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Free Text In User Reviews: Their Role In Recommender Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Abstract

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.