A New User Similarity Measure for Collaborative Filtering Algorithm

This paper proposes a new user similarity measure to improve the collaborative filtering algorithm. We apply a basic fractional function and an exponential function to calculate the similarity between users by taking both common features and different features into consideration. We test our two measures on two data sets, movie lens and book-crossing data sets. Experiment results show that our basic fractional function slightly improves the performance, while exponential function significantly outperforms other similarity measures.