Intelligent question recommendation system based on large model
Through an intelligent recommendation system based on large models, combined with deep learning and integrated learning technology, the cold start, data sparseness and user diversity of the recommendation system are solved, personalized recommendation and user satisfaction are improved, and different application scenarios are adapted to different application scenarios.
Patent Information
- Application Number
- CN202510367312.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-26
- Publication Date
- 2025-07-11
AI Technical Summary
When existing recommendation systems face problems such as cold start, data sparsity and user diversity, it is difficult to provide accurate and personalized recommendations, and privacy protection is insufficient.
It adopts an intelligent recommendation system based on large models, including data collection, processing, model training, recommendation generation and user feedback modules, and combines deep learning algorithms such as CNN and RNN, integrated learning methods and data augmentation technology to adjust recommended content in real time to adapt to user changes.
It improves the accuracy and coverage of recommendations, reduces cold start and data sparseness problems, enhances the ability to respond to changes in user behavior, realizes personalized recommendations, improves user satisfaction and has good scalability.
Smart Images
Figure CN120296253A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical fields of intelligent recommendation systems and deep learning, and particularly relates to an intelligent recommendation problem system based on a large model. Background Art
[0002] Today, with the rapid development of information technology, the amount of information faced by users has increased exponentially. Traditional information retrieval and recommendation methods can no longer meet the personalized needs of users. As an information filtering technology, recommendation systems are widely used in multiple fields such as e-commerce, social networks, and streaming media. Its core goal is to provide users with personalized content and product recommendations to improve user experience and conversion rates.
[0003] Recommendation systems are generally divided into the following categories:
[0004] Content-based recommendation: This method analyzes the characteristics of items based on the user's past behavior and preferences for recommendation. For example, in music recommendation, the system may recommend similar songs based on characteristics such as the user's favorite music genres and artists. Content-based recommendation can effectively mine users' interests, but may fall into the problem of "information island", that is, the recommended content is too single;
[0005] Collaborative filtering recommendation: Collaborative filtering is one of the most commonly used recommendation techniques at present, and is divided into user collaborative filtering and item collaborative filtering. User collaborative filtering recommends items liked by similar users by analyzing the similarity between users; item collaborative filtering recommends based on the similarity between items. Although collaborative filtering can capture potential user preferences, it faces challenges in cold start problems and sparsity problems. Especially when new users or new items appear, it is difficult for the system to provide effective recommendations;
[0006] Hybrid recommendation system: To overcome the limitations of traditional recommendation methods, the hybrid recommendation system combines the advantages of content-based recommendation and collaborative filtering. By integrating multiple recommendation algorithms, it can more comprehensively capture users' preferences and improve the accuracy and diversity of recommendations.
[0007] In recent years, with the rapid development of deep learning technology, recommendation systems have gradually begun to introduce deep learning algorithms to improve the recommendation effect. Deep learning can automatically extract features from massive data and establish complex non-linear models, thereby enhancing the understanding of user preferences.
[0008] Neural collaborative filtering: By constructing a neural network model, neural collaborative filtering can embed the features of users and items into the same vector space, thereby better calculating the similarity between users and items.
[0009] Convolutional Neural Network (CNN): When processing image and video content, CNN can extract key features, and this technology is used in recommendation systems to improve the recommendation effect for visual content.
[0010] Recurrent Neural Network (RNN): RNN is particularly suitable for processing time series data and can effectively model the temporal characteristics of user behavior, enabling the recommendation system to capture the dynamic changes and potential interests of users.
[0011] Although recommendation systems have achieved remarkable results in various fields, they still face some challenges:
[0012] Cold start problem: New users or new items lack sufficient historical data, making it difficult for the recommendation system to make accurate recommendations;
[0013] Data sparsity: The interaction data between users and items is often sparse, resulting in a decline in the effectiveness of collaborative filtering algorithms;
[0014] User diversity: Users' preferences are often variable, and the recommendation system needs to be able to quickly adapt to changes in users' interests;
[0015] Privacy and security: In the process of collecting user data, how to protect users' privacy has become an important factor to consider when designing a recommendation system.
[0016] With the continuous development of big data and artificial intelligence technologies, the research and application prospects of recommendation systems are broad. By adopting advanced deep learning algorithms and optimized data processing technologies, recommendation systems will be able to more accurately meet users' needs, improve user satisfaction, and promote the further development of various industries. Therefore, the research on intelligent recommendation systems based on large models not only has important theoretical significance but also has broad application value.
[0017] For this reason, we propose an intelligent recommendation problem system based on a large model. Summary of the Invention
[0018] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides an intelligent recommendation problem system based on a large model.
[0019] To achieve the above object, the present invention adopts the following technical solution. An intelligent recommendation problem system based on a large model includes a data collection module, a data processing module, a model training module, a recommendation generation module, and a user feedback module;
[0020] The data collection module is responsible for obtaining user behavior data and item feature data from multiple data sources, where the user behavior data includes data on users' click, browse, purchase, rating, and comment behaviors;
[0021] The data processing module performs data cleaning, deduplication, and feature extraction on the user behavior data;
[0022] The model training module uses deep learning algorithms to train the preprocessed data and construct a recommendation model;
[0023] The recommendation generation module generates recommendation results based on the recommendation model and dynamically updates the recommended content according to user behavior and feedback;
[0024] The user feedback module collects feedback information from users on the recommendation results and optimizes the recommendation algorithm and model parameters;
[0025] The system solves the cold start problem, data sparsity problem, and user diversity problem in traditional recommendation systems through a comprehensive variety of data sources and recommendation algorithms.
[0026] As a preferred technical solution of the present invention, the deep learning algorithms include convolutional neural network (CNN) and recurrent neural network (RNN).
[0027] As a preferred technical solution of the present invention, the data collection module can collect data from at least one of social media, online trading platforms, or user behavior logs.
[0028] As a preferred technical solution of the present invention, the recommendation generation module adopts an ensemble learning method and combines the outputs of multiple recommendation algorithms.
[0029] As a preferred technical solution of the present invention, the user feedback module can collect the click-through rate, conversion rate, and satisfaction score of users on the recommendation results in real time.
[0030] As a preferred technical solution of the present invention, the recommendation generation module can dynamically adjust the recommendation results according to the context information of the user.
[0031] As a preferred technical solution of the present invention, the data processing module adopts data augmentation technology.
[0032] As a preferred technical solution of the present invention, the user feedback module adopts the method of deep reinforcement learning.
[0033] As a preferred technical solution of the present invention, the visualization interface of the system can display the recommended content, reasons for recommendation, and feedback results of users.
[0034] The present invention provides an intelligent recommendation problem system based on a large model. It has the following beneficial effects:
[0035] 1. The intelligent recommendation problem system based on large models improves the accuracy and coverage of recommendations by integrating multiple data sources and recommendation algorithms, reducing cold start and data sparsity problems.
[0036] 2. The intelligent recommendation problem system based on large models captures users' potential preferences through deep learning techniques, enhancing the response ability of the recommendation system to changes in user behavior.
[0037] 3. The intelligent recommendation problem system based on large models realizes personalized recommendations, adapts to the diverse needs of users, and improves user satisfaction through dynamic adjustment.
[0038] 4. The intelligent recommendation problem system based on large models is designed with good scalability, can adapt to different application scenarios, and realizes diverse recommendation strategies. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] The structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions for the implementation of the present invention. Therefore, they do not have technical essential significance.
[0040] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained based on the provided drawings.
[0042] The structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limited conditions for the implementation of the present invention. Therefore, they do not have technical essential significance. Any modification of the structure, change of the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.
[0043] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.
[0044] In the description of the embodiments of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "inner", "outer", "side", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the invention product is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.
[0045] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect", "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific situations.
[0046] Embodiment 1
[0047] Please refer to Figure 1 , an intelligent recommendation problem system based on a large model, including a data collection module, a data processing module, a model training module, a recommendation generation module, and a user feedback module;
[0048] The data collection module is responsible for obtaining user behavior data and item feature data from multiple data sources, where the user behavior data includes the user's click, browse, purchase, rating, and review behavior data;
[0049] The data processing module performs data cleaning, deduplication, and feature extraction on the user behavior data;
[0050] The model training module uses deep learning algorithms to train the preprocessed data and construct a recommendation model;
[0051] The recommendation generation module generates recommendation results based on the recommendation model and dynamically updates the recommended content according to user behavior and feedback;
[0052] The user feedback module collects feedback information from users on the recommendation results and optimizes the recommendation algorithm and model parameters;
[0053] The system solves the cold start problem, data sparsity problem, and user diversity problem in traditional recommendation systems by integrating diverse data sources and recommendation algorithms.
[0054] Deep learning algorithms include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
[0055] The data collection module can collect data from at least one of the following channels: social media, online trading platforms, or user behavior logs.
[0056] The recommendation generation module adopts an ensemble learning method, combining the outputs of multiple recommendation algorithms.
[0057] The user feedback module can collect the click-through rate, conversion rate, and satisfaction ratings of users on the recommendation results in real-time.
[0058] The recommendation generation module can dynamically adjust the recommendation results according to the user's context information.
[0059] The data processing module adopts data augmentation techniques.
[0060] The user feedback module adopts the method of deep reinforcement learning.
[0061] The visualization interface of the system can display the user's recommended content, reasons for recommendation, and feedback results.
[0062] Example 2:
[0063] Based on Example 1, this example is an application in the direction of e-commerce platforms:
[0064] In an e-commerce platform, the data collection module accumulates user behavior data by obtaining the user's browsing history, purchase records, and search keywords. The data processing module cleans and extracts features from this data to form a user profile and an item feature matrix. The model training module uses a Convolutional Neural Network (CNN) to process image data related to products and uses a Recurrent Neural Network (RNN) to analyze the user's time series behavior data. The recommendation generation module recommends products in real-time based on the trained model and adjusts the recommendation results according to the user's real-time feedback (such as click and purchase behavior).
[0065] Example 3:
[0066] Based on Example 1, this example is an application in the direction of social network recommendations:
[0067] In a social network, the system collects data on users' social relationships, interaction behaviors, and content preferences through the data collection module. The data processing module analyzes the social network data to extract the user's interest points. The model training module constructs a recommendation model by combining the user's social network structure. The recommendation generation module recommends potentially interesting posts and friends in real-time, and the user feedback module analyzes the user's interactions with the recommended content (such as likes, shares) to optimize the recommendation strategy.
[0068] Example 4:
[0069] Based on Example 1, this example is an application in the direction of streaming media services:
[0070] On the streaming media platform, the system analyzes the user's viewing history, ratings, and review data, and collects user behavior data. The data processing module extracts the user's viewing habits and preferences. The model training module constructs a personalized recommendation model by combining the user's viewing time and content features. The recommendation generation module adjusts the recommended content based on the user's real-time feedback (such as viewing completion rate and skip rate) to ensure its relevance and freshness.
[0071] The working principle of the present invention: The present invention is an intelligent recommendation problem system based on a large model, specifically including the following steps:
[0072] Data collection: The data collection module obtains user behavior data and item feature data from multiple channels to ensure the diversity and richness of the data;
[0073] Data processing: The data processing module preprocesses the collected data, covering data cleaning, deduplication, and feature extraction, to generate a structured data set to support model training;
[0074] Model training: In the model training module, deep learning algorithms are used to train the preprocessed data to construct a recommendation model that can adapt to user preferences;
[0075] Recommendation generation: The recommendation generation module generates personalized recommendation results in real time based on the trained model and dynamically adjusts according to the user's context information;
[0076] User feedback: The user feedback module collects the feedback information of users on the recommendation results in real time, analyzes the user satisfaction and click-through rate, so as to continuously optimize the recommendation algorithm and model parameters.
[0077] By integrating multiple data sources and recommendation algorithms, the accuracy and coverage of recommendations are improved, the cold start and data sparsity problems are reduced, the potential preferences of users are captured through deep learning technology, the response ability of the recommendation system to user behavior changes is enhanced, personalized recommendations are realized, the diverse needs of users are adapted, the user satisfaction is improved through dynamic adjustment, the system design has good scalability, can adapt to different application scenarios, and realizes diverse recommendation strategies.
[0078] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.
Claims
1. An intelligent recommendation problem system based on a large model, characterized in that, It includes a data collection module, a data processing module, a model training module, a recommendation generation module, and a user feedback module; The data collection module is responsible for obtaining user behavior data and item feature data from multiple data sources, where the user behavior data includes click, browse, purchase, rating, and review behavior data of users; The data processing module performs data cleaning, deduplication, and feature extraction on the user behavior data; The model training module uses deep learning algorithms to train the preprocessed data and construct a recommendation model; The recommendation generation module generates recommendation results based on the recommendation model and dynamically updates the recommended content according to user behavior and feedback; The user feedback module collects feedback information from users on the recommendation results and optimizes the recommendation algorithm and model parameters; The system solves the cold start problem, data sparsity problem, and user diversity problem in traditional recommendation systems through comprehensive and diverse data sources and recommendation algorithms.
2. The intelligent recommendation problem system based on a large model according to claim 1, wherein: The deep learning algorithms include convolutional neural network (CNN) and recurrent neural network (RNN).
3. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The data collection module can collect data from at least one of social media, online trading platforms, or user behavior logs.
4. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The recommendation generation module adopts an ensemble learning method and combines the outputs of multiple recommendation algorithms.
5. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The user feedback module can collect the click-through rate, conversion rate, and satisfaction rating of users on the recommendation results in real time.
6. The intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The recommendation generation module can dynamically adjust the recommendation results according to the user's context information.
7. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The data processing module adopts data augmentation techniques.
8. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The user feedback module adopts the method of deep reinforcement learning.
9. An intelligent recommendation problem system based on a large model according to claim 1, characterized in that: The visualization interface of the system can display the recommended content, reasons for recommendation, and feedback results of users.