A creative personalized recommendation algorithm—User-based Slope One algorithm

In the field of collaborative filtering recommendation, the accuracy requirement of the recommendation algorithm always makes it complex and hard to realize. Slope One algorithm, as a recent proposed algorithm, was not only easy to achieve, but also efficient and effective. However, Slope One algorithm performs not so well when dealing with personalized recommendation tasks which concern the relationship of users because Slope One Scheme and most of its improved algorithms are item-based collaborative filtering algorithms. To solve these problems, we proposed a User-based Slope One algorithm. The algorithm contains three parts. First, we should calculate the similarity of users. Second, we define a new variable to indicate the relationship between items and users. Third, we add this new variable into the weight of Weighted Slope One algorithm and get the final recommendation expression. We carry a lot of experiments with the MovieLens data set, and the result proves that our algorithm performs more accurate than the Slope One algorithm and the Weighted Slope One algorithm. Furthermore, it shows that User-based Slope One algorithm can improve the recommendation quality of the recommender system and deal with user-related tasks better.