  Matrix factorization (MF) is one of the most efficient methods for rating
predictions. MF learns user and item representations by factorizing the
user-item rating matrix. Further, textual contents are integrated to
conventional MF to address the cold-start problem. However, the textual
contents do not reflect all aspects of the items. In this paper, we propose a
model that leverages the information hidden in the item co-click (i.e., items
that are often clicked together by a user) into learning item representations.
We develop TCMF (Textual Co Matrix Factorization) that learns the user and item
representations jointly from the user-item matrix, textual contents and item
co-click matrix built from click data. Item co-click information captures the
relationships between items which are not captured via textual contents. The
experiments on two real-world datasets MovieTweetings, and Bookcrossing)
demonstrate that our method outperforms competing methods in terms of rating
prediction. Further, we show that the proposed model can learn effective item
representations by comparing with state-of-the-art methods in classification
task which uses the item representations as input vectors.
