Research on Personalized Recommendation Based on Matrix Factorization, Clustering, and Deep Reinforcement Learning

Abstract

To address the challenges of data sparsity, cold start, and insufficient dynamic adaptability in traditional recommendation systems, this study proposes a personalized recommendation model named CDRL-MF, which integrates Matrix Factorization, multi-view clustering, and Deep Reinforcement Learning. The method achieves performance improvement through a three-stage collaborative optimization process: First, a dual-channel architecture employing Singular Value Decomposition and an Artificial Neural Network generates high-quality user and item embeddings. Second, multi-view K-means clustering is introduced to construct precise user interest clusters by synthesizing user rating patterns, statistical attributes, and content features. Finally, a Cluster-Guided Deep Reinforcement Recommendation framework is designed, where a DDPG-based agent integrates user states, item cluster features, and real-time feedback to achieve continuous dynamic optimization of recommendation policies. Experimental results on the MovieLens 1M dataset demonstrate that the CDRL-MF model significantly outperforms multiple baseline models across key evaluation metrics, including rating prediction (MAE, RMSE) and Top-N recommendation (Precision, Recall, F1, NDCG). Furthermore, the model exhibits excellent balancing capabilities in recommendation diversity, novelty, and user group fairness. By incorporating differential privacy and federated learning mechanisms, it maintains acceptable performance trade-offs while ensuring user privacy protection. Additional experiments on large-scale datasets such as Amazon Reviews and Netflix Prize further validate its robust generalization capability and practicality.