Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering under Data Sparsity

Data sparsity remains a major challenge for collaborative filtering (CF) systems, where enhancing recommendation accuracy with limited data is crucial.Traditional CF methods depend on common ratings for similarity computation but often ignore users' bounded rationality in neighbor selection and rating behavior.To overcome these limitations, we propose an Expected Utility-based Collaborative Filtering (EUCF) method with three key contributions: (1) a positioning function computing item expected ratings to capture global patterns, (2) a dynamic similarity criterion incorporating item rating frequency and user rating saturation for adaptive neighbor selection (threshold ), and (3) an average deviation correction model addressing imperfect ratings.Experiments on MovieLens-1M and Netflix show EUCF outperforms KNN-IBCF, improving MAE by 3.56% and 4.17%, and MSE by 6.02% and 6.45%, respectively.Moreover, EUCF boosts recommendation coverage by 9.96% and 11%, mitigating data sparsity and long-tail challenges.With reduced computational complexity, EUCF ensures better scalability for real-world deployment.The results demonstrate its effectiveness in balancing accuracy, efficiency, and robustness under sparse data conditions.