  Collaborative filtering (CF) aims to build a model from users' past behaviors
and/or similar decisions made by other users, and use the model to recommend
items for users. Despite of the success of previous collaborative filtering
approaches, they are all based on the assumption that there are sufficient
rating scores available for building high-quality recommendation models. In
real world applications, however, it is often difficult to collect sufficient
rating scores, especially when new items are introduced into the system, which
makes the recommendation task challenging. We find that there are often "short"
texts describing features of items, based on which we can approximate the
similarity of items and make recommendation together with rating scores. In
this paper we "borrow" the idea of vector representation of words to capture
the information of short texts and embed it into a matrix factorization
framework. We empirically show that our approach is effective by comparing it
with state-of-the-art approaches.
