Improving Neighborhood-Based Collaborative Filtering by a Heuristic Approach and an Adjusted Similarity Measure

“Collaborative filtering” is the most used approach in recommendation systems since it provides good predictions. However, it still suffers from many drawbacks such as sparsity and scalability problems especially for huge datasets which consist of a large number of users and items. This paper presents a new algorithm for neighborhood selection based on two heuristic approaches. The first of which is based on selecting users who rated the same items as the active user called “intersection neighborhood” while the second one builds the neighborhood using all users who rated one item at least as the active user called “union neighborhood”. In addition, we employ an adjusted similarity measure that combines Pearson correlation with a set-similarity measure (such as Jaccard similarity) as a correction coefficient for .accurate similarities among users. Finally, experiments using FilmTrust dataset show that the proposed approaches give more predictions accuracy than the traditional collaborative filtering. Keywords—Collaborative filtering; Neighborhood selection;Spatial complexity; Recommender system; Similarity measure