A new user similarity model using item genre information for collaborative filtering

Abstract

Collaborative filtering is one of the most widely used approaches in recommendation systems. To enhance the performance of collaborative filtering, it is important to accurately identify similar users or items using a user-item rating matrix. Most similarity measures used in user-based collaborative filtering calculate similarity using the co-rated items between users. However, they suffer from the cold-start problem, because the user-item rating matrix is generally very sparse. To address this issue, this study proposes new similarity measures that exploit item genre information. The proposed similarity measures utilize either the number of rated items per genre or the average rating of genres and they were evaluated by different rating prediction methods including methods to aggregate the predicted ratings for multiple genres to which the item belongs. In addition, this study also proposed an approach to hybridize the proposed similarity measure with conventional similarity measures that do not exploit item genre information. The performance of the proposed similarity measures and the hybrid similarity measures was evaluated using three metrics such as MAE, F1, and NDCG and the empirical results revealed which proposed measures were superior to the others and confirmed the effectiveness and usefulness of the hybrid similarity measures.