Personalized intelligent recommendation system and method for financial knowledge table

Abstract

The invention discloses a personalized intelligent recommendation system and method for a financial knowledge platform, and belongs to the technical field of financial systems, and the system comprises a server burying point collection module which collects user key financial behavior event data after business logic processing; the service attribute enhancement module is used for adding a fine service context attribute set to the behavior event; the multi-modal user portrait construction module associates and fuses real-time behaviors and historical business data of the user through a security interface, and generates dynamic user feature vectors from multiple dimensions such as investment specialty and market insight ability; the tensor decomposition recommendation engine is used for modeling the user-article interaction relationship into a four-dimensional tensor containing users, products, specialty and insight, and calculating preference prediction scores through a fusion function to generate a recommendation list; according to the method, on the premise of guaranteeing data security and consistency, personalized recommendation which is highly accurate and conforms to professional ability and preference of financial users is provided for the financial users.

Description

Technical Field

[0001] This invention relates to the field of financial system technology, and in particular to a personalized intelligent recommendation system and method for a financial knowledge platform. Background Technology

[0002] With the rapid development of fintech, financial institutions have accumulated massive amounts of user behavior and business data. To enhance user experience, increase customer loyalty, and accurately match financial products and services, personalized recommendation systems have become a core component of financial knowledge platforms. However, due to the high accuracy, high security, and high complexity inherent in financial transactions, directly applying general-domain recommendation technologies to financial scenarios faces numerous challenges, and existing technologies have significant shortcomings, primarily in the following aspects: Insufficient reliability and business value of behavioral data collection: Existing recommendation systems largely rely on front-end event tracking to collect user behavior data, such as page clicks and browsing time. In financial scenarios, this approach has serious flaws. First, front-end data is highly susceptible to loss, distortion, or inconsistency due to network transmission anomalies, client-side caching, or malicious tampering, failing to meet the stringent requirements of financial transactions for data accuracy and transaction consistency. Second, traditional event tracking often only captures superficial interaction events, lacking the collection of high-value core financial business events such as transaction orders, risk assessments, and portfolio changes. The lack of this data, which truly reflects users' financial intentions and decision-making outcomes, hinders subsequent analysis and prevents the accurate depiction of users' financial behavior profiles. User profiling models are simplistic and fail to capture the true characteristics of financial professionals: Existing user profiles are mostly built upon general entertainment behaviors such as browsing and clicking, and the tagging system is limited to superficial dimensions such as interests and demographic attributes. In the financial field, a user's investment expertise, such as their understanding and analytical abilities regarding financial instruments and their market insight, as well as their judgment and reaction speed regarding information, are key intrinsic qualities influencing their decision-making. Current technologies lack quantitative assessment methods and models for these deep, professional dimensions, making it impossible to distinguish between a seasoned professional investor and an ordinary financial user, thus hindering the provision of truly differentiated, personalized content tailored to their cognitive level and decision-making ability. Recommendation algorithms are disconnected from the financial business context: Mainstream recommendation algorithms such as collaborative filtering and content filtering are mostly based on a two-dimensional "user-item" interaction matrix. This model is too crude in the financial context. It fails to take into account key financial dimensions such as the user's professional capabilities, risk preferences, and real-time market conditions. For example, it cannot determine whether an in-depth macroeconomic research report is suitable for a user's current professional understanding, nor can it recommend a high-risk financial product to a risk-averse user. Their recommendations are often superficial, lack financial logic, and may even mislead users into making inappropriate investment decisions.

[0003] Existing technologies suffer from problems such as insufficient reliability and business value in behavioral data collection, a single user profile model that fails to depict the characteristics of financial professionals, and a disconnect between recommendation algorithms and the context of financial business, resulting in very low applicability of existing recommendation methods in financial knowledge platforms. Summary of the Invention

[0004] To address the aforementioned issues, this invention aims to resolve the problems described above, such as insufficient reliability and business value in behavioral data collection, the simplistic user profile models failing to depict financial professional characteristics, and the disconnect between recommendation algorithms and financial business context. One objective of this invention is to provide a personalized intelligent recommendation system and method for a financial knowledge platform that solves the problems mentioned above. This system can ensure the accuracy and reliability of financial behavioral data from the data source, deeply mine and quantify users' core financial characteristics, and integrate the complex context of financial business into the recommendation model. Ultimately, while ensuring security and compliance, it provides users with truly accurate, reliable, and valuable personalized financial information services.

[0005] The solution adopted in this invention is: a personalized intelligent recommendation system for a financial knowledge platform, comprising: a server-side data collection module configured to collect high-value financial behavior event data of users on the server side after the completion of key business logic processing in the core business system, to ensure strong consistency between data and business transactions. The behavior events include at least account opening, transaction entrustment, purchase of wealth management products, changes in holdings, submission of risk assessments, and collection of knowledge articles. A business attribute enhancement module, communicatively connected to the server-side data collection module, is used to attach a structured set of business context attributes to each behavior event; wherein, for the "purchase of wealth management products" event, the attribute set includes product type, amount, term, risk level, and expected rate of return; for the "browsing knowledge articles" event, the attribute set includes the financial topic to which the article belongs, the level of professional difficulty, and the author's authority. A multimodal user profile building module is used to receive and process enhanced user behavior data. It associates data with the customer relationship management system and the transaction system through a secure data mapping interface, integrates users' real-time interactive behavior with historical asset allocation and transaction records, and generates dynamic user feature vectors from multiple dimensions such as investment professionalism, market insight ability, risk preference and liquidity preference based on a preset quantitative model. A four-dimensional tensor decomposition recommendation engine models the user-item interaction relationship as a four-dimensional tensor, where the four dimensions include the user, financial knowledge product, user investment expertise, and user market insight capability. The final user preference prediction score for specific knowledge content or financial products is calculated through a fusion function. This function includes: a first interaction component, which is the dot product of the user's latent semantic vector and the knowledge content's latent semantic vector, used to measure the general preference matching degree between the user and the content; a second capability matching component, which is the dot product of the user's investment expertise latent semantic vector and the market insight capability latent semantic vector, used to measure the degree of matching between the user's comprehensive capability traits and the level of expertise and insight required or implied by the target content; and a global bias term, used to correct overall biases in the data. The preference prediction score is the sum of the first interaction component, the second capability matching component, and the global bias term. Based on the user's preference prediction score for the target content, a personalized recommendation list is generated. A reinforcement learning and feedback calibration module is configured to accurately collect and distinguish between the "exposure" event of recommended content and the user's subsequent "conversion" behavior event. The conversion behavior is used as a reinforcement learning signal to dynamically adjust the model parameters of the tensor decomposition recommendation engine. A built-in periodic calibration mechanism is also included to optimize the weight allocation of the evaluation model in the multimodal user profile construction module by comparing user behavior sequences with actual market performance data.

[0006] The preferred technical solution is that when the server-side data collection module collects event data, it is triggered by listening to transaction logs or embedding in the business logic layer to ensure that data collection and business operations are in the same database transaction or have eventual consistency, thereby fundamentally avoiding data distortion caused by front-end data tampering, loss or network anomalies.

[0007] A preferred technical solution is that the multimodal user profile construction module further includes: an investment professionalism assessment submodule, which uses natural language processing technology to analyze the text entered by users in search queries, online consultations, and community discussions, statistically analyzes the frequency, accuracy, and contextual complexity of specific financial professional terms and regulations, and analyzes the user's data query logic, depth of use of advanced analysis tools, and complexity of simulated trading strategies, outputting a quantitative professionalism index; a market insight assessment submodule, which uses time series analysis to monitor users' access patterns to different types of information streams, including reaction delay time, reading dwell time, and subsequent operational behavior to breaking news, macro data, and company financial reports, and quantifies the accuracy of their insight by recording the degree of consistency between users' predicted views and actual market conditions, outputting a quantitative insight index; and a comprehensive profile fusion submodule, which uses a machine learning model, taking the professionalism index, insight index, user risk assessment results, and historical transaction data characteristics as input, and outputting a comprehensive multidimensional user profile vector.

[0008] The specific evaluation dimensions of the investment professionalism assessment submodule include: Knowledge structure depth: assessed through the user's complete reading rate of in-depth research reports, repeated viewing patterns of professional teaching videos, and accuracy rate in answering online professional tests. Decision-making behavior quality: assessed by analyzing the user's historical portfolio's Sharpe ratio, maximum drawdown, portfolio diversification indicators, and stop-loss discipline demonstrated in simulated or actual trading. Community contribution value: assessed by calculating the user's adoption rate of answers provided in the Q&A community, the number of likes, and the positive sentiment polarity of their published content.

[0009] The preferred technical solution involves the following specific evaluation methods for the market insight capability assessment submodule: Information value discrimination capability: By analyzing the distribution of information topics of interest to users, the system distinguishes the proportion of their preference for market noise-related information versus high-value fundamental information. Behavioral leadership indicator: By calculating the difference between the time of a user's first relevant action after a significant market event and the time of the event itself, the smaller the difference, the higher the leadership indicator score. Prediction verification system: A subsystem is established to anonymously collect market prediction opinions voluntarily submitted by users and automatically compare them with actual market data after a preset time point, generating an objective accuracy report.

[0010] The reinforcement learning and feedback calibration module further includes: a multi-armed bandit exploration strategy submodule, used to explore new or less popular content that users may be interested in with a certain probability during the recommendation process, in order to balance exploration and utilization in recommendations; a model hot update submodule, supporting incremental updates to the recommendation model based on recent user feedback streaming data to quickly capture user interest drift; and an expert review loop, which periodically extracts user profile data and recommendation results, has domain experts review and correct them, and feeds the correction results back to the model training process as a supervision signal, forming a hybrid closed-loop optimization of human-machine collaboration.

[0011] The preferred technical solution is that the system further includes: a privacy computing and security compliance gateway, integrated into the critical path of data flow, which performs real-time encryption and desensitization processing on all collected raw behavioral data and generated user profile feature vectors; during the data access phase, it supports model training and recommendation calculation without exposing the original data through federated learning or differential privacy technology, and performs tamper-proof log auditing on all data access operations to ensure compliance with financial data security regulations.

[0012] This invention also provides a personalized intelligent recommendation method for a financial knowledge platform, the method comprising the following steps: S10, server-side data collection step: on the financial service end, after the core business logic processing is completed and the database transaction is completed, key financial behavior event data of users is collected by listening to the business system transaction log or calling a dedicated API interface. The behavior events include at least account opening, transaction order, purchase of wealth management products, changes in holdings, submission of risk assessment, and collection of knowledge articles; S20, business context attribute attachment step: structured business context attributes are attached to each behavior event to generate user behavior with multi-dimensional tags. The dataset includes attributes for the "purchase of financial products" event, such as product type, amount, term, risk level, and expected rate of return; and attributes for the "browsing of knowledge articles" event, such as the financial topic of the article and its level of professional difficulty. S30, User Data Fusion Step: Connecting front-end behavioral data with the user identifier mapping of the back-end core business system, linking real-time user interaction behavior with historical asset allocation and transaction records. S40, Multi-Dimensional User Profile Construction Step: Processing the user behavior dataset to construct a quantitative user characteristic profile from multiple financial dimensions, including investment expertise, market insight, risk preference, and liquidity preference. This step includes:

[0013] S41, Investment Professionalism Assessment Sub-step: Utilizing natural language processing technology to analyze the frequency, accuracy, and contextual complexity of professional terms in user text, this sub-step assesses the depth of data queries and the use of advanced analytical tools. The specific execution process includes: Knowledge Depth Assessment: Analyzing user consultation language, search queries, completion rate of in-depth research reports, and performance in professional tests; Decision-Making Behavior Analysis: Tracking user simulated or actual investment operation records to assess the consistency of their decision-making logic, risk control capabilities, and referencing historical Sharpe ratio indicators; Interaction and Contribution Quality Analysis: Analyzing the quality, accuracy, and adoption rate of information provided by users in community discussions and Q&A sections.

[0014] S42, Market Insight Ability Assessment Sub-step: Analyze the types of information users pay attention to using time series analysis technology, monitor their reaction speed to market events, and verify the accuracy of their historical forecasts; the specific execution process includes: Market Information Processing Pattern Analysis: Monitor the types of information users pay attention to, such as breaking news, macroeconomic data, and company financial reports, and their reaction speed; Prediction and Judgment Verification: Analyze the market views published by users, their prediction records, and how these predictions are subsequently verified by the market; Data Analysis and Application Ability Assessment: Assess the user's ability to use financial models to conduct customized analysis and extract conclusions from complex data.

[0015] S43, Profile Fusion Sub-step: Use a gradient boosting tree model to fuse the professionalism score and insight score to generate a comprehensive user profile vector; S50, Four-dimensional Tensor Recommendation Calculation Step: Expand the user-item two-dimensional interaction matrix into a four-dimensional tensor containing user, financial product, professionalism, and market insight capabilities. Use tensor decomposition to learn the latent semantic vectors of each dimension and calculate the user's preference score for the content to generate a recommendation list; S60, Feedback Learning and Calibration Step: Collect the "exposure" and "conversion" behavior of the recommended content as feedback signals, dynamically adjust the recommendation model parameters, and introduce a periodic calibration mechanism to optimize and evaluate the model weights.

[0016] The preferred technical solution is that the method further includes: S70, data security and compliance assurance steps: This step runs through all data processing stages, and real-time encryption and desensitization processing is implemented on the collected behavioral data and generated user profile data; during the data usage stage, federated learning technology is used for model training, or differential privacy technology is used to add noise when outputting results, and audit logs are recorded for all data access operations.

[0017] The preferred technical solution is that the method is implemented using a cloud-native architecture, specifically including: S80, cloud-native deployment step: using containerization technology to encapsulate each functional step into an independent microservice; S90, data storage step: adopting a cold / hot separation strategy, storing real-time data in an online database, and archiving historical data to a distributed object storage; S100, model update step: supporting a combination of online learning and periodic retraining, wherein model deployment adopts a blue-green deployment or canary deployment mode to achieve seamless updates and rollbacks.

[0018] Compared with existing technologies, the personalized intelligent recommendation system of the financial knowledge platform of this invention has the following technical advantages:

[0019] 1. The personalized intelligent recommendation system of the financial knowledge platform in this application includes a business attribute enhancement module, which is communicatively connected to the server-side data collection module. This module is used to attach a structured set of business context attributes to each behavioral event. Specifically, for the "purchase of financial products" event, the attribute set includes product type, amount, term, risk level, and expected rate of return; for the "browsing of knowledge articles" event, the attribute set includes the financial topic of the article, its level of professional difficulty, and the author's authority. Adding a business attribute set makes the financial attributes stronger in subsequent profile creation, resulting in higher accuracy and professionalism in the profile. Recommendations based on deep business attributes greatly enhance the relevance, personalization, and commercial value of the recommendation results.

[0020] 2. A multimodal user profile construction module receives and processes enhanced user behavior data. It associates data with the customer relationship management system and transaction system through a secure data mapping interface, integrating real-time user interaction behavior with historical asset allocation and transaction records. Based on a preset quantitative model, it generates dynamic user feature vectors from multiple dimensions, including investment expertise, market insight, risk preference, and liquidity preference. A tensor decomposition recommendation engine models the user-item interaction relationship as a four-dimensional tensor, where the four dimensions include the user, financial knowledge product, user investment expertise, and user market insight. The final user preference prediction score for specific knowledge content or financial products is calculated through a fusion function. This architecture treats user capabilities as latent vectors that can be learned and updated. As users improve their expertise and market insight through learning and practice, their behavioral patterns change. The system captures these changes through a feedback learning module and dynamically adjusts their investment expertise latent semantic vector and market insight latent semantic vector. This means that the recommendation system can not only adapt to changes in users' current interests, but also evolve with users as they grow, continuously providing users with information and services that best match their current ability stage, thereby establishing a long-term, dynamic, and co-growing user relationship.

[0021] Other features and advantages of the invention will become clear when reading the following description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. In these drawings, similar reference numerals are used to denote similar elements. The drawings described below are some embodiments of the invention, but not all embodiments. Other drawings will be readily available to those skilled in the art based on these drawings without any inventive effort.

[0023] Figure 1 This is a schematic diagram of a personalized intelligent recommendation system for a financial knowledge platform provided in a specific embodiment of the present invention;

[0024] Figure 2 This is a system flowchart provided in a specific embodiment of the present invention;

[0025] Figure 3 This is a flowchart illustrating the personalized intelligent recommendation method for the financial knowledge platform provided in a specific embodiment of the present invention.

[0026] Figure 4 This is a flowchart illustrating the steps for constructing a multi-dimensional user profile as provided in a specific embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0028] The personalized intelligent recommendation system of this financial knowledge platform will be described in detail below with reference to the accompanying drawings and embodiments.

[0029] like Figure 1-4As shown, a personalized intelligent recommendation system for a financial knowledge platform includes: a server-side event tracking module configured to collect high-value financial behavior event data of users on the server side after the completion of key business logic processing in the core business system, ensuring strong consistency between data and business transactions. The behavior events include at least account opening, transaction order placement, purchase of wealth management products, changes in holdings, risk assessment submission, and collection of knowledge articles. Traditional front-end event tracking is susceptible to network latency, data tampering, or loss. This solution uses server-side event tracking, triggering collection after business logic completion and database transaction submission, ensuring that every piece of financial-related behavior data is strictly consistent with the actual state of the business system. This guarantees the accuracy and reliability of subsequent analysis and recommendations from the data source, avoiding erroneous user profiles and recommendations due to data distortion, and providing a solid foundation for the financial field, which has extremely high requirements for data accuracy.

[0030] To make the attribute sets more professional and applicable to finance, a business attribute enhancement module is also included. This module communicates with the server-side event tracking module and is used to attach a structured set of business context attributes to each behavioral event. For the "purchase of financial products" event, the attribute set includes product type, amount, term, risk level, and expected rate of return. For the "browsing of knowledge articles" event, the attribute set includes the financial topic of the article, its level of professional difficulty, and the author's authority. This module works in conjunction with the server-side event tracking module to ensure that the attached business attributes, such as amount and risk level, are highly consistent with the records in the core business system, avoiding the risk of data tampering or distortion that may occur when collecting data from the front end. This high-quality, highly consistent, standardized data output provides stable and reliable training samples for machine learning models, reducing model bias caused by data noise at the source, and improving the model's convergence speed and final recommendation effect. Original behavioral events such as "purchase" and "browse" lack deep semantic information. This module obtains rich business attributes of events by calling the product center and content management system, transforming low-dimensional events into high-dimensional features. For example, "User purchased product A" can be enhanced to "User purchased product A of type [Money Market Fund], amount [50,000 RMB], term [7 days], risk level [R1], and expected return [2.5%]". This provides directly usable and business-meaning feature dimensions for building refined user profiles and achieving accurate personalized recommendations, and is a key preprocessing step to improve recommendation relevance. The multi-dimensional labeled dataset output by this module drives the subsequent intelligent recommendation engine. It enables the recommendation algorithm to move beyond shallow associations such as "User A read article B, and User C also read article B, so we recommend other articles that C reads to A," and instead achieve "Because User A has repeatedly browsed articles of 'fixed income' and 'medium difficulty', and has just purchased a 'short-term' and 'low-risk' financial product, we should recommend relevant knowledge about 'reverse repurchase of government bonds' that matches their current risk preferences and knowledge level." This recommendation based on deep business attributes greatly enhances the relevance, personalization, and commercial value of the recommendation results.

[0031] The multimodal user profile building module receives and processes enhanced user behavior data. It connects with the customer relationship management system and trading system via a secure data mapping interface, integrating real-time user interactions with historical asset allocation and transaction records. Based on a pre-defined quantitative model, it generates dynamic user feature vectors from multiple dimensions, including investment expertise, market insight, risk preference, and liquidity preference. This module solves the data silo problem by securely mapping and associating user identities (such as device ID and user ID) across different systems, such as front-end apps, CRM, and trading systems, through secure interfaces like API token-based authentication and encrypted access, forming a 360° user view. The "multimodal" aspect is reflected in its comprehensive utilization of structured transaction data, unstructured text data, and time-series behavioral data. Its built-in quantitative models, such as rule-based scoring cards or machine learning models, analyze the integrated data to abstract users into a series of calculable, dynamically updated feature vectors. For example, user A's investment expertise vector is [0.8, 0.2, 0.5], and their market insight vector is [0.6, 0.9, 0.3]. This allows the system to go beyond surface behavior and deeply understand users' intrinsic abilities and preferences, laying the foundation for precise matching in the next step. A four-dimensional tensor decomposition recommendation engine models the user-item interaction relationship as a four-dimensional tensor, where the four dimensions include the user, financial knowledge product, user's investment expertise, and user's market insight ability. The final user preference prediction score for specific knowledge content or financial products is calculated through a fusion function. This function includes: a first interaction component, which is the dot product of the user's latent semantic vector and the knowledge content's latent semantic vector, used to measure the general preference matching degree between the user and the content; a second ability matching component, which is the dot product of the user's investment expertise latent semantic vector and the market insight ability latent semantic vector, used to measure the degree of matching between the user's comprehensive ability traits and the level of expertise and insight required or implied by the target content; and a global bias term to correct overall biases in the data. The preference prediction score is the sum of the first interaction component, the second ability matching component, and the global bias term. Based on the user's preference prediction score for the target content, a personalized recommendation list is generated. Traditional recommendation models, such as two-dimensional matrix factorization, only consider the interaction between users and items, ignoring the deep matching relationship between user traits and item attributes. This engine innovatively introduces "user investment expertise" and "user market insight ability" as the third and fourth dimensions, constructing a four-dimensional tensor model. Its core principle lies in: the first interaction component: capturing traditional collaborative filtering signals to discover what users might "like." The second capability matching component: assessing whether the content is "suitable" for the user's current level of expertise and insight.For example, an in-depth report on "derivative pricing models" will have a low relevance for users with low expertise, even if it is of high quality, and the system will not recommend it to avoid user confusion and improve the experience. Conversely, for users with high expertise, the relevance will be high, and it will be prioritized for recommendation. A global bias term is used to eliminate biases caused by globally popular products or user activity levels. This fusion function ensures that the recommended results are both of interest to the user and match their cognitive abilities, significantly improving the accuracy, acceptability, and user satisfaction of the recommendations. As users improve their expertise and market insight through learning and practice, their behavioral patterns change. The system captures these changes through a feedback learning module and dynamically adjusts its implicit semantic vectors for investment expertise and market insight. This means that the recommendation system can not only adapt to changes in users' current interests but also evolve with users' growth, continuously providing users with information and services most suitable for their current skill level, thus establishing a long-term, dynamic, and co-growing user relationship.

[0032] The business attribute enhancement module is located at the very front of the data chain. It receives raw user behavior events from server-side tracking points, such as "User U purchased product P". Its core task is to inject rich business context attributes into these "skeleton" events by calling other business systems, such as the product library and content management system, transforming them into high-dimensional, semantically rich features, such as "User U purchased product P with [Risk Level R5], [Term 180 Days], [Equity Type]". The business attribute enhancement module is the sole data source and foundation of the profiling module. Without these rich attributes, the profiling module can only perform shallow frequency statistics, such as the number of times a user purchases, and cannot conduct in-depth quantitative analysis of "professionalism" and "insight". It is precisely because of the "financial theme" and "professional difficulty" attributes that the depth of the user's knowledge structure can be analyzed. The business attribute enhancement module indirectly determines the interpretability and accuracy of the recommendation engine. The recommendation engine ultimately calculates the matching degree between users and items in the feature space. Business attributes define the feature space of items, such as what type of product it is and whether it is multi-risk, while the profiling module defines the user's feature space based on these attributes. Both must be in the same semantic space for a match to be effective. This lays the semantic foundation for all subsequent analysis. It elevates data from a low-dimensional, generalized behavioral space to a high-dimensional, precise financial business semantic space, enabling subsequent modules to "understand" the financial meaning behind the behavior. The multimodal user profile building module receives the enhanced behavioral data and integrates historical business data from CRM and transaction systems through a secure interface. Its core is a built-in quantitative model, such as NLP, time series analysis, and machine learning models. These models extract features from multimodal data, ultimately abstracting users into a series of dynamically updated, computable feature vectors—the user profile vectors. The multimodal user profile building module relies on high-quality, semantically rich data input provided by the enhancement module. Data quality directly determines the accuracy of the profile. The multimodal user profile building module is the core driver of the recommendation engine. It provides two inputs to the recommendation engine: a user latent semantic vector, representing the user's traditional interests and preferences; and an investment expertise latent semantic vector (E) and a market insight latent semantic vector (Ins). These represent deeper user cognitive ability traits. They fundamentally change the matching logic of the recommendation engine, upgrading it from "recommending what they like" to "recommending what is suitable and what they like." It achieves a deep understanding of users from "behavior" to "cognition." Its output is no longer a simple label, but a mathematical vector representing the user's inherent abilities and traits, providing a basis for the next step of "cognition matching." The Tensor Decomposition Recommendation Engine module is the system's "decision center." It creatively expands the traditional two-dimensional model into a four-dimensional tensor (User-Item-Expertise-Insight). Its core is a fusion function: Score = User·Item + E·Ins + Bias.The first component (User·Item) captures traditional collaborative filtering signals to ensure that users "like" the recommended content. The second component (E·Ins) calculates the match between the user's comprehensive ability traits (the dot product of E and Ins) and the abilities required by the item. Here, "item" can be a financial product or knowledge content. For example, an in-depth derivatives research report might have a high implicit attribute of "required level of expertise." The Tensor Decomposition Recommendation Engine module directly relies on the E and Ins vectors output by the profiling module. The accuracy and representativeness of these two vectors directly determine the calculation effect of the second component, i.e., the accuracy of the "ability matching" recommendation. The Tensor Decomposition Recommendation Engine module defines the optimization goal of the profiling module: the feedback signal of the recommendation engine can propagate back to optimize the sub-models in the profiling model that evaluate expertise and insight, forming a closed-loop optimization system. The system can not only recommend content that users are interested in, but also content that matches their current cognitive abilities, greatly improving the acceptance and effectiveness of the recommendations. When a recommendation result is generated, it can be analyzed whether it is "because you like it" or "because it suits your abilities." This transparency is crucial for building user trust and meeting financial compliance requirements. For new users, even with limited behavioral data, the E and Ins vectors can be initially estimated based on their initial attributes, thus bypassing data sparsity and enabling early personalized recommendations.

[0033] The three modules do not work in isolation, but rather form a closely collaborative and progressively evolving system: the business attribute enhancement module provides foundational data for the multimodal user profile construction module. After digesting and absorbing this data, the multimodal user profile construction module extracts E and Ins vectors representing deep user traits and feeds them to the Tensor Factorization recommendation engine. The Tensor Factorization recommendation engine uses these E and Ins vectors to create a new and more advanced recommendation logic, ultimately producing highly accurate and interpretable personalized recommendation results. User feedback generated by the recommendation results flows back into the system to continuously optimize the profile and recommendation models, forming a self-improving intelligent closed loop. This system successfully integrates professional financial knowledge with artificial intelligence technology, moving beyond simply applying general recommendation algorithms to financial scenarios. It fundamentally reconstructs a recommendation system designed specifically for finance. It truly achieves a deep understanding of users and accurate value matching, thereby improving user experience, enhancing user trust, and ultimately realizing the intelligent upgrade of financial knowledge services and product distribution.

[0034] This system also includes a reinforcement learning and feedback calibration module, configured to accurately collect and distinguish between the "exposure" event of recommended content and the user's subsequent "conversion" behavior event. Conversion behavior is used as a reinforcement learning signal to dynamically adjust the model parameters of the tensor decomposition recommendation engine. A built-in periodic calibration mechanism optimizes the weight allocation of the evaluation model in the multimodal user profile construction module by comparing user behavior sequences with actual market performance data. Recommendation systems are not static; user interests and market environments are constantly changing. This module constructs an efficient closed-loop optimization system: Exposure and Conversion Separation: Strictly distinguishing between "what the system displays" and "what the user does with the displayed content" is a prerequisite for accurately evaluating recommendation effectiveness and obtaining unbiased feedback. Exposure without conversion is negative feedback. Reinforcement Learning: Treating each recommendation as a decision, user conversions such as clicks, purchases, and long dwell times serve as reward signals. The recommendation model is dynamically fine-tuned through algorithms such as policy gradients, such as adjusting the weights of the fusion function, allowing the system to continuously learn user real-time preferences and become smarter with use. Periodic Calibration: The efficiency of financial markets means that users' historical behavior is not always correct. This mechanism periodically compares users' decision-making behavior with subsequent market performance to determine the accuracy of the user profile evaluation model and adjusts its weight parameters accordingly; for example, if a predictive factor fails, its weight is reduced. This ensures that the user profile objectively reflects the true evolution of users' capabilities, preventing the model from becoming outdated or deviating from its intended purpose, and guaranteeing the accuracy of long-term recommendations.

[0035] The detailed technical solution for the event tracking module is as follows: When collecting event data, the server-side event tracking module triggers the data collection through transaction log monitoring or business logic layer embedding. This ensures that data collection and business operations are within the same database transaction or have eventual consistency, fundamentally preventing data distortion caused by front-end data tampering, loss, or network anomalies. Transaction log monitoring is a non-intrusive collection method that absolutely guarantees data consistency with the business database state. Business logic layer embedding involves calling the event tracking SDK at key nodes in the business code; while somewhat intrusive, it offers high flexibility. Both methods ensure strong consistency or eventual consistency between event tracking data and business data. This means that once a business operation is successful (such as deduction or order generation), the corresponding event tracking will definitely be successfully recorded, eliminating the risk of data loss due to network fluctuations, client anomalies, etc., and providing a complete and reliable data foundation for all subsequent analyses.

[0036] The core technology of multimodal user profiling lies in the fact that the multimodal user profiling module further includes: an investment professionalism assessment submodule, which uses natural language processing technology to analyze the text entered by users in search queries, online consultations, and community discussions. It statistically analyzes the frequency, accuracy, and contextual complexity of specific financial professional terms and regulations, while also analyzing the user's data query logic, the depth of their use of advanced analytical tools, and the complexity of their simulated trading strategies, outputting a quantitative professionalism index. A user's professional level is directly reflected in their language and behavioral patterns. This submodule automatically analyzes the text content generated by users through natural language processing technology. Users who frequently and accurately use professional terms such as "Sharpe ratio" and "DCF valuation" naturally have higher professionalism indices. Simultaneously, it analyzes their behavioral sequences: whether they simply query stock prices or construct complex multi-condition data filtering and backtesting. This is more objective, real-time, and detailed than traditional questionnaire assessments, accurately quantifying the user's intangible knowledge reserves and analytical capabilities. It also includes a market insight assessment submodule, which uses time series analysis to monitor users' access patterns to different types of information streams, including reaction time to breaking news, macroeconomic data, and company financial reports, reading dwell time, and subsequent actions. It quantifies the accuracy of user insights by recording the degree of alignment between user predictions and actual market conditions, outputting a quantitative insight index. Market insight emphasizes sensitivity, judgment, and timeliness of information. This submodule uses time series analysis to align user behavior with the timeline of market events. For example, users who consult relevant interpretations and adjust their positions within 5 minutes of the central bank's interest rate cut announcement score highly on the "behavioral leadership" indicator. Extensive reading of a research report without subsequent blind actions indicates high-quality information digestion. The establishment of a "prediction verification system" provides objective quantitative evidence: the user's historical prediction accuracy is the gold standard for measuring their insight. These indicators together constitute a comprehensive assessment of users' market judgment capabilities. Furthermore, it includes a comprehensive user profile fusion submodule, which employs a machine learning model. Taking the professionalism index, insight index, user risk assessment results, and historical transaction data features as input, it outputs a comprehensive, multi-dimensional user profile vector. The indices for individual dimensions are distributed. This submodule uses machine learning models such as Gradient Boosting Tree (GBDT) or Deep Neural Network (DNN) as a "fusionist" to automatically learn the importance weights of different features. For example, in wealth management scenarios, risk preference may have a higher weight; in investor education scenarios, professionalism may have a higher weight. It non-linearly combines the distributed indices and features into a unified, low-dimensional user profile vector. This vector is a dense numerical representation of user features, containing all the user's key information, making it ideal as input for downstream recommendation engines and automating and optimizing feature engineering.The specific evaluation dimensions of the investment professionalism assessment submodule include: Depth of knowledge structure: assessed through the user's complete reading rate of in-depth research reports, repeated viewing patterns of professional teaching videos, and accuracy rate in answering online professional tests. "Complete reading rate" and "repeated viewing" are strong signals measuring the user's depth of knowledge and focus, avoiding the noise caused by superficial browsing. The online test provides a direct and objective assessment of ability. The combination of these three aspects comprehensively evaluates the depth and solidity of the user's knowledge structure from three perspectives: learning behavior, consolidation behavior, and assessment results. Evaluation is also conducted by analyzing the user's historical portfolio's Sharpe ratio, maximum drawdown, portfolio diversification, and stop-loss discipline demonstrated in simulated or actual trading. Professionalism in the financial field is ultimately reflected in decision-making results. The Sharpe ratio, maximum drawdown, and portfolio diversification are recognized objective indicators in quantitative investment for measuring portfolio quality. Stop-loss discipline reflects the user's risk management ability, avoiding emotional trading. Using these hard indicators to assess professionalism makes the evaluation results more convincing and practical. The evaluation is conducted by calculating the adoption rate, number of likes, and positive sentiment polarity of users' answers in Q&A communities. A user's answer being "adopted" or "liked" by other users is a direct reflection of their professional value being recognized by the community, carrying high credibility. Sentiment analysis ensures that their contributions are constructive and positive, maintaining the quality of the community. This not only assesses their professionalism but also encourages knowledge sharing, forming a positive cycle in the community ecosystem.

[0037] The specific evaluation methods of the market insight capability assessment submodule include: Information value discrimination capability: By analyzing the distribution of information topics that users pay attention to, the evaluation distinguishes the proportion of their preference for market noise-type information and high-value fundamental information. The ability to distinguish between "noise" and "signals"—noise such as short-term market rumors and entertainment-oriented financial news; signals such as company financial reports, macroeconomic policies, and in-depth industry analysis—is the primary manifestation of insight. Automatic classification and statistics of user-browsed content are performed using topic models and classification algorithms to calculate the proportion of high-value information they focus on, quantifying their information filtering ability. It also includes a behavioral leadership indicator: By calculating the difference between the time of a user's first relevant action after an important market event and the time of the event, the smaller the difference, the higher the leadership indicator score. The speed of reaction to information is directly related to decision-making effectiveness. This indicator quantifies the delay from receiving information to making a decision through precise time difference calculation. The shorter the delay, the higher the information processing efficiency, the more decisive the decision, and the better the market opportunities they can seize—a direct behavioral manifestation of insight. It also includes a prediction verification system: a subsystem is established to anonymously collect market predictions voluntarily submitted by users, and automatically compares them with real market data at preset time points to generate an objective accuracy report. This is the most direct and objective evaluation method. The system provides a platform for users to record their predictions, such as "I believe stock A will rise by 10% in the next month," and automatically compares them with real market data after the deadline to calculate the prediction accuracy. The long-term accumulated accuracy reports are the most robust indicator of user insight, greatly enhancing the fairness and authority of the evaluation results.

[0038] The reinforcement learning and feedback calibration module further includes a multi-armed bandit exploration strategy submodule, used to explore new or less popular content that users may be potentially interested in during the recommendation process with a certain probability, in order to balance exploration and utilization in recommendations. Recommendation systems are prone to falling into the trap of only recommending content that users already know they like, failing to discover new points of interest for users. The multi-armed bandit algorithm cleverly solves this problem through a simple probability, such as the ε-greedy strategy: recommending the current best content with a 90% probability and randomly exploring new content with a 10% probability. This ensures that while satisfying the user's current preferences, the system continuously tries to broaden their horizons, discover potential interests, avoids the rigidity of the recommendation list, and enhances the system's discovery capabilities and user experience. It also includes a model hot update submodule, which supports incremental updates to the recommendation model based on recent user feedback streaming data to quickly capture user interest drift. User interests change over time, and retraining the model completely is time-consuming and resource-intensive. Hot updates allow the model to process the latest user feedback data in a streaming manner, perform incremental updates, and fine-tune model parameters in real time. This enables the recommendation system to respond to changes in user interests in near real-time. For example, during periods of sharp market fluctuations, it can quickly detect a user's intention to shift their focus from "growth stocks" to "safe-haven assets," significantly improving the timeliness and flexibility of recommendations. It also includes an expert review loop, periodically sampling user profile data and recommendation results for review and correction by domain experts. The correction results are then fed back as supervisory signals to the model training process, forming a hybrid closed-loop optimization through human-machine collaboration. Purely algorithmic models may contain subtle biases or errors. Introducing domain experts for manual sampling allows them to use their deep industry knowledge and experience to correct the system's output. These correction results, as high-quality labeled data, are fed back to the model for retraining, effectively correcting model biases, injecting domain knowledge, and achieving human-machine collaboration. This ensures that while the system operates automatically, its development direction and output quality are always guided and constrained by the wisdom of human experts, which is particularly important in the highly complex financial field.

[0039] A preferred technical solution is that the system further includes: a privacy computing and security compliance gateway, integrated into the critical path of data flow, which performs real-time encryption and de-identification processing on all collected raw behavioral data and generated user profile feature vectors; during the data retrieval phase, it supports model training and recommendation calculation without exposing the original data through federated learning or differential privacy technology, and performs tamper-proof log auditing on all data access operations to ensure compliance with financial data security regulations. Financial data is one of the most sensitive types of personal data. This solution deeply integrates privacy computing and security compliance into the system architecture. Encryption and de-identification: Provides basic protection for static data and data in transit. Federated learning: Allows parties to jointly train the model without exchanging the original data, exchanging only encrypted model parameter updates, fundamentally eliminating the risk of data leakage. Differential privacy: When outputting recommendation results or statistical information, carefully calculated noise is added, making it impossible to deduce any individual user's original information from the output results, protecting individual privacy while ensuring data availability. Audit logs: Meets the "traceable and auditable" requirements of financial industry regulations. These technologies together form a privacy and security barrier, enabling the system to fully utilize the value of data while ensuring security and compliance, which is a necessary guarantee for the system to be implemented and applied in the financial field.

[0040] Example 2

[0041] This embodiment also provides a personalized intelligent recommendation method for a financial knowledge platform. The method includes the following steps: S10, server-side data collection step: On the financial service side, after the core business logic is processed and the database transaction is completed, key financial behavior event data of users are collected by listening to the business system transaction log or calling a dedicated API interface. The behavior events include at least account opening, transaction entrustment, purchase of wealth management products, changes in holdings, submission of risk assessment, and collection of knowledge articles. As described in the previous module description, this method step ensures the accuracy and reliability of data collection and provides a high-quality data source for subsequent processes.

[0042] S20, Business Context Attribute Attachment Step: Attach structured business context attributes to each behavioral event to generate a user behavior dataset with multi-dimensional labels. For example, the attributes attached to the "Purchase of Financial Products" event include product type, amount, term, risk level, and expected rate of return; the attributes attached to the "Browsing Knowledge Articles" event include the financial topic of the article and its level of professional difficulty. As explained in the previous modules, this step enriches the data dimensions and provides key features for building accurate user profiles and recommendation models.

[0043] S30. User Data Integration Steps: Connect the front-end behavioral data with the user identifier mapping of the back-end core business system, and link the user's real-time interactive behavior with historical asset configuration and transaction records. This step solves the data silo problem by linking user behavior in different systems through identity mapping (such as linking the front-end session ID with the user account ID), forming a complete user behavior trajectory and business profile, providing a data foundation for comprehensive user analysis.

[0044] S40. Multi-dimensional User Profile Construction Step: Process the user behavior dataset to construct a quantitative user characteristic profile from multiple financial dimensions, including investment expertise, market insight, risk preference, and liquidity preference. This step includes: S41. Investment Expertise Assessment Sub-step: Analyze the frequency, accuracy, and contextual complexity of professional terms in user text using natural language processing technology, and analyze the depth of their data queries and their use of advanced analytical tools. The specific execution process includes: Knowledge Depth Assessment: Analyze user consultation language, search queries, completion rate of in-depth research reports, and performance in professional tests; Decision-Making Behavior Analysis: Track user simulated or actual investment operation records, assess the consistency of their decision-making logic, risk control capabilities, and refer to historical Sharpe ratio indicators; Interaction and Contribution Quality Analysis: Analyze the quality, accuracy, and adoption rate of information provided by users in community discussions and Q&A sections. Similar to the aforementioned Investment Expertise Assessment sub-module, this sub-step automatically and quantitatively assesses the user's professional level through multi-angle behavioral analysis.

[0045] S42, Market Insight Ability Assessment Sub-step: This sub-step uses time series analysis to analyze the types of information users pay attention to, monitor their reaction speed to market events, and verify the accuracy of their historical forecasts. The specific execution process includes: Market Information Processing Pattern Analysis: Monitoring the types of information users pay attention to, such as breaking news, macroeconomic data, and company financial reports, and their reaction speed; Prediction and Judgment Verification: Analyzing users' published market views, prediction records, and their subsequent market validation; Data Analysis and Application Ability Assessment: Assessing users' ability to use financial models for customized analysis and extract conclusions from complex data. Similar to the aforementioned Market Insight Ability Assessment sub-module, this sub-step objectively quantifies users' market acumen and judgment through time series behavior analysis and prediction verification.

[0046] S43, Profile Fusion Sub-step: The gradient boosting tree model is used to fuse the professionalism score and insight score to generate a comprehensive user profile vector. Similar to the aforementioned comprehensive profile fusion sub-module, this sub-step uses a machine learning model to automatically learn the importance of each dimension and output a unified user feature representation for use by the recommendation engine.

[0047] S50, Four-Dimensional Tensor Recommendation Calculation Steps: The user-item two-dimensional interaction matrix is ​​expanded into a four-dimensional tensor containing user, financial product, professionalism, and market insight capabilities. The latent semantic vectors of each dimension are learned using the tensor decomposition method, and the user's preference score for the content is calculated to generate a recommendation list. Similar to the tensor decomposition recommendation engine described above, this step achieves deeper, more accurate recommendations based on adaptability by introducing the capability and professionalism dimensions.

[0048] S60. Feedback Learning and Calibration Steps: This step collects feedback signals from the "exposure" and "conversion" behaviors of recommended content, dynamically adjusts the recommendation model parameters, and introduces a periodic calibration mechanism to optimize the evaluation model weights. Similar to the aforementioned reinforcement learning and feedback calibration module, this step enables the system's self-iteration and optimization, and can calibrate the user capability evaluation model based on real market performance, ensuring the system's long-term effectiveness and adaptability.

[0049] Furthermore, the method also includes: S70, Data Security and Compliance Assurance Steps: This step is integrated throughout all data processing stages, implementing real-time encryption and anonymization of collected behavioral data and generated user profile data; during the data usage stage, federated learning technology is used for model training, or differential privacy technology is used to add noise to the output results, and all data access operations are audited and logged. Similar to the aforementioned privacy computing and security compliance gateway description, this method ensures the security and compliance of the entire data processing flow, forming the cornerstone of the system's application in the financial field.

[0050] The method is implemented using a cloud-native architecture, specifically including: S80 and cloud-native deployment steps: using containerization technology to encapsulate each functional step into an independent microservice; containerization and microservice architecture decouple the system into multiple small, autonomous services. Each service can be developed, deployed, scaled, and updated independently, greatly improving the system's agility, maintainability, and scalability.

[0051] S90. Data Storage Steps: A hot / cold data separation strategy is adopted. Real-time data is stored in an online database, while historical data is archived to distributed object storage. Different storage schemes are used based on the frequency of data usage. Frequently accessed hot data is stored in a high-performance online database to ensure low-latency response. Infrequently accessed cold data is stored in low-cost distributed object storage. This strategy optimizes data storage costs while ensuring system performance.

[0052] S100, Model Update Steps: Supports a combination of online learning and periodic retraining, with model deployment employing blue-green deployment or canary deployment modes to achieve seamless updates and rollbacks. The combination of online learning and retraining balances the model's real-time performance and global optimality.

[0053] Blue-Green Deployment: Prepare two environments, one running the current version and the other deploying the new model. By switching load balancer routes, instant switching and zero-downtime deployment are achieved. If the new version has issues, it can be switched back instantly. Canary Deployment: Deploy the new model to a small group of users first to observe its effects and stability. Once confirmed to be working correctly, gradually expand the scope to all users. These two deployment modes greatly reduce the risks associated with model updates, ensuring the stability and high availability of online services. They are essential engineering practices for production-grade AI systems.

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the article or apparatus that includes that element.

[0055] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. The present invention has been described in detail with reference to preferred embodiments. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A personalized intelligent recommendation system for a financial knowledge platform, characterized in that, include: A server-side event tracking module is configured to collect high-value financial behavior event data from users on the server side after the critical business logic processing of the core business system is completed, to ensure strong consistency between data and business transactions. These behavior events include at least account opening, transaction order placement, wealth management product purchase, portfolio changes, risk assessment submission, and knowledge article collection. A business attribute enhancement module, communicatively connected to the server-side event tracking module, is used to attach a structured set of business context attributes to each behavior event. In the wealth management product purchase event, the attribute set includes product type, amount, term, risk level, and expected rate of return. In the knowledge article event... In the browsing event, the attribute set includes the financial topic of the article, the level of professional difficulty, and the author's authority; a multimodal user profile construction module is used to receive and process enhanced user behavior data, associate data with the customer relationship management system and transaction system through a secure data mapping interface, integrate the user's real-time interaction behavior with historical asset allocation and transaction records, and generate dynamic user feature vectors from multiple dimensions such as investment professionalism, market insight ability, risk preference, and liquidity preference based on a preset quantitative model; a tensor decomposition recommendation engine is used to model the user-item interaction relationship as a four-dimensional tensor, wherein the four dimensions include... The system considers factors such as user profile, financial knowledge products, user investment expertise, and user market insight capabilities. The final user preference prediction score for specific knowledge content or financial products is calculated using a fusion function. This function includes: a first interaction component, which is the dot product of the user's latent semantic vector and the knowledge content's latent semantic vector, used to measure the general preference matching degree between the user and the content; a second capability matching component, which is the dot product of the user's investment expertise latent semantic vector and the market insight capability latent semantic vector, used to measure the degree of matching between the user's comprehensive capability traits and the level of expertise and insight required or implied by the target content; and a global bias term, used for... The overall bias in the data is corrected; the preference prediction score is the sum of the first interaction component, the second capability matching component, and the global bias term; a personalized recommendation list is generated based on the user's preference prediction score for the target content; a reinforcement learning and feedback calibration module is configured to accurately collect and distinguish the exposure events of the recommended content from the user's subsequent conversion behavior events, use the conversion behavior as a reinforcement learning signal to dynamically adjust the model parameters of the tensor decomposition recommendation engine, and has a built-in periodic calibration mechanism to optimize the weight allocation of the evaluation model in the multimodal user profile construction module by comparing user behavior sequences with actual market performance data.

2. The system according to claim 1, characterized in that, When collecting event data, the server-side data collection module is triggered by listening to transaction logs or embedding data in the business logic layer. This ensures that data collection and business operations are within the same database transaction or have eventual consistency, thereby fundamentally avoiding data distortion caused by front-end data tampering, loss, or network anomalies.

3. The system according to claim 1, characterized in that, The multimodal user profile construction module further includes: an investment professionalism assessment submodule, which uses natural language processing technology to analyze the text entered by users in search queries, online consultations, and community discussions, statistically analyzes the frequency, accuracy, and contextual complexity of specific financial professional terms and regulations, and analyzes the user's data query logic, depth of use of advanced analysis tools, and complexity of simulated trading strategies, outputting a quantitative professionalism index; a market insight assessment submodule, which uses time series analysis to monitor users' access patterns to different types of information streams, including reaction delay time, reading dwell time, and subsequent operational behavior to breaking news, macro data, and company financial reports, and quantifies the accuracy of their insight by recording the degree of consistency between users' predicted views and actual market conditions, outputting a quantitative insight index; and a comprehensive profile fusion submodule, which uses a machine learning model, taking the professionalism index, insight index, user risk assessment results, and historical transaction data characteristics as input, and outputting a comprehensive multidimensional user profile vector.

4. The system according to claim 3, characterized in that, The specific evaluation dimensions of the investment professionalism assessment submodule include: Depth of knowledge structure: assessed by the user's complete reading rate of in-depth research reports, repeated viewing patterns of professional teaching videos, and accuracy rate in answering online professional tests; Quality of decision-making behavior: assessed by analyzing the user's historical portfolio Sharpe ratio, maximum drawdown, portfolio diversification indicators, and stop-loss discipline demonstrated in simulated or actual trading; Community contribution value: assessed by calculating the user's adoption rate of answers provided in the Q&A community, the number of likes, and the positive sentiment polarity of the content published.

5. The system according to claim 3, characterized in that, The specific evaluation methods of the market insight capability assessment submodule include: information value discrimination capability: by analyzing the distribution of information topics that users pay attention to, distinguishing the proportion of their preference for market noise information and high-value fundamental information; behavioral leading indicator: by calculating the difference between the time of the user's first relevant operation after the occurrence of an important market event and the time of the event, the smaller the difference, the higher the leading indicator score; prediction verification system: establishing a subsystem to anonymously collect market prediction opinions voluntarily submitted by users, and automatically comparing them with real market data after a preset time point to generate an objective accuracy report.

6. The system according to claim 1, characterized in that, The reinforcement learning and feedback calibration module further includes: a multi-armed bandit exploration strategy submodule, used to explore new or niche content that users may be interested in with a certain probability during the recommendation process, so as to balance the exploration and utilization of recommendations; a model hot update submodule, which supports incremental updates to the recommendation model based on recent user feedback streaming data, so as to quickly capture user interest drift; and an expert review loop, which periodically extracts profile data of some users and recommendation results, and has domain experts review and correct them, and feeds the correction results back to the model training process as a supervision signal, forming a hybrid closed-loop optimization of human-machine collaboration.

7. The system according to claim 1, characterized in that, The system also includes: a privacy computing and security compliance gateway, integrated into the critical path of data flow, which performs real-time encryption and desensitization processing on all collected raw behavioral data and generated user profile feature vectors; during the data retrieval phase, it supports model training and recommendation calculation without exposing the raw data through federated learning or differential privacy technology, and performs tamper-proof log auditing on all data access operations to ensure compliance with financial data security regulations.

8. A personalized intelligent recommendation method for a financial knowledge platform, wherein the method is executed by the system described in any one of claims 1-7, characterized in that, The method includes the following steps: S10, Server-side data collection step: On the financial service side, after the core business logic is processed and the database transaction is completed, key financial behavior event data of users is collected by listening to the business system transaction log or calling a dedicated API interface. The behavior events include at least account opening, transaction entrustment, purchase of wealth management products, changes in holdings, risk assessment submission, and collection of knowledge articles; S20, Business context attribute attachment step: Structured business context attributes are attached to each behavior event to generate a user behavior dataset with multi-dimensional labels. Among them, the attributes attached to the wealth management product purchase event include product type, amount, term, risk level, and expected rate of return, and the attributes attached to the knowledge article browsing event include the financial topic of the article and the level of professional difficulty; S30, User data fusion step: The front-end behavior data is integrated with the user identifier mapping of the back-end core business system, and the real-time user interaction behavior is associated with historical asset allocation and transaction records; S40, Multi-dimensional user profile construction step: The user behavior dataset is processed to construct a quantitative user feature profile from multiple financial dimensions such as investment professionalism, market insight ability, risk preference, and liquidity preference. This step includes: S41, Investment Professionalism Assessment Sub-step: Utilizing natural language processing technology to analyze the frequency, accuracy, and contextual complexity of professional terms in user text, this sub-step assesses the depth of data queries and the use of advanced analytical tools. The specific execution process includes: Knowledge Depth Assessment: Analyzing user consultation language, search queries, completion rate of in-depth research reports, and performance in professional tests; Decision-Making Behavior Analysis: Tracking user simulated or actual investment operation records to assess the consistency of their decision-making logic, risk control capabilities, and referencing historical Sharpe ratio indicators; Interaction and Contribution Quality Analysis: Analyzing the quality, accuracy, and adoption rate of information provided by users in community discussions and Q&A sections. S42, Market Insight Ability Assessment Sub-step: This sub-step utilizes time series analysis techniques to analyze the types of information users focus on, monitor their reaction speed to market events, and verify the accuracy of their historical forecasts. The specific execution process includes: Market Information Processing Pattern Analysis: Monitoring the types of information users focus on, such as breaking news, macroeconomic data, and company financial reports, and their reaction speed; Prediction and Judgment Verification: Analyzing users' published market views, forecast records, and their subsequent market validation; Data Analysis and Application Ability Assessment: Assessing users' ability to use financial models for customized analysis and extract conclusions from complex data. S43, Profile Fusion Sub-step: Use a gradient boosting tree model to fuse the professionalism score and insight score to generate a comprehensive user profile vector; S50, Four-dimensional Tensor Recommendation Calculation Step: Expand the user-item two-dimensional interaction matrix into a four-dimensional tensor containing user, financial product, professionalism, and market insight capabilities. Use tensor decomposition to learn the latent semantic vectors of each dimension and calculate the user's preference score for the content to generate a recommendation list; S60, Feedback Learning and Calibration Step: Collect the exposure and conversion behavior of recommended content as feedback signals, dynamically adjust the recommendation model parameters, and introduce a periodic calibration mechanism to optimize and evaluate the model weights.

9. The method according to claim 8, characterized in that, The method also includes: S70, data security and compliance assurance steps: This step is carried out throughout all data processing stages, and real-time encryption and desensitization processing is implemented for the collected behavioral data and generated user profile data; during the data usage stage, federated learning technology is used for model training, or differential privacy technology is used to add noise when outputting results, and audit logs are recorded for all data access operations.

10. The method according to claim 8, characterized in that, The method is implemented using a cloud-native architecture, specifically including: S80, cloud-native deployment steps: using containerization technology to encapsulate each functional step into an independent microservice; S90, data storage steps: adopting a hot / cold separation strategy, storing real-time data in an online database, and archiving historical data to distributed object storage; S100, model update steps: supporting a combination of online learning and periodic retraining, wherein model deployment adopts a blue-green deployment or canary deployment mode to achieve seamless updates and rollbacks.

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