Clustering- and regression-based multi-criteria collaborative filtering with incremental updates

Abstract

Recommender systems are a valuable means for online users to find items of interest in situations when there exists a large set of alternatives. Collaborative Filtering (CF) is a popular technique to build such systems which is based on explicit rating feedback on the items by a larger user community. Recent research has demonstrated that the predictive accuracy of CF based recommender systems can be measurably improved when multi-criteria ratings are available, i.e., when users provide ratings for different aspects of the recommendable items. Technically, in particular regression-based techniques have been shown to be a promising means to predict the user’s overall assessment of an item based on the multi-criteria ratings.Since in many domains customer subgroups (segments) exist that share similar preferences regarding the item features, we propose a novel CF recommendation approach in which such customer segments are automatically detected through clustering and preference models are learned for each customer segment. In addition, since in practical application constantly new rating information is available, the proposed method supports incremental updates of the preference models. An empirical evaluation of our method shows that the predictions of the resulting models are more accurate than previous multi-criteria recommendation methods.