Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles / 830
Claudiu Cristian Musat, Boi Faltings
Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify user profiles as a basis for similarity. We show that careful use of the available data and separating users into classes can greatly improve the performance of such techniques. We significantly improve MAE, RMSE, and Kendall tau, compared to the previous best results. In addition, we show that personalization does not benefit all the users to the same extent. We propose switching between a personalized and a non personalized method based on the user opinion profile. We show that the user's opinionatedness is a good indicator of whether the personalization will work or not.