A New Similarity Computation Method in Collaborative Filtering Based Recommendation System
Xiaokun Wu, Yongfeng Huang, Shihui Wang · 2017
Recently, collaborative filtering algorithm is widely used in recommendation systems. Similarity calculation is an important step of this algorithm. Some of the existing similarity computing methods can not accurately measure the similarity, and the accuracy needs to be improved. In this paper, a new similarity calculation method is proposed, which is called JacRA. In order to verify the effectiveness of our proposed method, we compare several methods based on MovieLens data sets. Experimental results show that our method outperforms the contrast methods in terms of accuracy.