Semantic ratings and heuristic similarity for collaborative filtering
Robin Burke · 2000
Collaborative filtering systems make recommendations based on ratings of user preference. Usually, the ratings are unidimensional (e.g. like vs. dislike), and can be either explicitly elicited from users or, more typically, are implicitly generated from observations of user behavior. This research examines multi-dimensional or semantic ratings in which a system gets information about the reason behind a preference. Such multidimensional ratings can be projected onto a single dimension, but experiments show that metrics in which the semantic meaning of each rating is taken into account have markedly superior performance. Introduction Collaborative filtering (CF) is a technique for recommending items to a user's attention based on similarities between the past behavior of the user and that of other users. A canonical example is the GroupLens system that recommends news articles based on similarities between users' reading behavior (Resnick, et al. 1994). This technique ha...