- Nachiketa Sahoo, R. Krishnan, G. Duncan, Jamie Callan. 2008. On Multi-component Rating and Collaborative Filtering for Recommender Systems : The Case of Yahoo ! Movies. Abstract: Collaborative filtering algorithms learn from the ratings of a group of users on a set of items to find recommendations for each user. Traditionally they have been designed to work with one dimensional ratings. With interest growing in recommending based on multiple aspects of items (Adomavicius and Kwon 2007, Adomavicius and Tuzhilin 2005) we present an algorithm for using multi-component rating data. This mixture model based algorithm uses the dependency structure between the rating components discovered by a structure learning algorithm and validated by the psychometric literature on the halo effect. We evaluate the algorithm using data from Yahoo! Movies. Use of multiple components leads to significant improvements in recommendations when we have small amount of training data–a case very common in real life implementations. However, when there is more training data we gain little from using additional components of the ratings. Beyond generating recommendations we show that the proposed model can fill-in missing rating components. Theories in psychometric literature and the empirical evidence suggest that rating specific aspects of a subject is difficult. Hence, filling in the missing component values leads to the possibility of a rater support system to facilitate gathering of multi-component ratings. We also show that this allows recommendations to be generated for more users.
