Modeling Behaviors of Browsing and Buying for Alidata Discovery Using Joint Non-negative Matrix Factorization

In this paper, we propose a novel recommender algorithm for the data competition launched by the Alibaba Group based on the intuition that a user´s buying behaviors will be influenced by the user´s browsing behaviors on the web, which means that the latent preferences that lie behind these two behaviors are consistent. We present a matrix factorization framework that fuses a user-item buying matrix with a user-item browsing matrix using joint nonnegative matrix factorization. This approach assumes that the two factorized coefficient matrices obtained from the browsing matrix and the buying matrix should be regularized toward a common consensus. The experimental results show that our algorithm outperforms other algorithms based only on a single matrix factorization.