart of the information recommendation method according to the fourth embodiment of the present disclosure is schematically shown.

[0083] like Figure 5 As shown, the information recommendation method of this embodiment includes operations S510 to S520, and the transaction processing method can be executed according to the following operations.

[0084] According to an embodiment of the present disclosure, determining the recommendation priorities of user groups corresponding to at least two target category information respectively includes:

[0085] In operation S510, the number of users of at least two user groups corresponding to at least two target category information is obtained.

[0086] In operation S520 , recommendation priorities of user groups corresponding to at least two pieces of target category information are determined based on the number of users of the at least two user groups.

[0087] In an embodiment of the present disclosure, based on operation S410, the target category information is determined, and the first category information indicates that the loan business includes user A, user D, and user F. The third category information indicates that the deposit business includes user C and user E. The numbers of the corresponding two user groups are 3 and 2, respectively. Therefore, it can be determined that the number of customer groups corresponding to the first category information is greater than the number of customer groups corresponding to the third category information. Furthermore, it can be determined that the recommendation information generated by the first category information is displayed to the corresponding customer first, and the recommendation information generated by the third category information is displayed to the corresponding customer later. According to the number of user groups, the recommendation priority can be determined simply and efficiently.

[0088] Figure 6 The flowchart of the information recommendation method according to the fifth embodiment of the present disclosure is schematically shown.

[0089] like Figure 6 As shown, the information recommendation method of this embodiment includes operations S610 to S620, and the transaction processing method can be executed according to the following operations.

[0090] According to an embodiment of the present disclosure, the information recommendation method further includes:

[0091] In operation S610, the crowd density of the target area is obtained.

[0092] In operation S620, in response to the crowd density being greater than or equal to a preset threshold, the first recommendation information is displayed to the corresponding user; or, in response to the crowd density being less than the preset threshold, the second recommendation information is displayed to the user.

[0093] In an embodiment of the present disclosure, the crowd density in the target area can be obtained according to a preset rule, wherein the preset rule can be a set interval time T. Every time the fixed interval time T is reached, the detection of the crowd density in the target area is triggered to obtain the crowd density in the target area, thereby avoiding real-time trigger response and reducing the waste of resources caused by real-time response.

[0094] When the crowd density exceeds the set threshold, the determination of the target category information is triggered, and the first recommended information is generated based on the target category information, and the generated first recommended information is displayed to the user. When the crowd density is less than the preset threshold, the determination of the target category information is not triggered, and the second recommended information is displayed to the user, thereby avoiding the waste of resources caused by generating recommended information when the crowd density in the target area is low.

[0095] According to an embodiment of the present disclosure, obtaining the crowd density of the target area includes: obtaining an image of the target area; and identifying the image to determine the crowd density of the target area.

[0096] In the embodiments of the present disclosure, the image of the target area can be captured by a camera, such as the waiting area of ​​a bank branch, and the number of people in the image can be automatically analyzed through image recognition technology to determine the density of human traffic. Through automated human traffic density recognition, the reliance on manpower can be reduced and operating costs can be reduced.

[0097] Figure 7 The flowchart of the information recommendation method according to the sixth embodiment of the present disclosure is schematically shown.

[0098] like Figure 7 As shown, the information recommendation method of this embodiment includes operations S710 to S720, and the transaction processing method can be executed according to the following operations.

[0099] According to an embodiment of the present disclosure, determining at least one target category information from at least one category information includes:

[0100] In operation S710, the number of users in the user group corresponding to each category information is obtained.

[0101] In operation S720, at least one target category information is determined based on the number of users.

[0102] In an embodiment of the present disclosure, the various types of information determined based on operation S230 are as follows:

[0103] The first category of information indicates that the loan business includes user A, user D, and user F; the second category of information indicates that the credit card business includes user B; the third category of information indicates that the deposit business includes user C and user E. The number of users in the first category of information is 3, the number of users in the second category of information is 1, and the number of users in the third category of information is 2.

[0104] According to the determined number of users, the information is sorted by size, and arranged from large to small as: first category information, third category information, and second category information. According to this queue, the target category information is determined, for example, the target category information is the first category information. This method of determining the target category information is simple to operate, avoids a large amount of data calculation, and can quickly and efficiently determine the target category information.

[0105] Figure 8 The flowchart of the information recommendation method according to the seventh embodiment of the present disclosure is schematically shown.

[0106] like Figure 8 As shown, the information recommendation method of this embodiment includes operations S810 to S820, and the transaction processing method can be executed according to the following operations.

[0107] According to an embodiment of the present disclosure, based on first information of at least one user in at least one user group corresponding to at least one target category information, generating first recommendation information corresponding to at least one user group includes:

[0108] In operation S810, corresponding first feature information is generated based on each target category information and first information of at least one user in a user group corresponding to each category information, where the first feature information is used to represent a label of a recommended product corresponding to each target category information.

[0109] In operation S820, in response to the first feature information and the second feature information matching, first recommendation information corresponding to at least one user group is generated, and the second feature information is used to represent a label of the to-be-recommended product pre-stored in the database.

[0110] In some possible embodiments, for example, the target category information is the first category information is loan business, and the user corresponding to the first category information is young people, and feature information is generated based on the loan business and young people. This feature information indicates recommendations that meet the loan product needs of young people.

[0111] The pre-stored recommended products include products corresponding to various business types, which may be small credit loan products, high-yield time deposit products, credit card services and high-yield financial products, etc., and each product is provided with a corresponding label. By matching the first feature information with the labels of the recommended products pre-stored in the database, it can be determined that the generated recommendation information is a small credit loan product.

[0112] In this embodiment, the first characteristic information is generated based on the user group, and the first characteristic information is matched with the second characteristic information, which reduces the amount of data calculation for existing one-to-one recommendations, improves the response speed, and also improves the utilization rate of resources.

[0113] Based on the above information recommendation method, the second aspect of the present disclosure provides an information recommendation device. Fig. 9 The device is described in detail.

[0114] Fig. 9 The structural block diagram of the information recommendation device according to an embodiment of the present disclosure is schematically shown.

[0115] like Fig. 9 As shown, the device of this embodiment includes: a portrait module 910, a processing module 920 and a recommendation information generation module 930.

[0116] The portrait module 910 is used to generate user portraits corresponding to each user in response to a first instruction input by at least one user, and the user portraits include first information and second information. The first information is used to represent the attribute information of the user, and the second information is used to represent the business type of the pending business corresponding to the user. In one embodiment, the portrait module 910 can be used to execute the operation S210 described above, which will not be repeated here.

[0117] The processing module 920 is used to determine at least one category information of at least one user based on the business type, each category information is used to represent a user group, and the user group includes at least one user; and determine at least one target category information from the at least one category information; in one embodiment, the processing module 910 can be used to perform the operations S220 and S230 described above, which will not be repeated here.

[0118] The recommendation information generation module 930 is used to generate first recommendation information corresponding to at least one user group based on the first information of at least one user in at least one user group corresponding to at least one target category information, and display the first recommendation information to the corresponding user group. In one embodiment, the recommendation information generation module 930 can be used to perform the operation S240 described above, which will not be repeated here.

[0119] According to an embodiment of the present disclosure, the device further includes:

[0120] The difference module 940 is used to match the first recommendation information with the second recommendation information, where the second recommendation information is preset recommendation information displayed to the user in advance; in response to the first recommendation information being different from the second recommendation information, determine the difference information between the first recommendation information and the second recommendation information. In one embodiment, the difference module 940 can be used to perform the operations S310 and S320 described above, which will not be described in detail here.

[0121] The updating module 950 is used to update the second recommendation information based on the difference information and display the updated second recommendation information. In one embodiment, the updating module 950 can be used to perform the operation S330 described above, which will not be described in detail here.

[0122] Fig.10 The device structure block diagram of the processing module according to an embodiment of the present disclosure is schematically shown.

[0123] like Fig.10 As shown, according to an embodiment of the present disclosure, the processing module 920 includes:

[0124] The first processing unit 921 is used to obtain the number of users in the user group corresponding to each category information. In one embodiment, the first processing unit 921 can be used to perform the operation S710 described above, which will not be described in detail here.

[0125] The second processing unit 922 is used to determine at least one target category information based on the number of users. In one embodiment, the second processing unit 922 can be used to perform the operation S720 described above, which will not be described in detail here.

[0126] The third processing unit 923 is used to determine the recommendation priorities of the user groups corresponding to the at least two target category information when the number of the determined target category information is at least two. In one embodiment, the third processing unit 923 can be used to perform the operation S410 described above, which will not be described in detail here.

[0127] The fourth processing unit 924 is used to control the display parameters of the first recommendation information based on the recommendation priority, the display parameters including display time, display interval, display speed and display order. In one embodiment, the fourth processing unit 924 can be used to perform the operation S420 described above, which will not be repeated here.

[0128] Fig.11 The following schematically shows a block diagram of the device structure of a recommendation information generation module according to an embodiment of the present disclosure.

[0129] like Fig.11 As shown, according to an embodiment of the present disclosure, the information recommendation generation module 930 further includes:

[0130] The first generating unit 931 is used to generate corresponding first feature information based on each target category information and the first information of at least one user in the user group corresponding to each category information, where the first feature information is used to represent a label of a recommended product corresponding to each target category information. In one embodiment, the first generating unit 931 can be used to perform the operation S810 described above, which will not be described in detail here.

[0131] The second generating unit 932 is used to generate first recommendation information corresponding to at least one user group in response to the matching of the first characteristic information and the second characteristic information, wherein the second characteristic information is used to represent a label of the product to be recommended pre-stored in the database. In one embodiment, the second generating unit 932 can be used to perform the operation S820 described above, which will not be described in detail here.

[0132] According to an embodiment of the present disclosure, the device further includes:

[0133] The density acquisition module 960 is used to acquire the density of the flow of people in the target area. In one embodiment, the density detection module 960 can be used to perform the operation S610 described above, which will not be described in detail here.

[0134] The trigger module 970 is used to display the first recommendation information to the corresponding user in response to the crowd density being greater than or equal to the preset threshold; or to display the second recommendation information to the user in response to the crowd density being less than the preset threshold. In one embodiment, the trigger module unit 970 can be used to perform the operation S620 described above, which will not be described in detail here.

[0135] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0136] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0137] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0138] According to an embodiment of the present disclosure, any multiple modules among the portrait module 910, the processing module 920, the recommendation information generation module 930, the difference module 940, the update module 950, the density acquisition module 960 and the trigger module 970 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the portrait module 910, the processing module 920, the recommendation information generation module 930, the difference module 940, the update module 950, the density detection module 960 and the trigger module 970 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the portrait module 910, the processing module 920, the recommendation information generation module 930, the difference module 940, the update module 950, the density detection module 960 and the trigger module 970 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is executed.

[0139] Fig.12 A block diagram of an electronic device suitable for implementing an information recommendation method according to an embodiment of the present disclosure is schematically shown.

[0140] like Fig.12 As shown, the electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 to a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0141] In RAM 1003, various programs and data required for the operation of electronic device 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0142] According to an embodiment of the present disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1500, which is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the input / output (I / O) interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk, etc.; and a communication portion 1009 including a network interface card such as a LAN card, a modem, etc. The communication portion 1900 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage portion 1008 as needed.

[0143] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0144] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus, or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0145] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the information recommendation method provided by the embodiment of the present disclosure.

[0146] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1001. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0147] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0148] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0149] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0150] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0152] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An information recommendation method, characterized in that: include: In response to a first instruction respectively input by at least one user, generating a user portrait corresponding to each user respectively, the user portrait comprising first information and second information, the first information being used to represent attribute information of the user, and the second information being used to represent a business type of a pending business corresponding to the user; Determine at least one category information of the at least one user based on the service type, each category information is used to represent a user group, and the user group includes at least one user; determining at least one target category information from the at least one category information; Based on the first information of at least one user in at least one user group corresponding to the at least one target category information, first recommendation information corresponding to the at least one user group is generated, and the first recommendation information is displayed to the corresponding user group.

2. The information recommendation method according to claim 1, characterized in that: Also includes: matching the first recommendation information with second recommendation information, where the second recommendation information is preset recommendation information pre-displayed to the user; In response to the first recommendation information being different from the second recommendation information, determining difference information between the first recommendation information and the second recommendation information; The second recommended information is updated based on the difference information, and the updated second recommended information is displayed.

3. The information recommendation method according to claim 1 or 2, characterized in that: Also includes: When the number of the determined target category information is at least two, determining recommendation priorities of user groups corresponding to the at least two target category information respectively; The display parameters of the first recommendation information are controlled based on the recommendation priority, and the display parameters include display time, display interval, display speed and display order.

4. The information recommendation method according to claim 3, characterized in that: The determining of the recommendation priorities of the user groups respectively corresponding to the at least two target category information includes: Obtaining the number of users of the at least two user groups corresponding to the at least two target category information; The recommendation priorities of the user groups respectively corresponding to the at least two target category information are determined based on the number of users of the at least two user groups.

5. The information recommendation method according to claim 2, characterized in that: Also includes: Get the crowd density of the target area; In response to the crowd density being greater than or equal to a preset threshold, the first recommendation information is displayed to the corresponding user; or, in response to the crowd density being less than a preset threshold, the second recommendation information is displayed to the user.

6. The information recommendation method according to claim 5, characterized in that: The obtaining of the crowd density of the target area includes: Acquire an image of the target area; The image is recognized to determine the crowd density in the target area.

7. The information recommendation method according to claim 1, characterized in that: The determining at least one target category information from the at least one category information comprises: Obtain the number of users in the user group corresponding to each category of information; The at least one target category information is determined based on the number of users.

8. The information recommendation method according to claim 1, characterized in that: The generating, based on the first information of at least one user in at least one user group corresponding to the at least one target category information, first recommendation information corresponding to the at least one user group includes: Based on each of the target category information and the first information of at least one user in the user group corresponding to each of the category information, generating corresponding first feature information, wherein the first feature information is used to indicate a label of a recommended product corresponding to each of the target category information; In response to the first characteristic information and the second characteristic information matching, first recommendation information corresponding to the at least one user group is generated, and the second characteristic information is used to represent a label of the to-be-recommended product pre-stored in the database.

9. An information recommendation device, comprising: A portrait module, configured to generate user portraits corresponding to respective users in response to first instructions respectively input by at least one user, wherein the user portraits include first information and second information, wherein the first information is used to represent attribute information of the user, and the second information is used to represent a business type of a pending business corresponding to the user; A processing module, configured to determine at least one category information of the at least one user based on the service type, each category information is used to represent a user group, and the user group includes at least one user; and determining at least one target category information from the at least one category information; The recommendation information generation module is used to generate first recommendation information corresponding to at least one user group based on first information of at least one user in at least one user group corresponding to the at least one target category information, and display the first recommendation information to the corresponding user group.

10. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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