On the Improvement of E-Commerce Based Recommender Systems

Recommender systems are essential for improving user experience in e-commerce by providing personalized product suggestions. However, traditional systems often struggle with the cold start problem, particularly for new users with minimal interaction history. This research proposes a Monte Carlo-based pagerank hybrid recommender system (MCPRHRec), which integrates Monte Carlo-based PageRank into the Multi-source Category Extended Historical Sequential Recommendation (MCE-HSPRec) System to enhance recommendation accuracy. By constructing a user-category graph, the system generates enriched user profiles and effectively mitigates the user cold start issue. Experimental results on an e-commerce dataset demonstrate significant improvements in recommendation performance, achieving a 97.09% increase in F1-score, 99.08% improvement in recall, 95.19% enhancement in precision, and 94.45% advancement in accuracy compared to existing methods. The proposed approach offers a scalable and efficient solution for personalized recommendations, providing a valuable contribution to the field of recommender systems.