Information rearrangement method and device based on financial system, electronic equipment and medium

Abstract

The embodiment of the invention provides an information rearrangement method and device based on a financial system, electronic equipment and a medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring all financial products in a financial system; for each financial product, collecting data generated when a user interacts with the financial product within a preset duration to obtain sample data; generating triple data according to the user information of the user, the sample data and the financial product; performing inversion operation on the triple data based on the interaction frequency to obtain a sample sequence; setting a plurality of demarcation values according to the sample sequence and the interaction frequency, and setting a pressing function according to the demarcation values; performing score calculation on the triple data based on a pressing function to obtain a scoring result; performing rearrangement operation on the financial products according to the scoring result to obtain a target display sequence; and recommending a target financial product corresponding to the order of the target display sequence to the user. According to the embodiment of the invention, the product recommendation accuracy can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to an information rearrangement method, device, electronic device and medium based on a financial system. Background Art

[0002] In the financial field, insurance recommendation systems play a key role in the financial field. They not only provide users with personalized insurance product recommendations, but also help insurance companies improve customer stickiness and market competitiveness.

[0003] However, traditional systems often rely on static user information and simple product matching logic, ignoring the dynamic changes in user behavior and the complexity of insurance needs. And as insurance products are frequently updated and changed, the insurance products recommended by traditional recommendation systems may not necessarily meet the current needs of users. For example, a user may have just purchased a specific type of insurance, but the system still recommends similar or duplicate products without considering the user's existing insurance coverage. This overlap may not only waste the user's time, but may also lead to user disappointment and dissatisfaction with the system. If the system always recommends products with high user click-through rates, it is easy to form a Matthew effect, that is, popular products get more exposure, while those that are not clicked or have low click-through rates are gradually marginalized, reducing their exposure opportunities, resulting in a lack of personalization and accuracy in the recommendation results. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an information rearrangement method, device, electronic device and medium based on a financial system, which can improve the accuracy of product recommendations and avoid the Matthew effect.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes an information rearrangement method based on a financial system, the method comprising:

[0006] Access all financial products in the financial system;

[0007] For each of the financial products, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, wherein the sample data includes the interaction frequency;

[0008] Generate triple data according to the user information of the user, the sample data and the financial product;

[0009] Performing a reverse operation on the triple data based on the interaction frequency to obtain a sample sequence;

[0010] Setting a plurality of cutoff values ​​according to the sample sequence and the interaction frequency, and setting a suppression function according to the cutoff values;

[0011] Calculate the score of the triple data based on the suppression function to obtain a scoring result;

[0012] Rearranging the financial products according to the scoring results to obtain a target display sequence;

[0013] Target financial products corresponding to the order of the target display sequence are recommended to the user.

[0014] In some embodiments, for each of the financial products, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, including:

[0015] For each of the financial products, record the user's click behavior, collection behavior, and purchase behavior of the financial product within a preset time period;

[0016] Collecting a first frequency of the user's clicking behavior, a second frequency of the collecting behavior, and a third frequency of the purchasing behavior;

[0017] Obtaining an interaction frequency according to the first frequency, the second frequency, and the third frequency;

[0018] Obtaining interactive behavior according to the clicking behavior, the collecting behavior, and the purchasing behavior;

[0019] Sample data is obtained according to the interaction frequency and the interaction behavior.

[0020] In some embodiments, the setting of multiple cutoff values ​​according to the sample sequence and the interaction frequency includes:

[0021] Dividing the sample sequence into intervals based on a preset Pareto analysis method to obtain a first interval, a second interval, and a third interval, wherein any two intersections of the first interval, the second interval, and the third interval are empty;

[0022] The minimum value of the second interval is set as the first delimiting value, and the maximum value of the second interval is set as the second delimiting value.

[0023] In some embodiments, the step of setting a suppression function according to the demarcation value includes:

[0024] Setting a first piecewise function corresponding to the first interval, a second piecewise function corresponding to the second interval, and a third piecewise function corresponding to the third interval;

[0025] The segmentation value of the first segmentation function is set according to the first boundary value, the segmentation value of the second segmentation function is set according to the first boundary value and the second boundary value, and the segmentation value of the third segmentation function is set according to the second boundary value to obtain a suppression function.

[0026] In some embodiments, the dividing the sample sequence into intervals based on a preset Pareto analysis method to obtain a first interval, a second interval, and a third interval includes:

[0027] Based on a preset Pareto analysis method, determining an interval in the sample sequence at a first preset ratio as a first interval, determining an interval in the sample sequence at a second preset ratio as a second interval, and determining an interval in the sample sequence at a third preset ratio as a third interval;

[0028] or,

[0029] Setting a first ratio threshold and a second ratio threshold according to a preset Pareto analysis method;

[0030] The sample sequence is cut according to the first ratio threshold and the second ratio threshold, a sequence portion less than the first ratio threshold is determined as a first interval, a sequence portion greater than or equal to the first ratio threshold and less than the second ratio threshold is determined as a second interval, and a sequence portion greater than or equal to the second ratio threshold is determined as a third interval.

[0031] In some embodiments, the triple data includes multiple groups of data to be scored; the scoring of the triple data based on the suppression function to obtain the scoring result includes:

[0032] For each set of scoring data in the triplet data, inputting the scoring data into a preset deep learning model to perform refined ranking score calculation, and outputting the refined ranking score corresponding to the scoring data;

[0033] Dividing the triple data according to the first dividing value, the second dividing value and the interaction frequency to obtain a first data set, a second data set and a third data set;

[0034] Determine a first refined ranking score set corresponding to the first data set, a second refined ranking score set corresponding to the second data set, and a third refined ranking score set corresponding to the third data set;

[0035] Input the first data set into the first piecewise function to output a first rearranged score set, input the second data set into the second piecewise function to output a second rearranged score set, and input the third data set into the third piecewise function to output a third rearranged score set;

[0036] Determine a first score according to the first refined ranking score set and the first rearranged score set, determine a second score according to the second refined ranking score set and the second rearranged score set, and determine a third score according to the third refined ranking score set and the third rearranged score set;

[0037] A scoring result is obtained according to the first score, the second score and the third score.

[0038] In some embodiments, dividing the triple data according to the first dividing value, the second dividing value and the interaction frequency to obtain a first data set, a second data set and a third data set includes:

[0039] comparing the interaction frequency with the first cutoff value and the second cutoff value;

[0040] Taking the data in the triple data whose interaction frequency is less than the first cutoff value as the first data set;

[0041] Taking the data in the triplet data whose interaction frequency is greater than or equal to the first cutoff value and less than or equal to the second cutoff value as the second data set;

[0042] The data in the triplet data whose interaction frequency is greater than the second cutoff value is taken as the third data set.

[0043] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes an information rearrangement device based on a financial system, the device comprising:

[0044] Product acquisition module, used to obtain all financial products in the financial system;

[0045] A data collection module, configured to collect, for each of the financial products, data generated by the user's interaction with the financial product within a preset time period to obtain sample data, wherein the sample data includes an interaction frequency;

[0046] A data generation module, used to generate triple data according to the user information of the user, the sample data and the financial product;

[0047] A sequence inversion module, used for performing an inversion operation on the triple data based on the interaction frequency to obtain a sample sequence;

[0048] A cutoff value setting module, used to set a plurality of cutoff values ​​according to the sample sequence and the interaction frequency, and to set a suppression function according to the cutoff values;

[0049] A score calculation module, used to calculate the score of the triple data based on the suppression function to obtain a scoring result;

[0050] A target generation module, used to rearrange the financial products according to the scoring results to obtain a target display sequence;

[0051] The product recommendation module is used to recommend to the user target financial products corresponding to the order of the target display sequence.

[0052] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the information rearrangement method based on the financial system as described in the first aspect when executing the computer program.

[0053] To achieve the above objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the information rearrangement method based on the financial system as described in the first aspect.

[0054] The information rearrangement method, device, electronic device and storage medium based on the financial system proposed in the present application first obtain all financial products in the financial system, and for each financial product, collect the data generated by the user's interaction with the financial product within a preset time to obtain sample data, so that the user's recent interests and demand changes can be accurately reflected through the sample data, and then generate triple data based on the user's user information, sample data and financial products to achieve summary statistics of the data, which is convenient for subsequent analysis of the user's interaction frequency with different financial products. After that, the triple data is reversed based on the interaction frequency, and the triple data can be sorted in combination with the user's active behavior and interaction frequency to obtain a sample sequence, and multiple demarcation values ​​are set according to the sample sequence and the interaction frequency, so as to identify multiple interaction frequency values ​​that affect the sorting result, which is convenient for subsequent improvement of decision quality, and a suppression function is set according to the demarcation value to target different positions of the sample sequence. The financial products with different placements are given different degrees of suppression, thereby increasing the degree of suppression on high-frequency financial products and reducing their probability of appearing at the head, while reducing the degree of suppression on financial products with low interaction frequency and increasing the recommendation exposure opportunities for financial products with low interaction frequency. The score of the triplet data is calculated based on the suppression function to suppress financial products with higher initial scores and increase the scores of financial products with lower scores to obtain the scoring results, thereby avoiding the Matthew effect and increasing the exposure opportunities of financial products at the tail of the sample sequence. The financial products are rearranged according to the scoring results to obtain the target display sequence to meet the user's needs for diversity and freshness. Finally, the target financial products corresponding to the order of the target display sequence are recommended to the user, which can guide the user to discover new products while providing personalized financial product recommendations that are more in line with user needs, and increase the user's participation in new products, further improving the accuracy and effectiveness of the recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of a method for rearranging information based on a financial system provided in an embodiment of the present application;

[0056] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.

[0057] Figure 3 A flow chart of a method for setting multiple cutoff values ​​according to a sample sequence and an interaction frequency provided in an embodiment of the present application;

[0058] Figure 4 A flow chart of a method for setting a suppression function according to a demarcation value provided in an embodiment of the present application;

[0059] Figure 5 yes Figure 3 Flow chart of step S301 in FIG.

[0060] Figure 6 yes Figure 1 Flow chart of step S106 in FIG.

[0061] Figure 7 yes Figure 6 Flowchart of step S602 in FIG.

[0062] Figure 8 It is a structural schematic diagram of an information rearrangement device based on a financial system provided in an embodiment of the present application;

[0063] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0067] First, some nouns involved in this application are analyzed:

[0068] Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0069] Pareto Analysis: Often used to identify and focus on the most important issues or causes, based on the Pareto Principle (80 / 20 Principle), which states that 80% of the results usually come from 20% of the causes.

[0070] The information rearrangement method and device based on the financial system, electronic device and storage medium provided in the embodiments of the present application can improve the accuracy of product recommendations and avoid the Matthew effect.

[0071] The following embodiments are used to illustrate the present invention in detail. First, the information rearrangement method based on the financial system in the embodiments of the present application is described.

[0072] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0073] The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technologies mainly include computer vision technology, robotics technology, biometrics technology, module management technology of online meeting systems, natural language processing technology, and machine learning / deep learning.

[0074] The information rearrangement method based on the financial system provided in the embodiment of the present application relates to the field of financial technology. The information rearrangement method based on the financial system provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the information rearrangement method based on the financial system, etc., but is not limited to the above forms.

[0075] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data information types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0076] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0077] In the financial field, insurance recommendation systems play a key role in the financial field. They not only provide users with personalized insurance product recommendations, but also help insurance companies improve customer stickiness and market competitiveness.

[0078] However, traditional systems often rely on static user information and simple product matching logic, ignoring the dynamic changes in user behavior and the complexity of insurance needs. And as insurance products are frequently updated and changed, the insurance products recommended by traditional recommendation systems may not necessarily meet the current needs of users. For example, a user may have just purchased a specific type of insurance, but the system still recommends similar or duplicate products without considering the user's existing insurance coverage. This overlap may not only waste the user's time, but may also lead to user disappointment and dissatisfaction with the system. If the system always recommends products with high user click-through rates, it is easy to form a Matthew effect, that is, popular products get more exposure, while those that are not clicked or have low click-through rates are gradually marginalized, reducing their exposure opportunities, resulting in a lack of personalization and accuracy in the recommendation results.

[0079] In order to solve the above problems, the present embodiment provides an information rearrangement method, device, electronic device and storage medium based on a financial system. First, all financial products in the financial system are obtained. For each financial product, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, so that the user's recent interests and demand changes can be accurately reflected through the sample data. Then, triple data is generated based on the user's user information, sample data and financial products to achieve summary statistics of the data, which is convenient for subsequent analysis of the user's interaction frequency with different financial products. Afterwards, the triple data is reversed based on the interaction frequency, and the triple data can be sorted in combination with the user's active behavior and interaction frequency to obtain a sample sequence. Multiple demarcation values ​​are set according to the sample sequence and the interaction frequency, so as to identify multiple interaction frequency values ​​that affect the sorting result, which is convenient for subsequent improvement of decision quality, and a suppression function is set according to the demarcation value to target the sample. Financial products at different positions in the sequence are given different degrees of suppression, thereby increasing the degree of suppression on high-frequency financial products and reducing their probability of appearing at the head, while reducing the degree of suppression on financial products with low interaction frequency and increasing the recommendation exposure opportunities for financial products with low interaction frequency. The score of the triplet data is calculated based on the suppression function to suppress financial products with higher initial scores and increase the scores of financial products with lower scores to obtain the scoring results, thereby avoiding the Matthew effect and increasing the exposure opportunities of financial products at the tail of the sample sequence. The financial products are rearranged according to the scoring results to obtain the target display sequence to meet the user's needs for diversity and freshness. Finally, the target financial products corresponding to the order of the target display sequence are recommended to the user, which can guide users to discover new products while providing personalized financial product recommendations that are more in line with user needs, increase user participation in new products, and further improve the accuracy and effectiveness of recommendations.

[0080] The following is a detailed description with reference to the accompanying drawings.

[0081] Figure 1 is an optional flow chart of the information rearrangement method based on the financial system provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S108.

[0082] Step S101, obtaining all financial products in the financial system.

[0083] In step S101 of some embodiments, all financial products in the financial system are obtained, such as pension insurance products, automobile insurance products, financial management insurance products, etc., to facilitate subsequent analysis of their interaction frequencies.

[0084] Step S102: for each financial product, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, wherein the sample data includes the interaction frequency.

[0085] In step S102 of some embodiments, since the user may click or purchase certain financial products during the browsing process, for each financial product, the data generated by the user's interaction with the financial product within a preset time period is collected. By analyzing the user's interactive behavior and frequency table over a period of time, the relevance and accuracy of the financial system recommendation can be improved, sample data can be obtained, and real-time updates of user interactive behavior and frequency can be achieved. With the continuous update of user behavior data, the recommendation system can quickly adapt to market changes and changes in user needs. This real-time or near real-time data processing capability makes the recommendation system more flexible and adaptable.

[0086] It can be understood that the interaction frequency in the embodiment of the present application can be the number of user clicks, the number of purchases, etc. The interaction frequency will be specifically described below and the embodiment of the present application will not be repeated here.

[0087] Step S103, generating triplet data according to the user information, sample data and financial products of the user.

[0088] In step S103 of some embodiments, the user information includes the user ID, the sample data includes the interaction frequency, and the financial product includes the financial product ID. The embodiment of the present application generates triple data based on the user ID, the financial product ID and the interaction frequency to facilitate subsequent analysis of financial products with different interaction frequencies.

[0089] It can be understood that the triplet data in the embodiment of the present application can be expressed as (uid, iid, count), where uid is the user ID, iid is the financial product ID, and count is the interaction frequency.

[0090] Step S104, performing a reverse sort operation on the triple data based on the interaction frequency to obtain a sample sequence.

[0091] In step S104 of some embodiments, the triple data is inverted based on the interaction frequency. Specifically, the interaction behavior is used as a keyword to create an inverted list, and the list of users and financial products under each interaction behavior is recorded to obtain a sample sequence, thereby improving the accuracy of recommendations and user satisfaction.

[0092] Step S105, setting a plurality of cutoff values ​​according to the sample sequence and the interaction frequency, and setting a suppression function according to the cutoff values.

[0093] In step S105 of some embodiments, multiple dividing values ​​are set according to the sample sequence and the interaction frequency, so that the activity of user behavior can be distinguished more finely, and the key factors that have the greatest impact on the results can be identified more accurately. A suppression function is set according to the dividing value, so as to achieve differentiated suppression of financial products with different interaction frequencies and meet users' needs for diversity and freshness.

[0094] Step S106, score the triplet data based on the suppression function to obtain a scoring result.

[0095] In step S106 of some embodiments, a score is calculated for the triplet data based on a suppression function to obtain a scoring result, thereby re-ranking and scoring financial products with different interaction frequencies. This can increase the degree of suppression for financial products with high interaction frequencies and reduce the degree of suppression for financial products with low interaction frequencies, thereby increasing the recommendation exposure opportunities for financial products with low interaction frequencies, thereby meeting user needs, facilitating users to discover new products, and improving user experience.

[0096] Step S107, re-arrange the financial products according to the scoring results to obtain a target display sequence.

[0097] In step S107 of some embodiments, the financial products are rearranged according to the scoring results to reduce the probability of appearance of the top financial products and increase the probability of appearance of the bottom financial products, thereby obtaining a target display sequence.

[0098] Step S108: recommending to the user target financial products corresponding to the order of the target display sequence.

[0099] In step S108 of some embodiments, target financial products corresponding to the order of the target display sequence are recommended to the user, satisfying the user's demand for diversity and novelty, guiding the user to discover new products, and improving the user's participation.

[0100] See also Figure 2 In some embodiments, step S102 may also include but is not limited to steps S201 to S205.

[0101] Step S201, for each financial product, record the user's click behavior, collection behavior and purchase behavior on the financial product within a preset time period.

[0102] Step S202, collecting the first frequency of the user's click behavior, the second frequency of the collection behavior, and the third frequency of the purchase behavior.

[0103] Step S203, obtaining an interaction frequency according to the first frequency, the second frequency and the third frequency.

[0104] Step S204, obtaining interactive behaviors according to the click behaviors, collection behaviors, and purchase behaviors.

[0105] Step S205, obtaining sample data according to the interaction frequency and the interaction behavior.

[0106] In steps S201 to S205 of some embodiments, in the process of collecting data generated by the user's interaction with the financial product within a preset time, the user may browse, collect or purchase the financial product. In order to capture the user's preference, the embodiment of the present application records the user's click behavior, collection behavior and purchase behavior of the financial product within the preset time for each financial product in the financial system. These behaviors reflect the user's preference for the financial product, and the user's preference and purchase intention can be accurately understood. Then, the frequency of various interactive behaviors of the user is recorded. Specifically, the first frequency of the user's click behavior, the second frequency of the collection behavior and the third frequency of the purchase behavior are collected, so that the number of times various interactive behaviors occur can be determined through different frequencies, and the user's interests and demand changes can be further accurately reflected, and the user's real-time needs can be accurately captured. Afterwards, the user's interaction frequency with the financial product is obtained according to the first frequency, the second frequency and the third frequency, and the interactive behavior is obtained according to the click behavior, the collection behavior and the purchase behavior. By analyzing the user's interactive behavior and frequency table over a period of time, the relevance and accuracy of the financial system recommendation can be improved. Finally, sample data is obtained according to the interactive frequency and interactive behavior, and real-time update of the user's interactive behavior and frequency is achieved. As user behavior data is continuously updated, the recommendation system can quickly adapt to market changes and changes in user needs. This real-time or near-real-time data processing capability makes the recommendation system more flexible and adaptable.

[0107] It should be noted that the preset duration in the embodiment of the present application can be set according to user needs, for example, recording the user's interaction behavior within one month, recording the user's interaction behavior within one week, recording the user's interaction behavior within two weeks, etc. The embodiment of the present application does not make specific restrictions.

[0108] In some embodiments, the embodiments of the present application can also record the user's browsing behavior for financial products within a preset time period, and record the browsing duration of the user's browsing behavior, and use click behavior, collection behavior, purchase behavior and browsing behavior as interactive behaviors. At the same time, sample data can be obtained based on the interaction frequency, interactive behavior and browsing duration to further improve the relevance and accuracy of financial system recommendations.

[0109] Specifically, the first frequency in the embodiment of the present application is the number of click behaviors, for example, the number of times a user clicks on a financial product or link; the second frequency is the number of collection behaviors, for example, the number of times a user adds a product or content to a collection list; the third frequency is the number of purchase behaviors, for example, the number of times a user actually purchases a product.

[0110] See also Figure 3 , Figure 3 A flow chart of a method for setting multiple demarcation values ​​according to a sample sequence and an interaction frequency provided in an embodiment of the present application, wherein the method includes but is not limited to steps S301 to S302.

[0111] Step S301, dividing the sample sequence into intervals based on a preset Pareto analysis method to obtain a first interval, a second interval and a third interval.

[0112] It should be noted that any two intersections of the first interval, the second interval and the third interval are empty.

[0113] Step S302: Set the minimum value of the second interval as the first delimiting value, and set the maximum value of the second interval as the second delimiting value.

[0114] In steps S301 to S302 of some embodiments, in the process of setting multiple dividing values ​​according to the sample sequence and the interaction frequency, first, the sample sequence is divided into intervals based on the preset Pareto analysis method to obtain a first interval, a second interval and a third interval, wherein the first interval, the second interval and the third interval do not intersect with each other, thereby achieving effective division of the intervals, being able to more finely distinguish the activity of user behavior, and facilitating the subsequent formulation of different personalized suppression strategies for different intervals. Finally, the minimum value of the second interval is set to the first dividing value, and the maximum value of the second interval is set to the second dividing value. By setting different dividing values, the key factors that have the greatest impact on the results can be more accurately identified.

[0115] It can be understood that, taking the Pareto analysis method to divide the sample sequence according to the ratio of 20%, 40% and 40% as an example for explanation, according to the 28th law, the first 20% is regarded as the high active interaction number interval (first interval), followed by 40% as the medium active interaction number interval (second interval), and the last 40% as the low active interaction number interval (third interval), and then determine the interval values ​​of the first interval, the second interval and the third interval. Specifically, in the embodiment of the present application, the minimum value of the first interval is 0 and the maximum value is 3, the minimum value of the second interval is 4 and the maximum value is 10, and the minimum value of the third interval is 11 and the maximum value is 15. At this time, the minimum value of the second interval is set to the first delimiting value, that is, 4 is set to the first delimiting value, and the maximum value of the second interval is set to the second delimiting value, that is, 10 is set to the second delimiting value, so as to facilitate the subsequent setting of different suppression strategies for different intervals.

[0116] See also Figure 4 , Figure 4 A flow chart of a method for setting a suppression function according to a demarcation value provided in an embodiment of the present application, the method includes but is not limited to steps S401 to S402.

[0117] Step S401 : setting a first piecewise function corresponding to a first interval, a second piecewise function corresponding to a second interval, and a third piecewise function corresponding to a third interval.

[0118] Step S402, setting the segmentation value of the first segmentation function according to the first boundary value, setting the segmentation value of the second segmentation function according to the first boundary value and the second boundary value, and setting the segmentation value of the third segmentation function according to the second boundary value to obtain a suppression function.

[0119] In steps S401 to S402 of some embodiments, in the process of setting the suppression function according to the threshold value, since the financial system has a higher probability of recommending financial products with high interaction frequencies and a lower probability of recommending financial products with low interaction frequencies, the embodiment of the present application will set a first piecewise function corresponding to the first interval, a second piecewise function corresponding to the second interval, and a third piecewise function corresponding to the third interval, and adopt differentiated suppression strategies for different intervals, and adopt piecewise functions to increase the degree of suppression on financial products with high interaction frequencies and reduce the degree of suppression on financial products with low interaction frequencies. Afterwards, the piecewise value of the first piecewise function is set according to the first threshold value, the piecewise value of the second piecewise function is set according to the first threshold value and the second threshold value, and the piecewise value of the third piecewise function is set according to the second threshold value to obtain the suppression function, thereby achieving differentiated suppression of financial products with different interaction frequencies and meeting users' needs for diversity and freshness.

[0120] Specifically, since the financial products that have been purchased more times are likely to be the most popular top financial products, if only focusing on the top few products is likely to lead to the Matthew effect, in order to avoid the Matthew effect, the embodiment of the present application adopts differentiated financial product recommendation suppression strategies for different parts of the sample sequence, adopts a piecewise function, and designs a suppression function according to the interaction frequency of the financial products, wherein the first piecewise function of the embodiment of the present application is expressed as f(c)=log(c+2), the second piecewise function is expressed as f(c)=c, and the third piecewise function is expressed as f(c)=c 0.75 +5, thus obtaining the suppression function, and setting the segmentation value of the segmentation function according to different threshold values. Taking the first threshold value of 4 and the second threshold value of 10 as an example, the suppression function is expressed as follows:

[0121]

[0122] See also Figure 5 In some embodiments, step S301 may also include but is not limited to steps S501 to S503.

[0123] Step S501, based on a preset Pareto analysis method, determine the interval in the sample sequence at a first preset ratio as the first interval, determine the interval in the sample sequence at a second preset ratio as the second interval, and determine the interval in the sample sequence at a third preset ratio as the third interval.

[0124] In step S501 of some embodiments, in the process of dividing the sample sequence into intervals based on the preset Pareto analysis method, the embodiments of the present application can determine the interval in the sample sequence that is in a first preset ratio as the first interval, determine the interval in the sample sequence that is in a second preset ratio as the second interval, and determine the interval in the sample sequence that is in a third preset ratio as the third interval based on the preset Pareto analysis method, thereby achieving effective division of the intervals, thereby being able to identify a few factors that have the greatest impact on the results.

[0125] Specifically, the first preset ratio, the second preset ratio and the third preset ratio can be set according to the needs of the user. The embodiment of the present application takes the first preset ratio of 20%, the second preset ratio of 40%, and the third preset ratio of 40% as an example for explanation. The Pareto analysis method is used to divide the top 20% of the sample sequence into a first interval, i.e., a high active interaction frequency interval, and then the 40% interval in the sample sequence is divided into a second interval, i.e., a medium active interaction frequency interval, and finally, the 40% interval in the sample sequence is divided into a third interval, i.e., a high active interaction frequency interval.

[0126] Step S502: setting a first ratio threshold and a second ratio threshold according to a preset Pareto analysis method.

[0127] Step S503, cutting the sample sequence according to the first ratio threshold and the second ratio threshold, determining the sequence portion less than the first ratio threshold as the first interval, determining the sequence portion greater than or equal to the first ratio threshold and less than the second ratio threshold as the second interval, and determining the sequence portion greater than or equal to the second ratio threshold as the third interval.

[0128] In steps S502 to S503 of some embodiments, in the process of dividing the sample sequence into intervals based on the preset Pareto analysis method, the embodiments of the present application can also set a first ratio threshold and a second ratio threshold according to the preset Pareto analysis method, and then divide the sample sequence into intervals according to the first ratio threshold and the second ratio threshold, and determine the sequence portion that is less than the first ratio preset as the first interval, the sequence portion that is greater than or equal to the first ratio threshold and less than the second ratio threshold as the second interval, and the sequence portion that is greater than or equal to the second ratio threshold as the third interval, so as to more finely distinguish the activity of user behavior, and facilitate the subsequent formulation of different personalized suppression strategies for different intervals.

[0129] Specifically, the embodiment of the present application can set two thresholds according to the Pareto analysis method, and take the first ratio threshold of 20% and the second ratio threshold of 60% as an example for explanation, and cut the sample sequence according to the first ratio threshold and the second ratio threshold, and determine the sequence portion of the sample sequence that is less than 20% as the first interval, the sequence portion of the sample sequence that is greater than or equal to 20% and less than 60% as the second interval, and the sequence portion of the sample sequence that is greater than or equal to 60% is determined as the third interval.

[0130] See also Figure 6 In some embodiments, step S106 may also include but is not limited to steps S601 to S606.

[0131] It should be noted that the triplet data includes multiple groups of data to be scored.

[0132] Step S601: for each set of scoring data in the triple data, the scoring data is input into a preset deep learning model to perform refined ranking score calculation, and the refined ranking score corresponding to the scoring data is output.

[0133] Step S602, dividing the triple data according to the first dividing value, the second dividing value and the interaction frequency to obtain a first data set, a second data set and a third data set.

[0134] Step S603, determining a first refined ranking score set corresponding to the first data set, a second refined ranking score set corresponding to the second data set, and a third refined ranking score set corresponding to the third data set.

[0135] Step S604, input the first data set into the first piecewise function, output a first rearranged score set, input the second data set into the second piecewise function, output a second rearranged score set, and input the third data set into the third piecewise function, output a third rearranged score set.

[0136] Step S605, determining a first score according to the first refined sorting score set and the first rearranged score set, determining a second score according to the second refined sorting score set and the second rearranged score set, and determining a third score according to the third refined sorting score set and the third rearranged score set.

[0137] Step S606, obtaining a scoring result according to the first score, the second score and the third score.

[0138] In steps S601 to S606 of some embodiments, in the process of calculating the score of the triple data based on the suppression function, the triple data includes multiple groups of data to be scored. For each group of scoring data in the triple data, the scoring data is first input into a preset deep learning model for fine ranking score calculation. Specifically, the deep learning model extracts the product features of the financial product corresponding to the scoring data and the user features corresponding to the user, and then constructs a feature matrix based on the product features and the user features. At this time, the trained deep learning model scores the financial products corresponding to the feature matrix and outputs the fine ranking scores corresponding to the scoring data. By integrating the multi-dimensional information of the user and the product, the accuracy of the recommendation and the user satisfaction can be improved. Afterwards, the triple data is divided according to the first demarcation value, the second demarcation value and the interaction frequency, and the data with different interaction frequencies are divided into a first data set, a second data set and a third data set. By classifying the data with different interaction frequencies, the interaction frequencies of different financial products can be accurately analyzed, so that the data sets with different interaction frequencies can be suppressed, and the differentiated suppression of different interaction frequencies can be achieved. Afterwards, the first refined ranking score set corresponding to the first data set, the second refined ranking score set corresponding to the second data set, and the third refined ranking score set corresponding to the third data set are determined through a deep learning model, so as to accurately determine the products that can be accepted by the user and the products that are most likely to be recommended to the user. The different data sets are then segmented through a suppression function. Specifically, the first data set is input into the first segmentation function, and the first rearranged score set is output. The second data set is input into the second segmentation function, and the second rearranged score set is output. The third data set is input into the third segmentation function, and the third rearranged score set is output. This can increase the degree of suppression of financial products with high interaction frequencies, reduce their probability of appearing at the top, and reduce the degree of suppression of financial products with low interaction frequencies, thereby increasing the recommendation exposure opportunities for financial products with low interaction frequencies. Afterwards, the scores of the suppressed financial products are recalculated. Specifically, according to the first refined ranking score set and the first rearranged score set, the scores of the financial products with low interaction frequencies are recalculated. The score set determines the first score. In the embodiment of the present application, the first score is obtained by dividing the first refined ranking score by the first rearranged score. Similarly, the second score is obtained by dividing the score in the second refined ranking score set by the score in the second rearranged score set, and the third score is obtained by dividing the score in the third refined ranking score set by the score in the third rearranged score set. Finally, a scoring result is obtained according to the first score, the second score and the third score, thereby realizing the re-ranking and scoring of financial products with different interaction frequencies, reducing the probability of appearance of top financial products, and increasing the probability of appearance of tail financial products, thereby meeting user needs, facilitating users to discover new products, and improving user experience.

[0139] It should be noted that the deep learning model in the embodiments of the present application includes but is not limited to a logistic regression model, a random forest model, a gradient boosting function model, etc., and the embodiments of the present application do not impose specific restrictions.

[0140] See also Figure 7 In some embodiments, step S602 may also include but is not limited to steps S701 to S704.

[0141] Step S701, comparing the interaction frequency with the first threshold value and the second threshold value.

[0142] Step S702: taking the data in the triplet data whose interaction frequency is less than the first cutoff value as the first data set.

[0143] Step S703: taking the data in the triplet data whose interaction frequency is greater than or equal to the first threshold value and less than or equal to the second threshold value as the second data set.

[0144] Step S704: taking the data in the triplet data whose interaction frequency is greater than the second cutoff value as the third data set.

[0145] In steps S701 to S704 of some embodiments, in the process of dividing the triplet data according to the first boundary value, the second boundary value and the interaction frequency, the embodiment of the present application compares the interaction frequency with the first boundary value and the second boundary value, and then takes the data in the triplet data whose interaction frequency is less than the first boundary value as the first data set, takes the data in the triplet data whose interaction frequency is greater than or equal to the first boundary value and less than or equal to the second boundary value as the second data set, and takes the data in the triplet data whose interaction frequency is greater than the second boundary value as the third data set, thereby realizing the division of data sets with different interaction frequencies, so that suppression processing can be performed on data sets with different interaction frequencies, and differentiated suppression processing of different interaction frequencies can be realized.

[0146] Specifically, taking the first dividing value as 4, the second dividing value as 10, and the interaction frequency as the historical number of interactions as an example, the data with a historical number of interactions less than 4 in the triplet data is taken as the data in the first data set, the data with a historical number of interactions greater than or equal to 4 and less than or equal to 10 in the triplet data is taken as the data in the second data set, and the data with a historical number of interactions greater than 10 in the triplet data is taken as the data in the third data set, thereby completing the division of data sets with different interaction frequencies, which is convenient for subsequent personalized processing of different data sets.

[0147] See also Figure 8 The embodiment of the present application also provides an information rearrangement device based on a financial system, the device comprising:

[0148] Product acquisition module 801, used to acquire all financial products in the financial system;

[0149] The data collection module 802 is used to collect data generated by the user's interaction with the financial product within a preset time period for each financial product to obtain sample data, wherein the sample data includes the interaction frequency;

[0150] The data generation module 803 is used to generate triplet data according to the user information, sample data and financial products of the user;

[0151] A sequence inversion module 804 is used to perform an inversion operation on the triple data based on the interaction frequency to obtain a sample sequence;

[0152] A cutoff value setting module 805 is used to set multiple cutoff values ​​according to the sample sequence and the interaction frequency, and to set a suppression function according to the cutoff values;

[0153] The score calculation module 806 is used to calculate the score of the triple data based on the suppression function to obtain the scoring result;

[0154] A target generation module 807 is used to rearrange the financial products according to the scoring results to obtain a target display sequence;

[0155] The product recommendation module 808 is used to recommend target financial products corresponding to the order of the target display sequence to the user.

[0156] The specific implementation of the information rearrangement device based on the financial system is basically the same as the specific implementation of the information rearrangement method based on the financial system described above, and will not be described in detail herein.

[0157] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned information rearrangement method based on the financial system. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0158] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0159] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0160] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the information rearrangement method based on the financial system in the embodiment of this application;

[0161] Input / output interface 903, used to implement information input and output;

[0162] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0163] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0164] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0165] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned information rearrangement method based on the financial system is implemented.

[0166] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0167] The information rearrangement method, device, electronic device and storage medium based on the financial system provided by the embodiments of the present application first obtain all financial products in the financial system, and for each financial product, collect the data generated by the user's interaction with the financial product within a preset time to obtain sample data, so that the user's recent interests and demand changes can be accurately reflected through the sample data, and then generate triple data based on the user's user information, sample data and financial products to achieve summary statistics of the data, which is convenient for subsequent analysis of the user's interaction frequency with different financial products. Thereafter, the triple data is reversed based on the interaction frequency, and the triple data can be sorted in combination with the user's active behavior and interaction frequency to obtain a sample sequence, and multiple demarcation values ​​are set according to the sample sequence and the interaction frequency, so as to identify multiple interaction frequency values ​​that affect the sorting result, which is convenient for subsequent improvement of decision quality, and a suppression function is set according to the demarcation value to target the sample sequence that is not Financial products in the same position are given different degrees of suppression, thereby increasing the degree of suppression on high-frequency financial products and reducing their probability of appearing at the head, while reducing the degree of suppression on financial products with low interaction frequency and increasing the recommendation exposure opportunities for financial products with low interaction frequency. The triple data is scored based on the suppression function to suppress financial products with higher initial scores and increase the scores of financial products with lower scores to obtain scoring results, thereby avoiding the Matthew effect and increasing the exposure opportunities of financial products at the tail of the sample sequence. Financial products are rearranged according to the scoring results to obtain the target display sequence to meet users' needs for diversity and freshness. Finally, target financial products corresponding to the order of the target display sequence are recommended to users, which can guide users to discover new products while providing personalized financial product recommendations that are more in line with user needs, increase user participation in new products, and further improve the accuracy and effectiveness of recommendations.

[0168] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0169] It can be understood by those skilled in the art that Figure 1-9 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0170] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0172] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0173] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0174] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0175] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0177] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0178] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for rearranging information based on a financial system, characterized in that: The method comprises: Access all financial products in the financial system; For each of the financial products, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, wherein the sample data includes the interaction frequency; Generate triple data according to the user information of the user, the sample data and the financial product; Performing a reverse operation on the triple data based on the interaction frequency to obtain a sample sequence; Setting a plurality of cutoff values ​​according to the sample sequence and the interaction frequency, and setting a suppression function according to the cutoff values; Calculate the score of the triple data based on the suppression function to obtain a scoring result; Rearranging the financial products according to the scoring results to obtain a target display sequence; Target financial products corresponding to the order of the target display sequence are recommended to the user.

2. The information rearrangement method based on the financial system according to claim 1, characterized in that: For each of the financial products, data generated by the user's interaction with the financial product within a preset time period is collected to obtain sample data, including: For each of the financial products, record the user's click behavior, collection behavior, and purchase behavior of the financial product within a preset time period; Collecting a first frequency of the user's clicking behavior, a second frequency of the collecting behavior, and a third frequency of the purchasing behavior; Obtaining an interaction frequency according to the first frequency, the second frequency, and the third frequency; Obtaining interactive behavior according to the clicking behavior, the collecting behavior, and the purchasing behavior; Sample data is obtained according to the interaction frequency and the interaction behavior.

3. The information rearrangement method based on the financial system according to claim 1, characterized in that: The setting of multiple demarcation values ​​according to the sample sequence and the interaction frequency includes: Dividing the sample sequence into intervals based on a preset Pareto analysis method to obtain a first interval, a second interval, and a third interval, wherein any two intersections of the first interval, the second interval, and the third interval are empty; The minimum value of the second interval is set as the first delimiting value, and the maximum value of the second interval is set as the second delimiting value.

4. The information rearrangement method based on the financial system according to claim 3, characterized in that: The step of setting a suppression function according to the demarcation value comprises: Setting a first piecewise function corresponding to the first interval, a second piecewise function corresponding to the second interval, and a third piecewise function corresponding to the third interval; The segmentation value of the first segmentation function is set according to the first boundary value, the segmentation value of the second segmentation function is set according to the first boundary value and the second boundary value, and the segmentation value of the third segmentation function is set according to the second boundary value to obtain a suppression function.

5. The information rearrangement method based on the financial system according to claim 3, characterized in that: The step of dividing the sample sequence into intervals based on a preset Pareto analysis method to obtain a first interval, a second interval, and a third interval includes: Based on a preset Pareto analysis method, determining an interval in the sample sequence at a first preset ratio as a first interval, determining an interval in the sample sequence at a second preset ratio as a second interval, and determining an interval in the sample sequence at a third preset ratio as a third interval; or, Setting a first ratio threshold and a second ratio threshold according to a preset Pareto analysis method; The sample sequence is cut according to the first ratio threshold and the second ratio threshold, a sequence portion less than the first ratio threshold is determined as a first interval, a sequence portion greater than or equal to the first ratio threshold and less than the second ratio threshold is determined as a second interval, and a sequence portion greater than or equal to the second ratio threshold is determined as a third interval.

6. The information rearrangement method based on the financial system according to claim 4, characterized in that: The triple data includes multiple groups of data to be scored; the scoring of the triple data based on the suppression function to obtain the scoring result includes: For each set of scoring data in the triplet data, inputting the scoring data into a preset deep learning model to perform refined ranking score calculation, and outputting the refined ranking score corresponding to the scoring data; Dividing the triple data according to the first dividing value, the second dividing value and the interaction frequency to obtain a first data set, a second data set and a third data set; Determine a first refined ranking score set corresponding to the first data set, a second refined ranking score set corresponding to the second data set, and a third refined ranking score set corresponding to the third data set; Input the first data set into the first piecewise function to output a first rearranged score set, input the second data set into the second piecewise function to output a second rearranged score set, and input the third data set into the third piecewise function to output a third rearranged score set; Determine a first score according to the first refined ranking score set and the first rearranged score set, determine a second score according to the second refined ranking score set and the second rearranged score set, and determine a third score according to the third refined ranking score set and the third rearranged score set; A scoring result is obtained according to the first score, the second score and the third score.

7. The information rearrangement method based on the financial system according to claim 6, characterized in that: The step of dividing the triple data according to the first dividing value, the second dividing value and the interaction frequency to obtain a first data set, a second data set and a third data set includes: comparing the interaction frequency with the first cutoff value and the second cutoff value; Taking the data in the triple data whose interaction frequency is less than the first cutoff value as the first data set; Taking the data in the triplet data whose interaction frequency is greater than or equal to the first cutoff value and less than or equal to the second cutoff value as the second data set; The data in the triplet data whose interaction frequency is greater than the second cutoff value is taken as the third data set.

8. An information rearrangement device based on a financial system, characterized in that: The device comprises: Product acquisition module, used to obtain all financial products in the financial system; A data collection module, configured to collect, for each of the financial products, data generated by the user's interaction with the financial product within a preset time period to obtain sample data, wherein the sample data includes an interaction frequency; A data generation module, used to generate triple data according to the user information of the user, the sample data and the financial product; A sequence inversion module, used for performing an inversion operation on the triple data based on the interaction frequency to obtain a sample sequence; A cutoff value setting module, used to set a plurality of cutoff values ​​according to the sample sequence and the interaction frequency, and to set a suppression function according to the cutoff values; A score calculation module, used to calculate the score of the triple data based on the suppression function to obtain a scoring result; A target generation module, used to rearrange the financial products according to the scoring results to obtain a target display sequence; The product recommendation module is used to recommend to the user target financial products corresponding to the order of the target display sequence.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the information rearrangement method based on the financial system as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the information rearrangement method based on a financial system according to any one of claims 1 to 7 is implemented.

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