Informative household recommendation with feature-based matrix factorization
Qiuxia Lu, Diyi Yang, Tianqi Chen, Weinan Zhang, Yong Hai Yu · 2011
In this paper, we describe our solutions to the first track of CAMRa2011 challenge. The goal of this track is to generate a movie ranking list for each household. To achieve this goal, we propose to use the ranking oriented matrix factorization and the matrix factorization with negative examples sampling. We also adopt feature-based matrix factorization framework to incorporate various contextual information to our model, including user-household relations, item neighborhood, user implicit feedback, etc. Finally, we elaborate two kinds of methods to recommend movies for each household based on our models. Experimental results show that our proposed approaches achieve significant improvement over baseline methods.