Additive Co-Clustering with Social Influence for Recommendation
Xixi Du, Huafeng Liu, Liping Jing · 2017
Recommender system is a popular tool to accurately and actively provide users with potentially interesting information. For capturing the users' preferences and approximating the missing data, matrix completion and approximation are widely adopted. Except for the typical low-rank factorization-based methods, the additive co-clustering approach (ACCAMS) is recently proposed to succinctly approximate large-scale rating matrix. Although ACCAMS efficiently produces effective recommendation result, it still suffers from the cold-start problem. To address this issue, we propose a Social Influence Additive Co-Clustering method (SIACC) by making use of user-item rating data and user-user social relations.