关键词: CubeALS, CubeSVD, 三维协同过滤, 个性化推荐

Abstract: With the fast development of the Internet, there is high demand for three-dimension (3D) recommendation services in the fields of personalized Web search, personalized advertisement delivery and personalized social bookmarks. It is well known that the cube data are highly sparse and the relationships of 3D objects in the 3D collaborative filtering recommendation system are more complex than those in the 2D collaborative filtering recommendation system. In order to better model the relationships of 3D objects and address the sparsity problem of the cube data, this paper proposes a novel approach to 3D collaborative filtering recommendation, referred to as cube alternating least squares (CubeALS). CubeALS improves the 3D collaborative filtering recommendation algorithm, cube singular value decomposition (CubeSVD), by applying ALS algorithm in the 2D matrix decomposition. The experimental evaluation using a real-world dataset shows that CubeALS achieves better results in comparison with CubeSVD.

Key words: cube alternating least squares (CubeALS), cube singular value decomposition (CubeSVD), 3D collaborative fil-tering, personalized recommendation