Multimedia Recommendation via Convolutional Neural Network Feature Extraction
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Solution Overview
Problem
Current multimedia recommendation systems face challenges in accurately recommending new multimedia resources due to the lack of user behavior data, known as the 'cold start' problem, and struggle with information overload, leading to suboptimal recommendation performance.
Innovation Solution
The method employs a convolutional neural network with user interest and attention information to generate feature matrices, which are then processed and fused to create user preference data, effectively addressing the cold start issue and improving recommendation accuracy by emphasizing relevant features and suppressing irrelevant ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional collaborative filtering methods are used for recommendation, then the system can leverage user behavior data, but it fails to accurately recommend new multimedia resources due to the cold start problem
Solution Approach 1:
The patent segments the recommendation problem into multiple convolutional layers, each extracting features at different levels of abstraction. This segmentation allows the system to process multimedia resources independently of user behavior data, enabling accurate recommendations for new resources without relying on collaborative filtering
Solution Approach 2:
The system performs preliminary feature extraction and user preference modeling before actual recommendation occurs. By pre-processing multimedia resources through convolutional layers to extract intrinsic features and model user preferences independently, the system prepares recommendation capabilities that work even when user behavior data is unavailable
2Reliability
If the system processes all multimedia resources uniformly, then it maintains simplicity, but it cannot effectively address information overload and reduce recommendation quality
Solution Approach 1:
The patent applies local quality by using different convolutional layers with different kernels to process different aspects of multimedia resources. Each layer focuses on specific local features (e.g., edges, textures, patterns), and the system selectively combines these local features based on user preferences, thereby improving recommendation quality without uniformly processing all resources with maximum complexity
Solution Approach 2:
The system performs partial action by selectively processing only the most relevant features for each recommendation task. Through attention mechanisms and preference-based filtering, the system focuses computational resources on extracting and weighting only the critical features needed for accurate recommendations, rather than uniformly processing all possible features
3Measurement precision
If the system uses deep convolutional networks with multiple layers, then it can extract comprehensive features, but it increases computational complexity and processing time
Solution Approach 1:
The system performs preliminary feature extraction through pre-trained convolutional layers that capture common patterns and features across multimedia resources. By pre-processing and caching extracted features, the system reduces the computational burden during actual recommendation tasks, maintaining high feature extraction accuracy while reducing real-time processing time
Solution Approach 2:
The system applies partial action by selectively activating and processing only the most relevant convolutional layers based on the specific recommendation task and user preferences. Rather than always processing through all N layers uniformly, the system adapts the depth of processing to the task requirements, reducing unnecessary computational steps while maintaining extraction accuracy for critical features
Data Source
AI summary
The present disclosure provides a method, an apparatus, an electronic device, and a storage medium for recommending multimedia resource, and relates to the field of machine learning. The method includes: acquiring features of the multimedia resource based on a convolutional neural network, where the convolutional neural network comprises N convolutional layers, where N is a positive integer; determining user interest information based on an identifier of a recommended user, where the user interest information is corresponding to the feature of each convolutional layer; determining a first feature matrix based on the convolution of convolution kernel and the feature, where the convolution kernel comprises the user interest information; generating user preference data based on the first feature matrix; and recommending the multimedia resource to the recommended user based on the N generated user preference data.