Free Text In User Reviews: Their Role In Recommender Systems

As short free text user-generated reviews become ubiquitous on the social web, opportunities emerge for new approaches to recommender systems that can harness users‟ reviews in open text form. In this paper we present a first experiment towards the development of a hybrid recommender system which calculates users‟ similarity based on the content of users‟ reviews. We apply this approach to the movie domain and evaluate the performance of LSA, a state-of-the-art similarity measure, at estimating users‟ reviews similarity. Our initial investigation indicates that users‟ similarity is not well reflected in traditional score-based recommender systems which solely rely on users‟ ratings. We argue that short free text reviews can be used as a complementary and effective information source. However, we also find that LSA underperforms when measuring the similarity of short, informal user-generated reviews. For this we argue that further research is needed to develop similarity measures better suited to noisy short text.