Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

Recommendation Using Textual Opinions / 2684
Claudiu-Cristian Musat, Yizhong Liang, Boi Faltings

Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders.