RP-LGMC: Rating prediction based on local and global information with matrix clustering. (May 2021) - Zhang, Wen; Wang, Qiang; Yoshida, Taketoshi; Li, Jian
Highlights: The paper proposes the RP-LGMC approach for rating prediction by using both local and global information. Residual approximation and the SVD++ algorithm are proposed to predict ratings on the local and global blocks, respectively. Experiments on the MoviesLens-25 M dataset demonstrate that the proposed RP-LGMC approach can produce better performances than baseline methods. Abstract: Recommendation system has attracted large amount of attention in the field of E-commerce research. Traditional MF (Matrix Factorization) methods take a global view on the user-item rating matrix to derive latent user vectors and latent item vectors for rating prediction. However, there is an inherent structure in the user-item rating matrix and a local correspondence between user clusters and item clusters as the users induce the items and the items imply the users in a recommendation system. Motivated by this observation, this paper proposes a novel rating prediction approach called RP-LGMC (Rating Prediction based on Local and Global information with Matrix Clustering) based on matrix factorization by making use of the local correspondence between user clusters and item clusters. The RP-LGMC approach consists of three components. The first component is to partition the user-item rating matrix into small blocks by the sparse subspace clustering (SCC) algorithm with co-clustering its rows (users) and columns (items) simultaneously. The second component is local distillation to extract those dense and stable blocks by thresholding block density and standard deviation. The third component is to predict the ratings with residual approximation on the local blocks and SVD++ on the global blocks of the original user-item matrix R . The RP-LGMC approach can not only reduce the data sparsity but also increase the computation scalability. Experiments on the MovieLens-25 M dataset demonstrate that the proposed RP-LGMC approach performs better than most state-of-the-art methods in terms of recommendation accuracy and has lower computation complexity than the SVD++ algorithm.