Material recommendation method and device, equipment and medium

Abstract

The invention belongs to the field of big data, and relates to a material recommendation method, which comprises the following steps: acquiring session archive information between a financial management terminal and a client terminal, and extracting a keyword of the session archive information to obtain a session keyword of the session archive information; when a participation request sent by a client terminal is received, obtaining a target material putting strategy from a preset material putting library; matching the session keyword with a material keyword of the material information; if the matching is successful, generating a to-be-recommended material based on the material information, and determining delivery time based on session time and time conditions in the session archiving information; and sending the to-be-recommended material and the delivery time to the financial management terminal, so that the financial management terminal sends the to-be-recommended material to the client terminal based on the delivery time. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the session archiving information and the to-be-recommended material can be stored in a block chain. The recommendation accuracy of the to-be-recommended materials can be improved.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a material recommendation method, device, equipment and medium. Background Art

[0002] With the rapid development of the financial industry, the diversification and deepening of banking business have put forward higher requirements for customer service. Among them, financial managers have played an important role in customer marketing and service optimization. In recent years, in order to enhance customer stickiness and improve marketing efficiency, many banks have adopted instant messaging tools as the main channel for communicating with customers, and established a one-to-one personalized communication platform by adding customers as friends. Financial managers can take this opportunity to push customized financial products and service information according to the specific needs of customers. This model has become a key link in modern banking marketing.

[0003] However, the traditional instant messaging marketing method has exposed significant work efficiency bottlenecks in actual operation. Specifically, financial managers need to spend a lot of time reading and analyzing customer conversations to accurately grasp their potential needs. On this basis, they also need to manually search in the database containing massive campaign materials to match the marketing materials that best suit the customer. This process is not only extremely cumbersome and time-consuming, but also highly dependent on the personal experience and professional judgment of the financial manager, resulting in the difficulty in effectively guaranteeing the accuracy and efficiency of material push, and it is difficult to achieve accurate and efficient material push. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a material recommendation method, device, equipment and medium to solve the existing problem of difficulty in achieving accurate and efficient material push.

[0005] In order to solve the above technical problems, the present application embodiment provides a material recommendation method, which adopts the following technical solutions:

[0006] The session archive information between the financial management terminal and the client terminal is obtained, and the keywords of the session archive information are extracted to obtain the session keywords of the session archive information; when a participation request sent by the client terminal is received, the target material delivery strategy is obtained from the preset material delivery library, and the participation request is triggered by the client terminal replying to the activity invitation information of the financial management terminal, and the target material delivery strategy includes time conditions, matching rules and material information; based on the matching rules, the session keywords are matched with the material keywords of the material information; if the match is successful, the material to be recommended is generated based on the material information, and the delivery time of the material to be recommended is determined based on the session time and time conditions in the session archive information; the material to be recommended and the delivery time are sent to the financial management terminal, so that the financial management terminal sends the material to be recommended to the client terminal based on the delivery time.

[0007] Furthermore, the step of obtaining the target material delivery strategy from the preset material delivery library specifically includes:

[0008] Based on conversation keywords, a conversation scenario feature vector of conversation archive information is constructed; a preset material placement library is obtained, which includes multiple material placement strategies and a strategy scenario feature vector corresponding to each material placement strategy; the similarity between the conversation scenario feature vector and the strategy scenario feature vector is calculated to obtain a similarity result; based on the similarity result, a target material placement strategy is determined from multiple material placement strategies.

[0009] Furthermore, the matching rule includes a fuzzy matching algorithm. Based on the matching rule, the step of matching the session keyword with the material keyword of the material information specifically includes:

[0010] Extract keywords of material information to obtain material keywords of material information; calculate similarity scores between session keywords and material keywords based on fuzzy matching algorithm; if the similarity score reaches a preset threshold, it is determined that the session keywords and material keywords are successfully matched; if the similarity score is less than the threshold, it is determined that the session keywords and material keywords are not successfully matched.

[0011] Further, the step of extracting keywords from the session archive information to obtain session keywords from the session archive information specifically includes:

[0012] Preprocess the session archive information to obtain target session archive information; use a preset word segmentation algorithm to segment the target session archive information to obtain multiple word segments; calculate the weight value of each word segment according to the preset keyword extraction rule, and determine the session keyword from the multiple word segments based on the weight value.

[0013] Furthermore, the step of generating the material to be recommended based on the material information specifically includes:

[0014] A text mining algorithm is used to perform intent analysis on the target conversation archive information to obtain the conversation intent information of the customer terminal; based on the conversation intent information, product information of matching adapted products is obtained from the constructed product library; based on the product information and material information, materials to be recommended are generated.

[0015] Further, based on the session time and the time condition in the session archive information, the step of determining the release time of the to-be-recommended material specifically includes:

[0016] The session time in the session archive information is parsed to obtain the session start time and the session end time of the session time; based on the time condition, the session start time and the session end time, a preset algorithm is used to calculate the release time of the material to be recommended.

[0017] Furthermore, after the step of sending the materials to be recommended and the delivery time to the financial terminal so that the financial terminal sends the materials to be recommended to the client terminal based on the delivery time, the method further includes:

[0018] Obtain first feedback data from a client terminal on a recommended material; retrieve at least one historical material similar to the material to be recommended from a preset material delivery history database, and extract second feedback data of at least one historical material within a preset historical time period; and adjust a material delivery strategy in a material delivery library based on the first feedback data and the second feedback data.

[0019] In order to solve the above technical problems, the embodiment of the present application further provides a material recommendation device, which adopts the following technical solution:

[0020] An acquisition module is used to acquire the session archive information between the financial management terminal and the client terminal, extract keywords from the session archive information, and obtain session keywords from the session archive information;

[0021] The receiving module is used to obtain the target material delivery strategy from the preset material delivery library when receiving the participation request sent by the client terminal. The participation request is triggered by the client terminal replying to the activity invitation information of the financial terminal. The target material delivery strategy includes time conditions, matching rules and material information;

[0022] A matching module, used for matching session keywords with material keywords of material information based on matching rules;

[0023] A generation module, for generating a material to be recommended based on the material information if the match is successful, and determining a release time of the material to be recommended based on the session time and time conditions in the session archive information;

[0024] The sending module is used to send the materials to be recommended and the delivery time to the financial terminal, so that the financial terminal sends the materials to be recommended to the customer terminal based on the delivery time.

[0025] In order to solve the above technical problems, an embodiment of the present application also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the above-mentioned material recommendation method are implemented.

[0026] In order to solve the above technical problems, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-readable instructions. The computer-readable instructions can be executed by at least one processor to enable at least one processor to perform the steps of the above-mentioned material recommendation method.

[0027] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects: by obtaining the session archive information between the financial management terminal and the client terminal and accurately extracting keywords, a data basis is provided for subsequent material recommendations. When a customer responds to an activity invitation, the material delivery strategy that best matches the current session content can be quickly matched from the preset material delivery library. Through the matching rules, the precise correspondence between the session keywords and the material keywords ensures the pertinence and relevance of the materials to be recommended. At the same time, combined with the session time and the time conditions for material delivery, the optimal delivery time can be intelligently determined, thereby improving the efficiency and effect of the delivery of the materials to be recommended. Finally, the financial management terminal can accurately and timely send the materials to be recommended to the client terminal based on the provided delivery time and the materials to be recommended, thereby improving the accuracy of the recommendation of the materials to be recommended. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0030] Figure 2 It is a flow chart of a material recommendation method provided in this application;

[0031] Figure 3 It is a structural schematic diagram of a material recommendation device provided by the present application;

[0032] Figure 4 It is a structural schematic diagram of a computer device provided by this application. DETAILED DESCRIPTION

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0034] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0036] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0037] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0038] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0039] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0040] It should be noted that the material recommendation method provided in the embodiment of the present application is generally executed by a server, and accordingly, the material recommendation device is generally set in the server.

[0041] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0042] Continue to refer Figure 2 , shows a flow chart of an embodiment of a material recommendation method according to the present application. The material recommendation method comprises the following steps:

[0043] Step S201, acquiring the session archive information between the financial management terminal and the client terminal, extracting keywords from the session archive information, and obtaining session keywords from the session archive information.

[0044] In this embodiment, the electronic device (eg Figure 1 The server shown in the figure) can obtain the text to be processed through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0045] Among them, financial management terminals refer to terminal devices used to provide financial management service information, display financial management products, manage customer financial management accounts, etc. Financial management terminals are used to interact and communicate with customers related to financial management. Financial management terminals include but are not limited to smartphones, tablets, personal computers, etc.

[0046] Among them, the client terminal refers to the terminal device used by the client to receive information sent by the financial management terminal, submit financial management requests, check financial management status, etc. The client terminal includes but is not limited to smart phones, tablet computers, personal computers, etc.

[0047] The session archive information refers to the detailed record of all interactive conversations between the financial terminal and the customer terminal in the instant messaging tool. The session archive information may include but is not limited to text, voice, pictures and other information. The session archive information can be used for subsequent analysis, processing and as a basis for service improvement.

[0048] Keywords and conversation keywords refer to words or phrases with specific meanings or important values ​​extracted from conversation archive information. These keywords can summarize and reflect the main content and intention of conversation archive information. These keywords are used for subsequent matching, analysis, and recommendation.

[0049] Step S202, when receiving a participation request sent by a client terminal, obtaining a target material delivery strategy from a preset material delivery library, the participation request is triggered by the client terminal replying to the activity invitation information of the financial terminal, and the target material delivery strategy includes time conditions, matching rules and material information.

[0050] Among them, the participation request refers to a response behavior actively initiated by the client terminal based on the client's trigger operation after the client terminal receives the activity invitation information sent by the financial management terminal, indicating that the client is willing to participate in the activity and triggering the execution of the subsequent logic of releasing recommended materials.

[0051] The activity invitation information refers to the information sent by the financial terminal to the client terminal to invite the client to participate in a specific activity (such as financial product promotion, investment seminars, etc.). The activity invitation information may include key contents such as activity details and participation methods.

[0052] The material delivery library refers to a database storing a variety of material delivery strategies, which may include but are not limited to time conditions, matching rules, material information, etc. for different customer groups and different activity scenarios.

[0053] The target material delivery strategy is: based on the conditions triggered by the participation request, the material delivery strategy that best matches the current event invitation information and customer conditions is selected from the material delivery library. The target material delivery strategy may include specific time conditions, matching rules, material information, and other content.

[0054] Among them, time conditions: a set of rules or conditions defined in the target material delivery strategy, used to determine the specific delivery time of the recommended material, such as the delivery start time, end time, delivery period, etc.

[0055] Among them, matching rules: rules or algorithms used to compare and match conversation keywords with material keywords in material information to determine the correlation or matching degree between the two, which is an important basis for deciding whether to recommend a material to a customer. For example, exact matching algorithm (a match is successful only when the conversation keyword and the material keyword are exactly the same), fuzzy matching algorithm (conversation keywords and material keywords are semantically similar or related, such as using synonyms, near-synonyms or related phrases for matching), partial matching algorithm (as long as the conversation keyword contains part of the material keyword, it can be considered a match. For example, if the material keyword is "investment product" and the conversation keyword is "high-yield investment product", the partial matching rule will consider them to be matched), etc.

[0056] Among them, material information refers to the specific content of the material to be recommended to the customer as specified in the target material delivery strategy. Material information may include but is not limited to material type (such as text, picture, video, etc.), material title, material description, material keywords, etc.

[0057] Step S203: Match the conversation keywords with the material keywords of the material information based on the matching rules.

[0058] Material keywords refer to key words or phrases extracted or defined in material information to describe the content, theme or characteristics of the material. Material keywords are used to match with conversation keywords to evaluate the relevance of materials to customer needs.

[0059] In one example, if the matching rule is an exact matching algorithm, the conversation keyword and the material keyword must be exactly the same to be considered a match. If the matching rule is a fuzzy matching algorithm, the conversation keyword and the material keyword are semantically close or related, such as using synonyms, near-synonyms, or related phrases for matching. If the matching rule is a partial matching algorithm, as long as the conversation keyword contains part of the material keyword, it can be considered a match. For example, if the material keyword is "investment product" and the conversation keyword is "high-yield investment product", the partial matching rule will consider them to be matched.

[0060] Step S204: if the match is successful, the material to be recommended is generated based on the material information, and the release time of the material to be recommended is determined based on the session time and time conditions in the session archive information.

[0061] The material to be recommended refers to the specific material content selected from the material information and prepared to be recommended to the customer after the session keyword and the material keyword are successfully matched based on the matching rules.

[0062] The session time refers to the time when a specific interactive session between the financial management terminal and the customer terminal occurs, as recorded in the session archive information. The session time can be used to determine the time to release the recommended material in combination with the time condition.

[0063] The delivery time refers to the specific time point or time period when the recommended material is actually sent to the client terminal, which is determined based on the time condition and the session time. The delivery time is one of the key factors to ensure the delivery effect of the recommended material.

[0064] Step S205, sending the materials to be recommended and the delivery time to the financial management terminal, so that the financial management terminal sends the materials to be recommended to the customer terminal based on the delivery time.

[0065] In one embodiment, the data of the materials to be recommended and the delivery time can be packaged to generate a data packet, and then sent to the financial terminal through a secure communication protocol. The financial terminal can have a built-in time-sensitive distribution engine. The engine can parse the received data packet, automatically schedule the materials to be recommended according to the delivery time, and accurately push the materials to be recommended to the client terminal to achieve personalized and timely material display. Taking the promotion of a certain financial management product as an example, assuming that the material to be recommended is "Introduction to High-yield Financial Management Products", and the delivery time is set to 9 to 10 a.m. on the working day closest to the end of the session time. Through the embodiment of the present application, the material to be recommended "Introduction to High-yield Financial Management Products" and the delivery time are sent to the financial terminal. After receiving the instruction, the financial terminal pushes the material to be recommended "Introduction to High-yield Financial Management Products" to the client terminal on time from 9 to 10 a.m. on the working day closest to the end of the session.

[0066] The embodiment of the present application can obtain the conversation archive information between the financial management terminal and the client terminal, and accurately extract keywords, so as to provide a data basis for subsequent material recommendations. When a customer responds to an activity invitation, the material delivery strategy that best matches the current conversation content can be quickly matched from the preset material delivery library. Through the matching rules, the precise correspondence between the conversation keywords and the material keywords ensures the pertinence and relevance of the materials to be recommended. At the same time, combined with the conversation time and the time conditions for material delivery, the optimal delivery time can be intelligently determined, thereby improving the efficiency and effect of the delivery of the materials to be recommended. Finally, the financial management terminal can accurately and timely send the materials to be recommended to the client terminal based on the provided delivery time and the materials to be recommended, thereby improving the accuracy of the recommendation of the materials to be recommended.

[0067] In some optional implementations of this embodiment, step 201, extracting keywords of the session archive information to obtain session keywords of the session archive information, specifically includes the following steps:

[0068] Preprocess the session archive information to obtain target session archive information; use a preset word segmentation algorithm to segment the target session archive information to obtain multiple word segments; calculate the weight value of each word segment according to the preset keyword extraction rule, and determine the session keyword from the multiple word segments based on the weight value.

[0069] In one example, the session archive information can be taken as the session archive information in the financial field. First, the session archive information in the financial field is preprocessed. These session archive information may come from telephone recordings, online chat records or video conference records between customers and customer service personnel, and contain a large amount of unstructured data. The preprocessing steps include noise removal (such as silence, background music), speech recognition (converting audio to text), and data cleaning (removing irrelevant characters and standardizing formats) to obtain the target session archive information. Then, the target session archive information is segmented using a preset word segmentation algorithm. In the financial field, the word segmentation algorithm needs to consider the integrity of financial terms, such as "stock trading", "interest rate adjustment", etc., to avoid incorrect segmentation of professional vocabulary. This embodiment can use a word segmentation model based on conditional random field (CRF), which can effectively identify and retain financial professional vocabulary by training a large number of financial corpora. Subsequently, according to the preset keyword extraction rules, the weight value of each word segmentation is calculated. The rule design combines Term Frequency-Inverse Document Frequency (TF-IDF) with the knowledge graph in the financial field. It not only considers the frequency of occurrence of segmented words in the conversation, but also evaluates their importance in the financial knowledge system. For example, the word "stock" will have a higher weight value than common words such as "hello" and "thank you" in conversations that frequently discuss market dynamics. Finally, based on the weight value, the conversation keywords are determined from multiple segmented words. Accurate extraction can be achieved by setting a threshold or selecting the top N segmented words as keywords. For example, in a conversation about mortgage interest rate adjustments, keywords may include "mortgage", "interest rate", "adjustment", etc. These keywords directly reflect the core content of the session archive information.

[0070] The embodiment of the present application can remove noise and irrelevant characters by preprocessing the conversation archive information, ensuring the accuracy of subsequent word segmentation and keyword extraction. The preset word segmentation algorithm can accurately identify financial professional vocabulary and avoid the loss or misunderstanding of key information caused by word segmentation errors. By accurately extracting conversation keywords, customer needs and concerns can be understood more accurately.

[0071] In some optional implementations of this embodiment, in step S202, a target material delivery strategy is obtained from a preset material delivery library, which specifically includes the following steps:

[0072] Based on conversation keywords, a conversation scenario feature vector of conversation archive information is constructed; a preset material placement library is obtained, which includes multiple material placement strategies and a strategy scenario feature vector corresponding to each material placement strategy; the similarity between the conversation scenario feature vector and the strategy scenario feature vector is calculated to obtain a similarity result; based on the similarity result, a target material placement strategy is determined from multiple material placement strategies.

[0073] In one example, the session archive information can be taken as the session archive information in the financial field. After preprocessing the session archive information, including removing noise, segmenting, and extracting keywords, each extracted session keyword is encoded as a unique digital identifier according to a preset vocabulary. Then, according to the occurrence of each session keyword, a session scenario feature vector is constructed. The preset material delivery library contains multiple material delivery strategies and corresponding strategy scenario feature vectors. The similarity between the session scenario feature vector and the strategy scenario feature vector is calculated to obtain a similarity result. Common methods such as cosine similarity and Euclidean distance can be used for similarity calculation. According to the similarity result, the strategy that best matches the target session scenario is selected from multiple material delivery strategies as the target material delivery strategy.

[0074] The embodiment of the present application can accurately capture the needs and preferences expressed by customers in the conversation by extracting conversation keywords and constructing conversation scene feature vectors. This keyword-based feature extraction method can deeply explore customer intentions and provide strong support for the selection of subsequent material delivery strategies. At the same time, similarity calculation with the strategy scene feature vectors in the preset material delivery library can further ensure the accurate match between the material delivery strategy and customer needs. Traditional material delivery methods often rely on manual judgment and experience-based decision-making, which are inefficient and difficult to ensure accuracy. The solution provided in this embodiment can improve the efficiency of material delivery through automated feature extraction, similarity calculation and strategy selection processes. A large amount of conversation archive information can be processed and analyzed in a short period of time, and the target material delivery strategy can be quickly determined, thereby achieving timely response and effective satisfaction of customer needs.

[0075] In some optional implementations of this embodiment, the matching rule includes a fuzzy matching algorithm. Step S203, based on the matching rule, matches the conversation keyword with the material keyword of the material information, specifically including the following steps:

[0076] Extract keywords of material information to obtain material keywords of material information; calculate similarity scores between session keywords and material keywords based on fuzzy matching algorithm; if the similarity score reaches a preset threshold, it is determined that the session keywords and material keywords are successfully matched; if the similarity score is less than the threshold, it is determined that the session keywords and material keywords are not successfully matched.

[0077] In one example, key information, such as the name, type, interest rate, and term of a financial product, can be extracted from the material information as material keywords. A fuzzy matching algorithm is used to calculate the similarity score between the session keywords and the material keywords in the session archive information. The fuzzy matching algorithm can consider factors such as semantic similarity and spelling errors of keywords to improve the accuracy of matching. A preset threshold can be set. If the similarity score reaches or exceeds the threshold, it is determined that the session keyword and the material keyword are matched successfully, and the corresponding material delivery strategy is triggered. If the similarity score is lower than the threshold, it is determined that the match is not successful, and the corresponding material delivery strategy is not triggered.

[0078] The embodiment of the present application can more accurately identify the content related to material information in the user session by extracting the keywords of the material information and performing fuzzy matching with the conversation keywords. The application of the fuzzy matching algorithm makes it possible to match the conversation keywords based on the similarity between the conversation keywords and the material keywords even if the conversation keywords of the session archive information are not completely consistent with the material keywords in the material information, thereby improving the accuracy of the match. The application of the fuzzy matching algorithm enables the solution provided by this embodiment to cope with a variety of user conversations and expressions of material information. Whether it is concise words or complex sentences entered by the customer, effective keyword extraction and matching can be performed.

[0079] In some optional implementations of this embodiment, step S204, generating materials to be recommended based on the material information, specifically includes the following steps:

[0080] A text mining algorithm is used to perform intent analysis on the target conversation archive information to obtain the conversation intent information of the customer terminal; based on the conversation intent information, product information of matching adapted products is obtained from the constructed product library; based on the product information and material information, materials to be recommended are generated.

[0081] In one example, a text mining algorithm (such as TF-IDF, Word2Vec, or BERT, etc.) can be used to perform intent analysis on the target session archive information to identify the customer's session intent information such as inquiry, consultation, investment intention, etc. about financial products. According to the session intent information, the product information of the adapted product matching the customer's needs is retrieved from the constructed financial product library (including but not limited to financial products, loan products, insurance products, etc.). The material to be recommended is generated by combining the material information and the product information of the adapted product. For example, the BERT text mining algorithm can be used to perform intent analysis on the target session archive information of the client terminal to identify the query intention of the customer of the client terminal for short-term, low-risk financial products. According to the identified session intent, financial products that meet the "short-term, low-risk" conditions are screened from the financial product library, such as a certain money market fund or a short-term fixed-term financial product. The product information and material information of the financial products that meet the "short-term, low-risk" conditions are combined to generate the material to be recommended.

[0082] The embodiment of the present application can quickly understand customer needs by accurately analyzing the conversation intention information of the customer terminal, thereby providing more personalized material recommendations. Customers do not need to screen among a large number of products by themselves, saving time and energy.

[0083] In some optional implementations of this embodiment, in step S204, based on the session time and the time condition in the session archive information, determining the release time of the to-be-recommended material specifically includes the following steps:

[0084] The session time in the session archive information is parsed to obtain the session start time and the session end time of the session time; based on the time condition, the session start time and the session end time, a preset algorithm is used to calculate the release time of the material to be recommended.

[0085] In one example, the start time and end time of the session corresponding to the session archive information can be extracted. The time condition is set to 9:00 to 10:00 a.m. on the working day closest to the session end time. Then, a preset algorithm (e.g., a time window algorithm) is used to calculate a reasonable time range based on the session end time, so that the time range falls within the range of 9:00 to 10:00 a.m. on the working day closest to the session end time.

[0086] The embodiment of the present application can understand when the customer consulted or inquired by analyzing the session time, so as to more accurately grasp the customer's interests and needs. Combined with the preset algorithm, the recommended materials can be placed in the time period when the customer is most likely to pay attention to or need product information, thereby improving the accuracy and effectiveness of the recommendation.

[0087] In some optional implementations of this embodiment, in step S205, after the recommended material and the delivery time are sent to the financial terminal so that the financial terminal sends the recommended material to the client terminal based on the delivery time, the following steps are specifically included:

[0088] Obtain first feedback data from a client terminal on a recommended material; retrieve at least one historical material similar to the material to be recommended from a preset material delivery history database, and extract second feedback data of at least one historical material within a preset historical time period; and adjust a material delivery strategy in a material delivery library based on the first feedback data and the second feedback data.

[0089] In one example, after the client terminal receives the material to be recommended, the first feedback data of the client terminal can be obtained. Historical materials similar to the material to be recommended to the client terminal (such as the time deposit product details page and fund investment guide previously recommended to other client terminals) are retrieved from the material delivery history database, and the second feedback data (such as click-through rate, conversion rate, etc.) of these historical materials within a preset historical time period are extracted. Based on the first feedback data of the client terminal and the second feedback data of similar historical materials, the material delivery strategy in the material delivery library is adjusted, such as adjusting the time conditions, optimizing the matching rules, improving the material information, etc., to improve the accuracy and effect of subsequent material recommendations.

[0090] The first feedback data of the embodiment of the present application directly reflects the immediate response of the client terminal to the current recommended material. These data provide a direct basis for evaluating the recommendation effect. Based on the comprehensive analysis of the first feedback data and the second feedback data, the material delivery strategy can be adjusted in time, such as adjusting the time conditions, optimizing the matching rules, and improving the content of the material information, thereby improving the accuracy and effect of the recommendation.

[0091] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned session archive information, session keywords, material placement strategies in the material placement library, and materials to be recommended, the above-mentioned session archive information, session keywords, material placement strategies in the material placement library, and materials to be recommended can also be stored in a blockchain node.

[0092] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0093] The embodiment of the present application can acquire and process the session archive information, session keywords, target material delivery strategy, material keywords, session time, material information, and materials to be recommended based on the relevant algorithms of natural language processing (NLP) technology under artificial intelligence. 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.

[0094] Natural language processing technology is a key branch in the field of artificial intelligence. It focuses on giving computers the ability to understand and process human language, so that they can parse, understand and generate natural language text like humans. By simulating some core mechanisms of human language processing, NLP technology can deeply process, analyze and interpret text data, extract key information, understand semantic content, and perform complex tasks or generate natural language responses based on it. Natural language processing technology can be used to perform keyword extraction processing on session archive information to generate session keywords for session archive information, perform keyword extraction processing on material information to generate material keywords, generate materials to be recommended based on material information, and generate delivery time based on session time and time conditions.

[0095] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0096] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0097] Further references Figure 3 , as a response to the above Figure 2 The present application provides an embodiment of a material recommendation device, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0098] like Figure 3 As shown, the material recommendation device 400 of this embodiment includes: an acquisition module 401, a receiving module 402, a matching module 403, a generation module 404 and a sending module 405. Among them:

[0099] The acquisition module 401 is used to acquire the session archive information between the financial management terminal and the client terminal, extract the keywords of the session archive information, and obtain the session keywords of the session archive information;

[0100] The receiving module 402 is used to obtain a target material delivery strategy from a preset material delivery library when receiving a participation request sent by a client terminal, wherein the participation request is triggered by the client terminal replying to the activity invitation information of the financial management terminal, and the target material delivery strategy includes time conditions, matching rules and material information;

[0101] A matching module 403, used for matching the conversation keywords with the material keywords of the material information based on the matching rules;

[0102] A generating module 404 is used to generate a material to be recommended based on the material information if the match is successful, and determine the delivery time of the material to be recommended based on the session time and the time condition in the session archive information;

[0103] The sending module 405 is used to send the materials to be recommended and the delivery time to the financial terminal, so that the financial terminal sends the materials to be recommended to the client terminal based on the delivery time.

[0104] The embodiment of the present application can obtain the conversation archive information between the financial management terminal and the client terminal, and accurately extract keywords, so as to provide a data basis for subsequent material recommendations. When a customer responds to an activity invitation, the material delivery strategy that best matches the current conversation content can be quickly matched from the preset material delivery library. Through the matching rules, the precise correspondence between the conversation keywords and the material keywords ensures the pertinence and relevance of the materials to be recommended. At the same time, combined with the conversation time and the time conditions for material delivery, the optimal delivery time can be intelligently determined, thereby improving the efficiency and effect of the delivery of the materials to be recommended. Finally, the financial management terminal can accurately and timely send the materials to be recommended to the client terminal based on the provided delivery time and the materials to be recommended, thereby improving the accuracy of the recommendation of the materials to be recommended.

[0105] In one embodiment, the acquisition module 401 includes:

[0106] The preprocessing submodule is used to preprocess the session archive information to obtain the target session archive information;

[0107] The word segmentation submodule is used to segment the target session archive information using a preset word segmentation algorithm to obtain multiple word segments;

[0108] The weight calculation submodule is used to calculate the weight value of each segmented word according to the preset keyword extraction rules, and determine the conversation keyword from multiple segmented words based on the weight value.

[0109] The embodiment of the present application can remove noise and irrelevant characters by preprocessing the conversation archive information, ensuring the accuracy of subsequent word segmentation and keyword extraction. The preset word segmentation algorithm can accurately identify financial professional vocabulary and avoid the loss or misunderstanding of key information caused by word segmentation errors. By accurately extracting conversation keywords, customer needs and concerns can be understood more accurately.

[0110] In one embodiment, the receiving module 402 includes:

[0111] A construction submodule is used to construct a conversation scenario feature vector of conversation archive information based on conversation keywords;

[0112] The first acquisition submodule is used to acquire a preset material delivery library, which includes multiple material delivery strategies and a strategy scenario feature vector corresponding to each material delivery strategy;

[0113] A similarity calculation submodule is used to calculate the similarity between the conversation scenario feature vector and the strategy scenario feature vector to obtain a similarity result;

[0114] The determination submodule is used to determine the target material delivery strategy from multiple material delivery strategies based on the similarity results.

[0115] The embodiment of the present application can accurately capture the needs and preferences expressed by customers in the conversation by extracting conversation keywords and constructing conversation scene feature vectors. This keyword-based feature extraction method can deeply explore customer intentions and provide strong support for the selection of subsequent material delivery strategies. At the same time, similarity calculation with the strategy scene feature vectors in the preset material delivery library can further ensure the accurate match between the material delivery strategy and customer needs. Traditional material delivery methods often rely on manual judgment and experience-based decision-making, which are inefficient and difficult to ensure accuracy. The solution provided in this embodiment can improve the efficiency of material delivery through automated feature extraction, similarity calculation and strategy selection processes. A large amount of conversation archive information can be processed and analyzed in a short period of time, and the target material delivery strategy can be quickly determined, thereby achieving timely response and effective satisfaction of customer needs.

[0116] In one embodiment, the matching module 403 includes:

[0117] The extraction submodule is used to extract the keywords of the material information and obtain the material keywords of the material information;

[0118] The score calculation submodule is used to calculate the similarity scores between the conversation keywords and the material keywords based on the fuzzy matching algorithm;

[0119] The first determination submodule is used to determine that the conversation keyword and the material keyword are matched successfully if the similarity score reaches a preset threshold;

[0120] The second determination submodule is used to determine that the conversation keyword and the material keyword are not matched successfully if the similarity score is less than a threshold.

[0121] The embodiment of the present application can more accurately identify the content related to material information in the user session by extracting the keywords of the material information and performing fuzzy matching with the conversation keywords. The application of the fuzzy matching algorithm makes it possible to match the conversation keywords based on the similarity between the conversation keywords and the material keywords even if the conversation keywords of the session archive information are not completely consistent with the material keywords in the material information, thereby improving the accuracy of the match. The application of the fuzzy matching algorithm enables the solution provided by this embodiment to cope with a variety of user conversations and expressions of material information. Whether it is concise words or complex sentences entered by the customer, effective keyword extraction and matching can be performed.

[0122] In one embodiment, the generating module 404 includes:

[0123] The analysis submodule is used to use a text mining algorithm to perform intent analysis on the target session archive information to obtain the session intent information of the client terminal;

[0124] The second acquisition submodule is used to acquire product information of matching adapted products from the constructed product library according to the session intention information;

[0125] The generation submodule is used to generate materials to be recommended based on product information and material information.

[0126] The embodiment of the present application can quickly understand customer needs by accurately analyzing the conversation intention information of the customer terminal, thereby providing more personalized material recommendations. Customers do not need to screen among a large number of products by themselves, saving time and energy.

[0127] In one embodiment, the generating module 404 includes:

[0128] A parsing submodule is used to parse the session time in the session archive information to obtain the session start time and session end time of the session time;

[0129] The time calculation submodule is used to calculate the delivery time of the recommended material based on the time condition, the session start time and the session end time using a preset algorithm.

[0130] The embodiment of the present application can understand when the customer consulted or inquired by analyzing the session time, so as to more accurately grasp the customer's interests and needs. Combined with the preset algorithm, the recommended materials can be placed in the time period when the customer is most likely to pay attention to or need product information, thereby improving the accuracy and effectiveness of the recommendation.

[0131] In one embodiment, the material recommendation device 400 further includes:

[0132] A data acquisition module, used to acquire first feedback data of the client terminal on the recommended material;

[0133] A retrieval module is used to retrieve at least one historical material similar to the material to be recommended from a preset material delivery history database, and extract second feedback data of at least one historical material within a preset historical time period;

[0134] The adjustment module is used to adjust the material delivery strategy in the material delivery library based on the first feedback data and the second feedback data.

[0135] The first feedback data of the embodiment of the present application directly reflects the immediate response of the client terminal to the current recommended material. These data provide a direct basis for evaluating the recommendation effect. Based on the comprehensive analysis of the first feedback data and the second feedback data, the material delivery strategy can be adjusted in time, such as adjusting the time conditions, optimizing the matching rules, and improving the content of the material information, thereby improving the accuracy and effect of the recommendation.

[0136] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0137] The computer device 6 includes a memory 61, a processor 62, and a network interface 63 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 6 having a memory 61, a processor 62, and a network interface 63, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital signal processors (DSP), embedded devices, etc.

[0138] Computer devices can be computing devices such as desktop computers, notebooks, PDAs, and cloud servers. Computer devices can interact with users through keyboards, mice, remote controls, touch pads, or voice control devices.

[0139] The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 61 can be an internal storage unit of the computer device 6, such as a hard disk or memory of the computer device 6. In other embodiments, the memory 61 can also be an external storage device of the computer device 6, such as a plug-in hard disk equipped on the computer device 6, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 61 can also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6, such as computer-readable instructions of the material recommendation method, etc. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or are to be output.

[0140] The processor 62 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 62 is generally used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is used to run computer-readable instructions stored in the memory 61 or process data, such as computer-readable instructions for running the material recommendation method.

[0141] The network interface 63 may include a wireless network interface or a wired network interface, and the network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

[0142] The embodiment of the present application can obtain the conversation archive information between the financial management terminal and the client terminal, and accurately extract keywords, so as to provide a data basis for subsequent material recommendations. When a customer responds to an activity invitation, the material delivery strategy that best matches the current conversation content can be quickly matched from the preset material delivery library. Through the matching rules, the precise correspondence between the conversation keywords and the material keywords ensures the pertinence and relevance of the materials to be recommended. At the same time, combined with the conversation time and the time conditions for material delivery, the optimal delivery time can be intelligently determined, thereby improving the efficiency and effect of the delivery of the materials to be recommended. Finally, the financial management terminal can accurately and timely send the materials to be recommended to the client terminal based on the provided delivery time and the materials to be recommended, thereby improving the accuracy of the recommendation of the materials to be recommended.

[0143] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions. The computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the material recommendation method as described above.

[0144] The embodiment of the present application can obtain the conversation archive information between the financial management terminal and the client terminal, and accurately extract keywords, so as to provide a data basis for subsequent material recommendations. When a customer responds to an activity invitation, the material delivery strategy that best matches the current conversation content can be quickly matched from the preset material delivery library. Through the matching rules, the precise correspondence between the conversation keywords and the material keywords ensures the pertinence and relevance of the materials to be recommended. At the same time, combined with the conversation time and the time conditions for material delivery, the optimal delivery time can be intelligently determined, thereby improving the efficiency and effect of the delivery of the materials to be recommended. Finally, the financial management terminal can accurately and timely send the materials to be recommended to the client terminal based on the provided delivery time and the materials to be recommended, thereby improving the accuracy of the recommendation of the materials to be recommended.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0146] The non-Company software tools or components appearing in the embodiments of this application are merely examples and do not represent actual use.

[0147] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A material recommendation method, characterized in that: The steps include: Acquire the session archive information between the financial management terminal and the client terminal, extract the keywords of the session archive information, and obtain the session keywords of the session archive information; When receiving a participation request sent by the client terminal, obtaining a target material delivery strategy from a preset material delivery library, wherein the participation request is triggered by the client terminal replying to the activity invitation information of the financial management terminal, and the target material delivery strategy includes time conditions, matching rules and material information; Based on the matching rule, matching the conversation keyword with the material keyword of the material information; If the match is successful, the material to be recommended is generated based on the material information, and the release time of the material to be recommended is determined based on the session time in the session archive information and the time condition; The materials to be recommended and the delivery time are sent to the financial management terminal, so that the financial management terminal sends the materials to be recommended to the customer terminal based on the delivery time.

2. The method according to claim 1, characterized in that: The step of obtaining the target material delivery strategy from the preset material delivery library specifically includes: Based on the conversation keywords, construct a conversation scenario feature vector of the conversation archive information; Obtaining a preset material delivery library, wherein the material delivery library includes multiple material delivery strategies and a strategy scenario feature vector corresponding to each material delivery strategy; Calculating the similarity between the conversation scenario feature vector and the strategy scenario feature vector to obtain a similarity result; According to the similarity result, a target material delivery strategy is determined from the multiple material delivery strategies.

3. The method according to claim 1, characterized in that The matching rule includes a fuzzy matching algorithm. The step of matching the conversation keyword with the material keyword of the material information based on the matching rule specifically includes: Extract keywords of the material information to obtain material keywords of the material information; Calculate the similarity score between the conversation keyword and the material keyword according to the fuzzy matching algorithm; If the similarity score reaches a preset threshold, it is determined that the conversation keyword matches the material keyword successfully; If the similarity score is less than the threshold, it is determined that the conversation keyword and the material keyword are not matched successfully.

4. The method according to claim 1, characterized in that: The step of extracting keywords from the session archive information to obtain session keywords from the session archive information specifically includes: Preprocessing the session archive information to obtain target session archive information; Using a preset word segmentation algorithm to segment the target conversation archive information to obtain a plurality of word segments; According to the preset keyword extraction rules, the weight value of each word segment is calculated, and based on the weight value, the conversation keyword is determined from the multiple word segments.

5. The method according to claim 4, characterized in that The step of generating the material to be recommended based on the material information specifically includes: Using a text mining algorithm to perform intent analysis on the target session archive information to obtain session intent information of the client terminal; According to the session intention information, obtain product information of matching adapted products from the constructed product library; Based on the product information and the material information, a material to be recommended is generated.

6. The method according to claim 1, characterized in that The step of determining the release time of the to-be-recommended material based on the session time in the session archive information and the time condition specifically includes: Parsing the session time in the session archive information to obtain the session start time and the session end time of the session time; Based on the time condition, the session start time and the session end time, a preset algorithm is used to calculate the delivery time of the to-be-recommended material.

7. The method according to claim 1, characterized in that After the step of sending the to-be-recommended material and the delivery time to the financial management terminal so that the financial management terminal sends the to-be-recommended material to the client terminal based on the delivery time, the step further includes: Acquire first feedback data of the client terminal on the material to be recommended; Retrieving at least one historical material similar to the material to be recommended from a preset material delivery history database, and extracting second feedback data of the at least one historical material within a preset historical time period; Based on the first feedback data and the second feedback data, the material delivery strategy in the material delivery library is adjusted.

8. A material recommendation device, characterized in that: include: An acquisition module, used to acquire the session archive information between the financial management terminal and the client terminal, extract keywords from the session archive information, and obtain session keywords from the session archive information; A receiving module, configured to obtain a target material delivery strategy from a preset material delivery library when receiving a participation request sent by the client terminal, wherein the participation request is triggered by the client terminal replying to the activity invitation information of the financial management terminal, and the target material delivery strategy includes a time condition, a matching rule and material information; A matching module, configured to match the conversation keyword with a material keyword of the material information based on the matching rule; A generating module, configured to generate a material to be recommended based on the material information if the match is successful, and determine a release time of the material to be recommended based on the session time in the session archive information and the time condition; The sending module is used to send the to-be-recommended material and the delivery time to the financial management terminal, so that the financial management terminal sends the to-be-recommended material to the customer terminal based on the delivery time.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the material recommendation method as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the material recommendation method according to any one of claims 1 to 7 are implemented.

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