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

Distributed-Representation Based Hybrid Recommender System with Short Item Descriptions