A Trusted Recommendation Scheme Based on the Improved Slope One Algorithm

Recommender systems have proved to be an important response to the information overload problem, by providing users with more proactive and personalized information services. Collaborative filtering is the most popular method in implementing a recommender system. The Slope One algorithm, which is one of collaborative filtering algorithms, is not only easy to implement, but also efficient and effective. However, the prediction precision of the Slope One algorithm is low. It is important to improve the prediction precision when data is sparse. This paper introduces a trust-based recommend model upon collaborative filtering. Firstly, the fraud users among the raw data set are spotted by employing Bayesian method, and then those fraud ratings are removed; secondly, the degree of trust between users and the degree of trust for ratings itself are calculated; finally, the trust-based recommend model upon collaborative filtering is proposed. We can conclude that the improved Slope One algorithm by employing the trust-based recommend model has a more accurate prediction precision than the traditional Slope One algorithm which is based on user similarity.