Collaborative filtering recommendation system based on improved Jaccard similarity

Recommendation performance in collaborative filtering is significantly influenced by the selected similarity measure. The Jaccard similarity measure which considers the number of co-rated items between users has been widely used in CF owing to its good performance and simplicity. However, it does not consider rating information during similarity estimation. Therefore, several studies have been conducted to address the limitations of Jaccard similarity and Rating_Jaccard, which explains that the number of items identically co-rated by two users is one of them. However, Rating_Jaccard still suffers from certain shortcomings: it cannot measure the similarity of many user pairs and provides low similarity values as the number of co-rated items increases. Therefore, this study proposes new similarity measures to address these issues. The recommendation performances of the proposed similarity measures were evaluated on six datasets widely used in recommendation systems: CiaoDVD, FilmTrust, MovieLens100K, MovieLens1M, Amazon, and Netflix. The experimental results show that, on average, the proposed measures outperformed existing alternatives, including Rating_Jaccard.