Hybrid Similarity Matrix in Neighborhood-based Recommendation System

Modern hybrid recommendation methods have successfully mitigated the data sparsity and cold-start problems. Existing hybrid neighborhood-based models adopt both the transaction history and profiles of users and items, although each is used separately in different phases of learning the similarity scores and giving recommendations. This paper proposes utilizing both types of information to measure similarity scores between items, creating a more robust hybrid similarity matrix which helps improve the accuracy of the neighborhood-based models. Comprehensive experiments show that our proposed hybrid similarity matrix can boost the accuracy of neighborhood-based systems by 0.77 - 4.46% compared to the earlier related hybrid methods.