关键词: 项目相关性, 加权Slope One, 协同过滤推荐, 数据稀疏

Abstract: Slope One algorithm is a widely used item-based collaborative filtering recommendation algorithm. But the algorithm does not consider the impact of the item type factors in the process of calculation of the item similarity, leading to type irrelevant items involved in the similarity calculation which influences the effectiveness of the recommendation, and the prediction accuracy is not high in the case of sparse data. In order to solve these problems, this paper considers using the item relevance to measure the relationships between items, and proposes a weighted Slope One algorithm incorporating the relevance of item. The algorithm synthetically calculates the similarity relationship between the items from two aspects: the type of the item itself and the scoring data of the item. On this basis, the item's culling strategy is added, which achieves the goal of stable score difference and getting more intensive item scoring matrix. Finally, the proposed algorithm is applied in the MovieLens data set. The experimental results show that the proposed algorithm not only improves the accuracy of recommendation, but also alleviates the data sparseness problem effectively.

Key words: item relevance, weighted Slope One, collaborative filtering recommendation, data sparseness