Interactive interface processing method, device and equipment based on user behaviors

Abstract

The invention provides an interactive interface processing method based on user behaviors. The interactive interface processing method can be applied to the technical field of artificial intelligence and big data. The user behavior-based interactive interface processing method comprises the following steps: acquiring first information of a target user side; acquiring a pre-established user data model based on the information of the plurality of other users; performing classification processing on the first information by utilizing the user data model to obtain a classification result of the first information corresponding to the user data model; querying the user data model by using the classification result to obtain first prediction information corresponding to the first information; and sending the first prediction information to the target user side. The invention further provides an interactive interface processing device and equipment based on the user behaviors.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence and big data technology, and more specifically to a method, apparatus, and device for processing an interactive interface based on user behavior. Background Art

[0002] The description in this section merely provides background information related to the present disclosure and may not constitute prior art.

[0003] With traditional banking products, users’ financial needs are mostly initiated proactively, and they need to go through a series of operations to select the business they want to handle. This results in low user operation efficiency and often causes users to face the problem of not being able to find the corresponding operation interface and window during use, affecting the user operation experience.

[0004] It should be noted that the above technical background is merely provided to provide a clear and complete description of the technical solutions of the present invention and to facilitate understanding by those skilled in the art. Simply because these solutions are described in the technical background section of the present invention, it should not be assumed that the above technical solutions are well known to those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present disclosure provides a method, device and equipment for processing an interactive interface based on user behavior, which solves the problem that existing users have too many steps and low operating efficiency when operating financial services.

[0006] According to a first aspect of the present disclosure, a method for processing an interactive interface based on user behavior is provided, which is used on a system side. The method includes:

[0007] Obtaining first information of a target user terminal;

[0008] Obtain a pre-built user data model based on information of multiple other users;

[0009] Classifying the first information using the user data model to obtain a classification result corresponding to the first information and the user data model;

[0010] querying the user data model using the classification result to obtain first prediction information corresponding to the first information;

[0011] The first prediction information is sent to the target user terminal.

[0012] According to an embodiment of the present disclosure, the user data model includes multiple central cluster user information and behavioral feature data corresponding to each central cluster user information.

[0013] According to an embodiment of the present disclosure, the plurality of central cluster user information is obtained by clustering the user information of the plurality of other users;

[0014] The behavior feature data corresponding to each of the central cluster user information is obtained by statistically processing the behavior data corresponding to the user information of each of the other users within the range of the central cluster user information.

[0015] According to an embodiment of the present disclosure, the classifying the first information using the user data model to obtain a classification result corresponding to the first information and the user data model includes:

[0016] Performing a similarity comparison on the first information using the plurality of central cluster user information of the user data model, and determining the first central cluster user information having the highest similarity to the first information;

[0017] The classification result is obtained according to the first central cluster user information.

[0018] According to an embodiment of the present disclosure, querying the user data model using the classification result to obtain first prediction information corresponding to the first information includes:

[0019] querying the user data model using the first central cluster user information to obtain first behavior feature data corresponding to the first central cluster user information;

[0020] Hotspot data is extracted using the first behavior feature data to obtain the first prediction information.

[0021] According to an embodiment of the present disclosure, the first central cluster user information is the central cluster user information having the highest similarity to the first information;

[0022] The first behavior feature data is the behavior feature data corresponding to the first center cluster user information.

[0023] According to an embodiment of the present disclosure, the behavior feature data includes multiple time features and transaction types corresponding to each of the time features. The first information includes time information.

[0024] The step of extracting hotspot data using the first behavior feature data to obtain the first prediction information includes:

[0025] Comparing the time information of the first information with the multiple time features of the first behavior feature data to obtain multiple first time features that belong to the same time period as the time information of the first information, and obtaining a first transaction type corresponding to each of the first time features;

[0026] Based on the usage frequencies of the plurality of first transaction types, hot spot data are extracted for the plurality of first transaction types to obtain the first prediction information.

[0027] According to an embodiment of the present disclosure, the first time feature is the time feature that belongs to the same period as the time information of the first information;

[0028] The first transaction type is the transaction type corresponding to the first time feature.

[0029] According to an embodiment of the present disclosure, extracting hotspot data of the plurality of first transaction types based on the usage frequencies of the plurality of first transaction types to obtain the first prediction information includes:

[0030] Based on the usage frequencies of the plurality of first transaction types, extracting N first transaction types with the highest usage frequencies to obtain first display information, where N is a positive integer greater than 1;

[0031] Extracting M first transaction types with the highest usage frequencies based on the usage frequencies of the plurality of first transaction types to obtain first push information, where M is a positive integer greater than or equal to 1;

[0032] The first prediction information is obtained according to the first display information and the first push information.

[0033] According to an embodiment of the present disclosure, the method further includes: acquiring first selection information of the target user terminal;

[0034] Obtain a target user data model pre-established based on the target user's behavioral characteristic data;

[0035] querying the target user data model using the first selection information to obtain first pre-filled information corresponding to the first selection information;

[0036] The first pre-filled information is sent to the target user terminal.

[0037] According to an embodiment of the present disclosure, the target user data model is obtained by statistically processing the behavioral characteristic data of the target user.

[0038] According to an embodiment of the present disclosure, the behavioral characteristic data of the target user includes multiple transaction type characteristics and multiple transaction parameters corresponding to each of the transaction type characteristics, and the first selection information includes transaction type information.

[0039] According to an embodiment of the present disclosure, querying the target user data model using the first selection information to obtain first pre-filled information corresponding to the first selection information includes:

[0040] querying the target user data model using the transaction type information of the first selection information to obtain a plurality of first transaction type features identical to the transaction type information, and obtaining a first transaction parameter corresponding to each of the first transaction type features;

[0041] Based on the usage frequencies of the plurality of first transaction parameters, hot data extraction is performed on the plurality of first transaction parameters to obtain the first pre-filled information.

[0042] According to an embodiment of the present disclosure,

[0043] The first transaction type feature is the same as the transaction type information of the first selection information;

[0044] The first transaction parameter is the transaction parameter corresponding to the first transaction type feature.

[0045] According to an embodiment of the present disclosure, the step of extracting hotspot data from the plurality of first transaction parameters based on the usage frequencies of the plurality of first transaction parameters to obtain the first pre-filled information includes:

[0046] Based on the usage frequencies of the plurality of first transaction parameters, the first transaction parameter with the highest usage frequency is extracted to obtain the first pre-filled information.

[0047] According to an embodiment of the present disclosure, the behavior feature data corresponding to each of the central cluster user information is obtained by statistically processing the behavior data corresponding to the user information of each of the other users within the range of each of the central cluster user information, and the acquisition method includes:

[0048] classifying the user information of the plurality of other users based on the ranges divided by the user information of each central cluster to obtain first user information of the plurality of other users within the ranges of the user information of each central cluster;

[0049] Acquire, based on the plurality of first user information, a plurality of first behavior data corresponding to each piece of the first user information;

[0050] The plurality of first behavior data are used to perform data sorting to obtain the behavior feature data corresponding to the center cluster user information.

[0051] According to an embodiment of the present disclosure, the first user information is the user information of the other users within the user information range of each central cluster;

[0052] The first behavior data is the behavior feature data corresponding to each of the first user information.

[0053] According to an embodiment of the present disclosure, the behavior data includes time data and transaction type data corresponding to the time data.

[0054] The step of using the plurality of the first behavior data to perform data sorting to obtain the behavior feature data corresponding to the center cluster user information includes:

[0055] Based on the divided multiple time periods, respectively counting the time data of the multiple first behavior data in each time period, and obtaining the transaction type data corresponding to each of the time data in each time period;

[0056] Based on the usage frequency of the plurality of transaction type data in each time period, data statistics are performed on the same transaction type data in the plurality of transaction type data in the form of cumulative counts to obtain the behavior feature data corresponding to each central cluster user information.

[0057] According to an embodiment of the present disclosure, the target user data model is obtained by statistically processing the target user's behavioral feature data, and the obtaining method includes:

[0058] Acquire multiple transaction type characteristics of the target user and multiple transaction parameters corresponding to each of the transaction type characteristics;

[0059] Based on the usage frequencies of the plurality of transaction parameters of each transaction type, data statistics are performed on the same transaction parameters in each transaction type in the form of cumulative counts to obtain the target user data model.

[0060] According to a second aspect of the present disclosure, a method for processing an interactive interface based on user behavior is provided, for use on a target user terminal, the method comprising:

[0061] Sending first information to a system end, the first information being used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model, the user data model being pre-established based on information of multiple other users, the classification result being used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information, the first prediction information being used to trigger the system end to send the first prediction information to the target user end;

[0062] Receive the first prediction information sent by the system end.

[0063] According to an embodiment of the present disclosure, the method further includes:

[0064] Sending first selection information to the system end, the first selection information being used to trigger the system end to query a target user data model to obtain first pre-filled information corresponding to the first selection information, wherein the target user data model is pre-established based on behavioral characteristic data of the target user, and the first pre-filled information is used to trigger the system end to send the first pre-filled information to the target user end;

[0065] Receive the first pre-filled information sent by the system end.

[0066] A third aspect of the present disclosure provides a user behavior-based interactive interface processing device, which is used on a system side. The device includes:

[0067] A first acquisition module, configured to acquire first information of a target user terminal;

[0068] A second acquisition module is used to acquire a user data model pre-established based on information of multiple other users;

[0069] as well as

[0070] a first processing module, configured to classify the first information using the user data model to obtain a classification result corresponding to the first information and the user data model;

[0071] a second processing module, configured to query the user data model using the classification result to obtain first prediction information corresponding to the first information;

[0072] A first transmission module sends the first prediction information to the target user terminal.

[0073] According to an embodiment of the present disclosure, the device further includes:

[0074] A third acquisition module is used to obtain the first selection information of the target user terminal;

[0075] A fourth acquisition module is used to acquire a target user data model pre-established based on the target user's behavioral feature data;

[0076] as well as

[0077] a third processing module, querying the target user data model using the first selection information to obtain first pre-filled information corresponding to the first selection information;

[0078] The second transmission module sends the first pre-filled information to the target user terminal.

[0079] A fourth aspect of the present disclosure provides a user behavior-based interactive interface processing device for a target user terminal, the device comprising:

[0080] a third transmission module, configured to send first information to a system end, wherein the first information is used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model, wherein the user data model is pre-established based on information of multiple other users, and the classification result is used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information, and the first prediction information is used to trigger the system end to send the first prediction information to the target user end;

[0081] The fifth acquisition module is used to receive the first prediction information sent by the system end.

[0082] According to an embodiment of the present disclosure, the device further includes:

[0083] a fourth transmission module, configured to send first selection information to the system end, wherein the first selection information is used to trigger the system end to query a target user data model to obtain first pre-filled information corresponding to the first selection information, wherein the target user data model is pre-established based on behavioral characteristic data of the target user, and the first pre-filled information is used to trigger the system end to send the first pre-filled information to the target user end;

[0084] A sixth acquisition module is used to receive the first pre-filled information sent by the system end.

[0085] The fifth aspect of the present disclosure provides an electronic device, comprising: one or more processors; 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-mentioned user behavior-based interactive interface processing method.

[0086] The sixth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instruction stored thereon, which implements the steps of the above-mentioned user behavior-based interactive interface processing method when the above-mentioned computer program or instruction is executed by a processor.

[0087] The seventh aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the steps of the above-mentioned user behavior-based interactive interface processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The above contents 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:

[0089] Figure 1Schematically illustrates an application scenario diagram of the user behavior-based interactive interface processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

[0090] Figure 2 The flowchart of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0091] Figure 3 The flowchart of step S300 of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0092] Figure 4 The flowchart of step S400 of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0093] Figure 5 A flowchart schematically illustrates a method for acquiring behavior feature data corresponding to user information of each central cluster according to an embodiment of the present disclosure;

[0094] Figure 6 Schematically shows a flow chart of step S300A of a method for acquiring behavior feature data corresponding to user information of each central cluster according to an embodiment of the present disclosure;

[0095] Figure 7 The flowchart of step S420 of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0096] Figure 8 The flowchart of step S422 of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0097] Figure 9 A supplementary flow chart of a method for processing a user behavior-based interactive interface on a system side according to an embodiment of the present disclosure is schematically shown;

[0098] Figure 10 The flowchart of step S800 of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown;

[0099] Figure 11 The flowchart of the method for processing the interactive interface based on user behavior for the target user terminal according to an embodiment of the present disclosure is schematically shown;

[0100] Figure 12 A supplementary flow chart of a method for processing an interactive interface based on user behavior for a target user terminal according to an embodiment of the present disclosure is schematically shown;

[0101] as well as

[0102] Figure 13 The following schematically shows a structural block diagram of a user behavior-based interactive interface processing device for a system end according to an embodiment of the present disclosure;

[0103] Figure 14 The following schematically shows a structural block diagram of a user behavior-based interactive interface processing device for a target user terminal according to an embodiment of the present disclosure;

[0104] Figure 15 A block diagram of an electronic device suitable for implementing a method for processing an interactive interface based on user behavior according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0105] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, 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 well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0106] 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 presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0107] All terms used herein (including technical and scientific terms) 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.

[0108] When expressions such as “at least one of A, B, and C, etc.” are used, they should generally be interpreted in accordance with the meaning 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 systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc. When expressions such as “at least one of A, B, or C, etc.” are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art. For example, “a system having at least one of A, B, or C” should include but is not limited to systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, and C, etc.

[0109] In the technical solutions disclosed herein, 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.

[0110] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them 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 an individual'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.

[0111] The embodiments of the present disclosure provide a method for processing an interactive interface based on user behavior.

[0112] Figure 1 The application scenario diagram of the user behavior-based interactive interface processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure is schematically shown.

[0113] like Figure 1As shown, the network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or fiber optic cables.

[0114] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as financial service applications, web browser applications, email clients, social platform software, etc.

[0115] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0116] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports information browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0117] It should be noted that, in the first aspect, the user behavior-based interactive interface processing method provided in the embodiment of the present disclosure can be executed by the server 105, and in the second aspect, the user behavior-based interactive interface processing method provided in the embodiment of the present disclosure can be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, in the third aspect, the user behavior-based interactive interface processing device provided in the embodiment of the present disclosure can be set in the server 105, and in the fourth aspect, the user behavior-based interactive interface processing device provided in the embodiment of the present disclosure can be set in the first terminal device 101, the second terminal device 102, or the third terminal device 103. The user behavior-based interactive interface processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Accordingly, the user behavior-based interactive interface processing 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 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0118] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0119] The following will be based on Figure 1 The scene described by Figures 2 to 12 The user behavior-based interactive interface processing method of the disclosed embodiment is described in detail.

[0120] Figure 2 The flowchart of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0121] like Figure 2 As shown, the user behavior-based interactive interface processing method of this embodiment includes operations S100 to S500.

[0122] In operation S100, first information of a target user terminal is acquired.

[0123] In some embodiments, the first information is the record information of the user's operation behavior when operating in real time on the target user end. Exemplarily, the first information includes but is not limited to operation time, transaction type, transaction frequency, transaction amount, etc. In a specific embodiment, the first information is recorded in a matrix form after collection is completed. In other embodiments, there is no specific restriction on the recording form and type of the first information.

[0124] In operation S200 , a user data model pre-established based on information of a plurality of other users is acquired.

[0125] In this embodiment, the information of other users is also the record information of the operation behaviors of other users when they actually operate on their respective user terminals within a period of time. For example, the information of other users includes but is not limited to operation time, transaction type, transaction frequency, transaction amount, etc. In a specific embodiment, after the information of other users is collected, it is recorded in a matrix form. In other embodiments, there is no specific restriction on the recording form and type of the information of other users.

[0126] In operation S300, the first information is classified using the user data model to obtain a classification result corresponding to the first information and the user data model.

[0127] In this embodiment, the first information is compared with the user data model and the differences are judged to determine the data in the user data model that is similar to the first information, and then the classification result corresponding to the first information and the user data model is determined. This can realize the comparison of the first information that the target user is operating with the content in the user data model, which is convenient for finding the operation behavior required by the target user and also convenient for providing the target user with the link of the corresponding operation behavior.

[0128] Figure 3 The flowchart of step S300 of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0129] In a specific embodiment, the user data model includes multiple central cluster user information and behavioral characteristic data corresponding to each central cluster user information. Therefore, operation S300 may further include operations S310 to S320.

[0130] In operation S310, a similarity comparison is performed on the first information using multiple center cluster user information of the user data model to determine the first center cluster user information having the highest similarity to the first information;

[0131] In this embodiment, the first information is compared with multiple central cluster user information of the user data model and differences are determined to determine the central cluster user information to which the first information belongs, and the central cluster user information to which the first information belongs is used as the classification result.

[0132] Specifically, in this embodiment, the first information is compared with the central cluster user information for similarity. Numerical methods can be used to approximate the similarity. For example, if both the first information and the central cluster user information are in matrix form, the similarity of the two matrices can be approximated by calculating the singular value decomposition of the first information and the singular value decomposition of the central cluster user information. In this embodiment, the first central cluster user information is the central cluster user information that has the highest similarity to the first information.

[0133] In operation S320 , a classification result is obtained based on the first center cluster user information.

[0134] Specifically in this embodiment, the first center cluster user information is also the classification result; in a specific embodiment, the center cluster user information is obtained by clustering the user information of multiple other users. In other embodiments, the center cluster user information can also be obtained by using other methods to determine the center, and there is no specific limitation on this; in this embodiment, the user information of multiple other users is clustered by a clustering method, such as the K-Means clustering algorithm, the hierarchical clustering algorithm, the fuzzy C-means clustering algorithm, etc., to obtain the cluster centers corresponding to the user information of multiple other users, that is, to obtain multiple center cluster user information.

[0135] In a specific embodiment, the behavioral feature data corresponding to the user information of each central cluster is obtained by statistical processing of the behavioral data corresponding to the user information of each other user within the scope of the user information of each central cluster, wherein the behavioral data includes but is not limited to the operation time, transaction type, transaction frequency, transaction amount, etc. mentioned above. In this embodiment, after the behavioral data corresponding to the user information of each other user within the scope of the user information of each central cluster is collected, it is statistically recorded in a matrix form.

[0136] In operation S400 , the user data model is queried using the classification result to obtain first prediction information corresponding to the first information.

[0137] In this embodiment, the classification result is used to query the user data model, that is, the data in the user data model corresponding to the first information is used to query the user data model to obtain user behavior feature data corresponding to the data, that is, to obtain the first prediction information.

[0138] In a specific embodiment, the classification result is used to query the user data model, that is, the classification result is used to query multiple central cluster user information in the user data model, and the behavioral characteristic data corresponding to each central cluster user information, to find the central cluster user information corresponding to the classification result, and to query the behavioral characteristic data corresponding to the central cluster user information to obtain the first prediction information corresponding to the first information.

[0139] Figure 4 The flowchart of step S400 of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0140] In a specific embodiment, operation S400 may further include operations S410 to S420.

[0141] In operation S410 , a user data model is queried using first central cluster user information to obtain first behavior feature data corresponding to the first central cluster user information.

[0142] In this embodiment, the first central cluster user information is used to query the first behavior characteristic data corresponding to the first central cluster user information, wherein the first behavior characteristic data is the behavior characteristic data corresponding to the first central cluster user information.

[0143] In operation S420 , hotspot data is extracted using the first behavior feature data to obtain first prediction information.

[0144] In this embodiment, hot spot data extraction is performed on the first behavioral characteristic data. According to the data after statistics of multiple behavioral data of the first behavioral characteristic data, the behavioral data with higher usage frequency can be extracted as the first prediction information, so that the target user can match the behavior with higher usage frequency and perform quick operations.

[0145] In operation S500, first prediction information is sent to a target user terminal. A frequently used behavior link may be presented on the target user terminal, so that the target user can quickly enter the interface of the operation behavior by selecting the presented behavior link.

[0146] In this embodiment, the interactive interface processing method based on user behavior can be used for the setting of financial transaction interactive interfaces in the field of financial technology. In other embodiments, it can also be used for the processing of interactive interfaces in other fields with more obvious periodicity, such as the field of life payment, the login interactive interface of the multi-function module, etc., without specific restrictions.

[0147] According to the user behavior-based interactive interface processing method of the embodiment of the present disclosure, by matching the central cluster user information corresponding to the target user, it is possible to recommend links of commonly used behaviors to the target user, so that the target user can quickly switch to the corresponding operation interface by selecting the link on the target user end, thereby improving the target user's commonly used operation efficiency. At the same time, it is also possible to match and predict the target user's operation habits and make targeted recommendations, which can effectively improve the target user's usage experience and optimize the target user's operation steps.

[0148] Figure 5 The flowchart schematically shows a method for acquiring behavior feature data corresponding to user information of each central cluster according to an embodiment of the present disclosure.

[0149] In a specific embodiment of the present disclosure, the behavior feature data corresponding to each central cluster user information is obtained by statistically processing the behavior data corresponding to the user information of each other user within the range of each central cluster user information, and the acquisition method includes: operations S100A to S300A.

[0150] In operation S100A, user information of multiple other users is classified based on the ranges divided by the user information of each central cluster to obtain first user information of multiple other users within the ranges of the user information of each central cluster.

[0151] In this embodiment, the user information of each central cluster is obtained by clustering the user information of each other user within its own range, so the user information of each other user within the range of each central cluster user information is obtained, and the first user information is the user information of the other users within the range of each central cluster user information.

[0152] In operation S200A, a plurality of first behavior data corresponding to each piece of first user information is acquired based on the plurality of first user information. In this embodiment, the first behavior data is behavior feature data corresponding to each piece of first user information.

[0153] In operation S300A, the plurality of first behavior data are collated to obtain behavior characteristic data corresponding to the central cluster user information. In this embodiment, the plurality of first behavior data corresponding to each other user within the scope of each central cluster user information is summed up and statistically analyzed. The behavior characteristic data corresponding to the first behavior data includes, but is not limited to, operation time, transaction type, transaction frequency, transaction amount, etc.

[0154] Figure 6 The flowchart of step S300A of the method for acquiring behavior feature data corresponding to user information of each central cluster according to an embodiment of the present disclosure is schematically shown.

[0155] In another specific embodiment of the present disclosure, the first behavior data includes multiple time data and transaction type data corresponding to each time data, and operation S300A further includes operations S310A to S320A.

[0156] In operation S310A, based on the divided multiple time periods, time data of multiple first behavior data in each time period are counted respectively, and transaction type data corresponding to each time data in each time period is obtained.

[0157] In operation S320A, based on the usage frequencies of the multiple transaction type data in each time period, data statistics are performed on the same transaction type data in the multiple transaction type data in the form of cumulative counts to obtain behavioral feature data corresponding to the user information of each central cluster.

[0158] In this embodiment, by statistically analyzing transaction type data within each time period, a user data model that more accurately reflects actual user needs during each time period can be developed and established, thereby improving the accuracy of predicting customer needs. In this embodiment, transaction data of the same type is counted cumulatively, while transaction data of different types is counted individually to establish behavioral characteristic data corresponding to the user information of each central cluster.

[0159] In a specific embodiment, the behavioral feature data includes multiple time features and the transaction type corresponding to each time feature, and the first information includes time information; in this embodiment, when using the classification results to query the user data model, the behavioral data within each time period can be classified and counted according to the division of time periods, that is, the links of commonly used behaviors within the corresponding time period can be recommended according to the actual operation time of the target user.

[0160] Figure 7The flowchart of step S420 of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0161] Specifically in this embodiment, operation S420 may further include operations S421 and S422.

[0162] In operation S421, the time information of the first information is compared with multiple time features of the first behavior feature data to obtain multiple first time features that belong to the same period as the time information of the first information, and obtain a first transaction type corresponding to each first time feature.

[0163] In this embodiment, the time information of the first information is the time point at which the target user performs real-time operation. The first behavior feature includes time features of multiple different time points and the first transaction type corresponding to each time feature. In this embodiment, the time features of the multiple different time points of the first behavior feature are a collection of time features of the behavior data of other users within the user information range of the first central cluster. The first time feature is a time feature that belongs to the same period as the time information of the first information. The first transaction type is a transaction type corresponding to the first time feature. For example, the time information of the first information can be 9:00, the first time feature can be 10:00, and the first transaction type corresponding to the first time feature can be futures trading. There is no specific restriction on this.

[0164] In this embodiment, the division of each time period is made differently according to the hot time and non-hot time of the behavior. It is defaulted that the user's operations are more frequent in the time period of 6:00-22:00, and every 2 hours is selected as a time period; it is defaulted that the user's operations are less frequent in the time period from 22:00 to 6:00 the next day, and every 4 hours is selected as a time period. This can ensure the sample size of the data and improve the prediction accuracy of the model in each time period.

[0165] In operation S422 , based on the usage frequencies of the multiple first transaction types, hot spot data is extracted for the multiple first transaction types to obtain first prediction information.

[0166] In this embodiment, the frequencies of multiple first transaction types are counted, wherein the number of occurrences of different first transaction types are counted respectively to form a statistical data group for hot spot data extraction.

[0167] Figure 8 The flowchart of step S422 of the method for processing the interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0168] In a specific embodiment, operation S422 may further include operations S4221 to S4223.

[0169] In operation S4221, based on the usage frequencies of the plurality of first transaction types, N first transaction types with the highest usage frequencies are extracted to obtain first display information, where N is a positive integer greater than 1.

[0170] In this embodiment, the first display information is displayed on the homepage or other operation page of the financial interactive interface, allowing the target user to quickly switch to the corresponding transaction type interface by selecting the first display information. In this embodiment, N is a positive integer greater than 1, which means that more commonly used transaction types can be pushed to the interactive interface, increasing the probability of selection by the target user.

[0171] In operation S4222 , based on the usage frequencies of the plurality of first transaction types, M first transaction types with the highest usage frequencies are extracted to obtain first push information, where M is a positive integer greater than or equal to 1.

[0172] In this embodiment, the first push message is used to push the most frequently used transaction type to the target user, thereby prompting the target user to select the transaction type. This proactive push notification reminds the target user to regularly access the transaction type. This also prevents the target user from failing to quickly find the transaction type they need to access through passive display, thereby improving the target user's efficiency in operating the interactive interface. For example, the first push message can be sent to the target user via text message or as a pop-up window within the interactive interface, without specific limitation.

[0173] In operation S4223, first prediction information is obtained according to the first presentation information and the first push information.

[0174] Figure 9 A supplementary flow chart of a method for processing a user behavior-based interactive interface on a system side according to an embodiment of the present disclosure is schematically shown.

[0175] According to a specific embodiment of the present disclosure, the method for processing an interactive interface based on user behavior further includes:

[0176] In operation S600, first selection information of a target user terminal is obtained;

[0177] In operation S700, a target user data model pre-established based on the target user's behavioral characteristic data is obtained;

[0178] In operation S800, the target user data model is queried using the first selection information to obtain first pre-filled information corresponding to the first selection information;

[0179] In operation S900 , first pre-filled information is sent to a target user terminal.

[0180] The user behavior-based interactive interface processing method disclosed in the present invention can also provide pre-filled information in the corresponding transaction information options or transaction amount and other fill-in-blank items according to the target data model of the target user, so that the target user can quickly confirm and submit directly, which speeds up the efficiency of the target user's operation and optimizes the operation steps of the interactive interface faced by the target user, so that the interactive interface can directly provide the information and operations required by the target user.

[0181] Specifically in this embodiment, the first pre-filled information is transaction information or transaction amount. In other embodiments, the first pre-filled information can also be other information, such as personal information number, personal address, etc., and there is no specific limitation on this.

[0182] In this embodiment, the target user data model is obtained by statistically processing the behavioral characteristic data of the target user over a period of time, that is, the target user data model includes but is not limited to operation time, transaction type, transaction frequency, transaction amount, etc. In a specific embodiment, the target user data model after statistical processing is recorded in a matrix form. In other embodiments, there is no specific restriction on the recording form and type of the target user data model.

[0183] Figure 10 The flowchart of step S800 of the method for processing an interactive interface based on user behavior on the system side according to an embodiment of the present disclosure is schematically shown.

[0184] In a specific embodiment, the target user's behavioral characteristic data includes multiple transaction type characteristics and multiple transaction parameters corresponding to each transaction type characteristic. The first selection information includes transaction type information. Therefore, operation S800 may further include operations S810 to S820.

[0185] In operation S810, the target user data model is queried using the transaction type information of the first selection information to obtain a plurality of first transaction type features identical to the transaction type information, and obtain a first transaction parameter corresponding to each first transaction type feature.

[0186] Specifically in this embodiment, the first transaction type feature is a transaction type feature that is the same as the transaction type information of the first selection information, and the first transaction parameter is a transaction parameter corresponding to the first transaction type feature. This embodiment counts the frequency of multiple first transaction type features, wherein different first transaction type features are counted for the number of times they appear to form a statistical data group for hot spot data extraction.

[0187] In operation S820 , based on usage frequencies of the plurality of first transaction parameters, hotspot data extraction is performed on the plurality of first transaction parameters to obtain first pre-filled information.

[0188] In one specific embodiment, operation S820 extracts the most frequently used first transaction parameter based on the usage frequencies of multiple first transaction parameters to obtain first pre-filled information. In this embodiment, setting the most frequently used first transaction parameter as the first pre-filled information helps the target user quickly identify the required information, saving the target user's time in filling out the form. It also facilitates subsequent automated transaction completion on the target user's end through voice commands via the target user, in conjunction with the large language model, thereby improving user operational efficiency.

[0189] In a specific embodiment of the present disclosure, the target user data model is obtained by statistically processing the target user's behavioral characteristic data, and the obtaining method includes: operations S100B to S200B.

[0190] In operation S100B, a plurality of transaction type characteristics of the target user and a plurality of transaction parameters corresponding to each transaction type characteristic are obtained;

[0191] In operation S200B, based on the usage frequencies of multiple transaction parameters of each transaction type, data statistics are collected for the same transaction parameters in each transaction type in the form of cumulative counts to obtain a target user data model.

[0192] In this embodiment, a target user data model is established by obtaining the transaction types of the target user within a period of time and obtaining multiple transaction parameters under each transaction type, where the same transaction parameters are counted in the form of cumulative counts of the same type, and different transaction parameters are counted in the form of separate counts.

[0193] Figure 11 The flowchart of the method for processing the interactive interface based on user behavior for the target user terminal according to an embodiment of the present disclosure is schematically shown.

[0194] like Figure 11 As shown, the user behavior-based interactive interface processing method of this embodiment includes operations S100C to S200C.

[0195] At operation S100C, first information is sent to the system end. The first information is used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model. The user data model is pre-established based on information of multiple other users. The classification result is used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information. The first prediction information is used to trigger the system end to send the first prediction information to the target user end.

[0196] In operation S200C, first prediction information sent by the system end is received.

[0197] In this embodiment, the target user end sends a first message to the system segment to trigger the system end to perform an interactive interface processing method based on user behavior for the system end to send the first prediction information to the target user end. By receiving the first prediction information, the target user end can display the first display information in the first prediction information on the user interaction interface, and can pop up the first push information in the first prediction information on the user interaction interface.

[0198] In this embodiment, the first information can be executed in the above-described operations S310, operation S320, operation S410, operation S420, operation S421, operation S422, operation S4221, operation S4222 and operation 4223 on the system side, which will not be repeated here.

[0199] Figure 12 A supplementary flow chart of a method for processing an interactive interface based on user behavior for a target user terminal according to an embodiment of the present disclosure is schematically shown.

[0200] In a specific embodiment of the present disclosure, the user behavior-based interactive interface processing method of this embodiment further includes operations S300C to S400C.

[0201] At operation S300C, first selection information is sent to the system end. The first selection information is used to trigger the system end to query the target user data model to obtain first pre-filled information corresponding to the first selection information. The target user data model is pre-established based on the behavioral characteristic data of the target user. The first pre-filled information is used to trigger the system end to send the first pre-filled information to the target user end.

[0202] In operation S400C, first pre-filled information sent by the system is received.

[0203] In this embodiment, the target user terminal sends the first selection information to the system segment, that is, sends the transaction type selected by the target user to the system terminal, which is used to trigger the system terminal to perform the interactive interface processing method based on user behavior used for the system terminal to send the first pre-filled information to the target user terminal. By receiving the first pre-filled information, the target user terminal can pre-fill the first pre-filled information in the corresponding position of the user interaction interface, which is convenient for the target user to quickly confirm and avoids the problems of long operation time and low operation efficiency caused by manual input by the target user.

[0204] Since the principle of solving the problem by the above-mentioned user behavior-based interactive interface processing method for the target user end is similar to that of the user behavior-based interactive interface processing method for the system end, the implementation of the user behavior-based interactive interface processing method for the target user end can refer to the implementation of the user behavior-based interactive interface processing method for the system end, and the repeated parts will not be repeated.

[0205] In this embodiment, the first selection information may be the operations 600, 700, 800, 900, S810, and S820 described above that are executed on the system side, and details thereof will not be repeated here.

[0206] Based on the above-mentioned method for processing radiation scanning images, the present disclosure also provides an interactive interface processing device based on user behavior, which is used in the system side. Figure 13 The device is described in detail.

[0207] Figure 13 The structural block diagram of the user behavior-based interactive interface processing device for the system side according to an embodiment of the present disclosure is schematically shown.

[0208] like Figure 13 As shown, the user behavior-based interactive interface processing device 200 of this embodiment includes a first acquisition module 210 , a second acquisition module 220 , a first processing module 230 , a second processing module 240 and a first transmission module 250 .

[0209] The first acquisition module 210 is used to acquire the first information of the target user terminal. In one embodiment, the first acquisition module 210 can be used to perform the operation S100 described above, which will not be repeated here.

[0210] The second acquisition module 220 is used to acquire a user data model pre-established based on information of multiple other users. In one embodiment, the second acquisition module 220 can be used to perform the operation S200 described above, which will not be repeated here.

[0211] The first processing module 230 is configured to classify the first information using the user data model to obtain a classification result corresponding to the first information and the user data model. In one embodiment, the first processing module 230 can be configured to perform operation S300 described above. Furthermore, the first processing module 230 can also be configured to perform operations S310 and S320 described above, which are not further described here.

[0212] The second processing module 240 is configured to query the user data model using the classification result to obtain first prediction information corresponding to the first information. In one embodiment, the second processing module 240 can be configured to perform operation S400 described above. Furthermore, the second processing module 240 can also be configured to perform operations S410, S420, S421, S422, S4221, S4222, and S4223 described above, which are not further described herein.

[0213] The first transmission module 250 is configured to send the first prediction information to the target user terminal. In one embodiment, the first transmission module 250 may be configured to perform the operation S500 described above, which will not be described in detail here.

[0214] In a specific embodiment, the user behavior-based interactive interface processing device further includes:

[0215] The third acquisition module 260 is used to acquire the first selection information of the target user terminal. In one embodiment, the third acquisition module 260 can be used to perform the operation S600 described above, which will not be repeated here.

[0216] The fourth acquisition module 270 is used to obtain a target user data model pre-established based on the target user's behavioral characteristic data. In one embodiment, the fourth acquisition module 270 can be used to perform the operation S700 described above, which will not be repeated here.

[0217] The third processing module 280 uses the first selection information to query the target user data model to obtain the first pre-filled information corresponding to the first selection information; in one embodiment, the third processing module 280 can be used to execute the operation S800 described above. At the same time, the third processing module 280 can also be used to execute the operation S810 and operation S820 described above, which will not be repeated here.

[0218] The second transmission module 290 sends the first pre-filled information to the target user terminal. In one embodiment, the second transmission module 290 can be used to perform the operation S900 described above, which will not be repeated here.

[0219] According to an embodiment of the present disclosure, any multiple modules among the first acquisition module 210, the second acquisition module 220, the first processing module 230, the second processing module 240, the first transmission module 250, the third acquisition module 260, the fourth acquisition module 270, the third processing module 280, and the second transmission module 290 can be combined into a single module for implementation, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module. According to an embodiment of the present disclosure, at least one of the first acquisition module 210, the second acquisition module 220, the first processing module 230, the second processing module 240, the first transmission module 250, the third acquisition module 260, the fourth acquisition module 270, the third processing module 280, and the second transmission module 290 may 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 may be implemented in hardware or firmware by any other reasonable means of circuit integration or packaging, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first acquisition module 210, the second acquisition module 220, the first processing module 230, the second processing module 240, the first transmission module 250, the third acquisition module 260, the fourth acquisition module 270, the third processing module 280, and the second transmission module 290 may be at least partially implemented as a computer program module, which, when executed, may perform the corresponding function.

[0220] Figure 14 The structural block diagram of the user behavior-based interactive interface processing device for a target user terminal according to an embodiment of the present disclosure is schematically shown.

[0221] like Figure 14 As shown, the user behavior-based interactive interface processing device 300 of this embodiment includes a third transmission module 310 and a fifth acquisition module 320 .

[0222] The third transmission module 310 transmits first information to the system end. The first information is used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model. The user data model is pre-established based on information of multiple other users. The classification result is used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information. The first prediction information is used to trigger the system end to transmit the first prediction information to the target user end. In one embodiment, the third transmission module 310 can be used to perform operation S100C described above, which will not be repeated here.

[0223] The fifth acquisition module 320 is configured to receive the first prediction information sent by the system end. In one embodiment, the fifth acquisition module 320 may be configured to perform the operation S200C described above, which will not be described in detail here.

[0224] In a specific embodiment, the user behavior-based interactive interface processing device further includes:

[0225] The fourth transmission module 330 is used to obtain the first selection information of the target user terminal. In one embodiment, the fourth transmission module 330 can be used to perform the operation S300C described above, which will not be repeated here.

[0226] The sixth acquisition module 340 is used to obtain a target user data model pre-established based on the target user's behavioral characteristic data. In one embodiment, the sixth acquisition module 340 can be used to perform the operation S400C described above, which will not be repeated here.

[0227] According to embodiments of the present disclosure, any multiple of the third transmission module 310, the fifth acquisition module 320, the fourth transmission module 330, and the sixth acquisition module 340 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the third transmission module 310, the fifth acquisition module 320, the fourth transmission module 330, and the sixth acquisition module 340 may 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 may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the third transmission module 310 , the fifth acquisition module 320 , the fourth transmission module 330 , and the sixth acquisition module 340 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0228] Figure 15 A block diagram of an electronic device suitable for implementing a method for processing an interactive interface based on user behavior according to an embodiment of the present disclosure is schematically shown.

[0229] like Figure 15 As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage unit 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0230] Various programs and data required for the operation of the electronic device 900 are stored in the RAM 903. The processor 901, ROM 902, and RAM 903 are connected to each other via a bus 904. The processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than the ROM 902 and RAM 903. The processor 901 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in one or more memories.

[0231] According to an embodiment of the present disclosure, electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to bus 904. Electronic device 900 may also include one or more of the following components connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 908 including a hard disk; and a communication section 909 including a network interface card such as a LAN card or modem. Communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 910 as needed, so that computer programs read from the removable media can be installed into storage section 908 as needed.

[0232] The present disclosure also provides a computer-readable storage medium, which may be included in the apparatus described in the above embodiments, or may exist independently without being incorporated into the apparatus. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present disclosure.

[0233] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above, and / or one or more memories other than ROM 902 and RAM 903.

[0234] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to enable the computer system to implement the user behavior-based interactive interface processing method provided in the embodiments of the present disclosure.

[0235] The computer program executes the above functions defined in the apparatus of the embodiment of the present disclosure when the processor 901 executes the computer program. According to the embodiment of the present disclosure, the apparatus described above can be implemented by a computer program module.

[0236] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0237] In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-mentioned functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the above-mentioned means and the like can be implemented by computer program modules.

[0238] 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 computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving 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 via the Internet).

[0239] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of 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 an order different from that 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 flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0240] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0241] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A user behavior-based interactive interface processing method, used on the system side, characterized in that: The method comprises: Obtaining first information of a target user terminal; Obtain a pre-built user data model based on information of multiple other users; Classifying the first information using the user data model to obtain a classification result corresponding to the first information and the user data model; querying the user data model using the classification result to obtain first prediction information corresponding to the first information; The first prediction information is sent to the target user terminal.

2. The method for processing an interactive interface based on user behavior according to claim 1, characterized in that: The user data model includes multiple central cluster user information and behavioral feature data corresponding to each central cluster user information.

3. The method for processing an interactive interface based on user behavior according to claim 2, characterized in that: The plurality of central cluster user information is obtained by clustering the user information of the plurality of other users; The behavior feature data corresponding to each of the central cluster user information is obtained by statistically processing the behavior data corresponding to the user information of each of the other users within the range of the central cluster user information.

4. The method for processing an interactive interface based on user behavior according to claim 2 or 3, characterized in that: The classifying the first information by using the user data model to obtain a classification result corresponding to the first information and the user data model includes: Performing a similarity comparison on the first information using the plurality of central cluster user information of the user data model, and determining the first central cluster user information having the highest similarity to the first information; The classification result is obtained according to the first central cluster user information.

5. The method for processing an interactive interface based on user behavior according to claim 4, characterized in that: The querying the user data model using the classification result to obtain first prediction information corresponding to the first information includes: querying the user data model using the first central cluster user information to obtain first behavior feature data corresponding to the first central cluster user information; Hotspot data is extracted using the first behavior feature data to obtain the first prediction information.

6. The method for processing an interactive interface based on user behavior according to claim 5, characterized in that: The first central cluster user information is the central cluster user information having the highest similarity to the first information; The first behavior feature data is the behavior feature data corresponding to the first center cluster user information.

7. The method for processing an interactive interface based on user behavior according to claim 5, characterized in that: The behavior feature data includes multiple time features and transaction types corresponding to each time feature. The first information includes time information. The step of extracting hotspot data using the first behavior feature data to obtain the first prediction information includes: Comparing the time information of the first information with the multiple time features of the first behavior feature data to obtain multiple first time features that belong to the same time period as the time information of the first information, and obtaining a first transaction type corresponding to each of the first time features; Based on the usage frequencies of the plurality of first transaction types, hot spot data are extracted for the plurality of first transaction types to obtain the first prediction information.

8. The method for processing an interactive interface based on user behavior according to claim 7, characterized in that: The first time feature is the time feature that belongs to the same period as the time information of the first information; The first transaction type is the transaction type corresponding to the first time feature.

9. The method for processing an interactive interface based on user behavior according to claim 7, characterized in that: The extracting hotspot data of the plurality of first transaction types based on the usage frequencies of the plurality of first transaction types to obtain the first prediction information includes: Based on the usage frequencies of the plurality of first transaction types, extracting N first transaction types with the highest usage frequencies to obtain first display information, where N is a positive integer greater than 1; Extracting M first transaction types with the highest usage frequencies based on the usage frequencies of the plurality of first transaction types to obtain first push information, where M is a positive integer greater than or equal to 1; The first prediction information is obtained according to the first display information and the first push information.

10. The method for processing an interactive interface based on user behavior according to claim 1, characterized in that: The method further comprises: Acquire first selection information of the target user terminal; Obtain a target user data model pre-established based on the target user's behavioral characteristic data; querying the target user data model using the first selection information to obtain first pre-filled information corresponding to the first selection information; The first pre-filled information is sent to the target user terminal.

11. The method for processing an interactive interface based on user behavior according to claim 10, characterized in that: The target user data model is obtained by statistically processing the behavioral characteristic data of the target user.

12. The method according to claim 11, characterized in that The behavior feature data of the target user includes a plurality of transaction type features and a plurality of transaction parameters corresponding to each of the transaction type features, and the first selection information includes transaction type information.

13. The method for processing an interactive interface based on user behavior according to claim 12, characterized in that: The querying the target user data model using the first selection information to obtain first pre-filled information corresponding to the first selection information includes: querying the target user data model using the transaction type information of the first selection information to obtain a plurality of first transaction type features identical to the transaction type information, and obtaining a first transaction parameter corresponding to each of the first transaction type features; Based on the usage frequencies of the plurality of first transaction parameters, hot data extraction is performed on the plurality of first transaction parameters to obtain the first pre-filled information.

14. The method for processing an interactive interface based on user behavior according to claim 13, characterized in that: The first transaction type feature is the same as the transaction type information of the first selection information; The first transaction parameter is the transaction parameter corresponding to the first transaction type feature.

15. The method for processing an interactive interface based on user behavior according to claim 13, characterized in that: The extracting hotspot data of the plurality of first transaction parameters based on the usage frequencies of the plurality of first transaction parameters to obtain the first pre-filled information includes: Based on the usage frequencies of the plurality of first transaction parameters, the first transaction parameter with the highest usage frequency is extracted to obtain the first pre-filled information.

16. The method for processing an interactive interface based on user behavior according to claim 3, characterized in that: The behavior feature data corresponding to each of the central cluster user information is obtained by statistically processing the behavior data corresponding to the user information of each of the other users within the range of each of the central cluster user information, and the acquisition method includes: classifying the user information of the plurality of other users based on the ranges divided by the user information of each central cluster to obtain first user information of the plurality of other users within the ranges of the user information of each central cluster; Acquire, based on the plurality of first user information, a plurality of first behavior data corresponding to each piece of the first user information; The plurality of first behavior data are used to perform data sorting to obtain the behavior feature data corresponding to the center cluster user information.

17. The method for processing an interactive interface based on user behavior according to claim 16, characterized in that: The first user information is the user information of the other users within the user information range of each central cluster; The first behavior data is the behavior feature data corresponding to each of the first user information.

18. The method for processing an interactive interface based on user behavior according to claim 16, characterized in that: The behavior data includes time data and transaction type data corresponding to the time data. The step of using the plurality of the first behavior data to perform data sorting to obtain the behavior feature data corresponding to the center cluster user information includes: Based on the divided multiple time periods, respectively counting the time data of the multiple first behavior data in each time period, and obtaining the transaction type data corresponding to each of the time data in each time period; Based on the usage frequency of the plurality of transaction type data in each time period, data statistics are performed on the same transaction type data in the plurality of transaction type data in the form of cumulative counts to obtain the behavior feature data corresponding to each central cluster user information.

19. The method for processing an interactive interface based on user behavior according to claim 12, characterized in that: The target user data model is obtained by statistically processing the target user's behavioral feature data, and the method for obtaining the model includes: Acquire multiple transaction type characteristics of the target user and multiple transaction parameters corresponding to each of the transaction type characteristics; Based on the usage frequencies of the plurality of transaction parameters of each transaction type, data statistics are performed on the same transaction parameters in each transaction type in the form of cumulative counts to obtain the target user data model.

20. A method for processing an interactive interface based on user behavior, used for a target user terminal, characterized in that: The method comprises: Sending first information to a system end, the first information being used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model, the user data model being pre-established based on information of multiple other users, the classification result being used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information, the first prediction information being used to trigger the system end to send the first prediction information to the target user end; Receive the first prediction information sent by the system end.

21. A user behavior-based interactive interface processing device, used in a system, characterized in that: The user behavior-based interactive interface processing device includes: A first acquisition module, configured to acquire first information of a target user terminal; A second acquisition module is used to acquire a user data model pre-established based on information of multiple other users; a first processing module, configured to classify the first information using the user data model to obtain a classification result corresponding to the first information and the user data model; a second processing module, configured to query the user data model using the classification result to obtain first prediction information corresponding to the first information; A first transmission module sends the first prediction information to the target user terminal.

22. A user behavior-based interactive interface processing device, used for a target user terminal, characterized in that: The user behavior-based interactive interface processing device includes: a third transmission module, configured to send first information to a system end, wherein the first information is used to trigger the system end to classify the first information using a user data model to obtain a classification result corresponding to the first information and the user data model, wherein the user data model is pre-established based on information of multiple other users, and the classification result is used to trigger the system end to query the user data model using the classification result to obtain first prediction information corresponding to the first information, and the first prediction information is used to trigger the system end to send the first prediction information to the target user end; The fifth acquisition module is used to receive the first prediction information sent by the system end.

23. 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 interactive interface processing method based on user behavior according to any one of claims 1 to 20.

24. 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 user behavior-based interactive interface processing method according to any one of claims 1 to 20 are implemented.

25. 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 user behavior-based interactive interface processing method according to any one of claims 1 to 20 are implemented.

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