An Effective Collaborative Filtering Via Enhanced Similarity and Probability Interval Prediction

Abstract

In recent years, as one of the most successful recommendation methods, collaborative filtering has been widely used in the recommendation system. Collaborative filtering predicts the active user preference for goods or services by collecting a historical data set of users' ratings for items; the underlying assumption is that the active user will prefer those items that the similar users prefer. Usually the data is quit sparse, which makes the computation of similarity between users or items imprecise and consequently reduces the accuracy of recommendations. In this paper, we propose an enhanced similarity method that the common ratings and the all ratings are both taken into account. Additionally, we present a generative probabilistic prediction framework in which we first predict a missing data probability value interval instead of a certain value by using the defined range of similar neighbors' ratings, and the final missing data rating is produced in the interval. Empirical studies on two datasets (Mov...