Author(s)

Mei Yangyang, Xiao Zhenghong, Ouyang Jia, Yan Yiting, Xu Shengdong

Corresponding Author

Mei Yangyang

ABSTRACT

In view of the low accuracy of the Slope One personalized recommendation algorithm because of ignoring user trust and project similarity, a weighted Slope One algorithm that integrates the user trust factor is proposed in this work. This study considers the proportion of users’ common-score items to the number of items scored by the target users, develops user trust factor model and algorithms, uses the Pearson correlation coefficient to calculate user similarity, introduces the trust factor to modify user similarity and obtain the target users’ top-K nearest neighbor sets, and uses a modified weighted Slope One algorithm for the predictive analysis of a sample. Experiments are conducted using the MovieLens data set. Results show that the proposed method improves the accuracy of prediction and effectively improves recommendation accuracy.

KEYWORDS

Collaborative Filtering, Slope One, Trust Factor, User Similarity