The new similarity measure based on user preference models for collaborative filtering

The recommender system is widely used in many areas in the age of information overload. Collaborative filtering (CF), as one of the most successful methods used for recommendation, recommends items based on the nearest neighbors of the target user. Thus, the performance of the recommender system depends largely on the similarity measure used for selecting neighbors. Most of the traditional similarity measures are based on the rating data that users give to the items, such as Pearson´s correlation and cosine, and suffer from low performances. In order to improve the performance of CF recommender system, this paper proposes a new kind of similarity measure based on user preference models and applies it to a movie recommender system. In this paper, two user preference models are build, one is focused on percentages of different movie genres a user has watched, the other is on the average ratings a user has given to different genres of movies. Based on the two user preference models, two new similarity measures are designed. The experiments compare the performance of the two new similarity measures with the Pearson´s correlation which is widely used in the traditional CF, and are carried out on the MovieLens data set. The results show that the new similarity measure based on the watched genre ratio model outperforms that of traditional CF in all aspects. While the new similarity measure based on the average genre rating model has almost the same performance with the traditional one, but has much less computing complexity for no need in finding the co-rated items.