The Magic Barrier of Recommender Systems – No Magic, Just Ratings

TitleThe Magic Barrier of Recommender Systems – No Magic, Just Ratings
Publication TypeConference Paper
Year of Publication2014
AuthorsBellogín, A, Said, A, de Vries, AP
Refereed DesignationRefereed
Conference NameProceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization
Conference LocationAalborg, Denmark

Recommender Systems have to deal with different types of users, who represent their preferences in many ways. This difference in user's behaviour has a deep impact on the final performance of the recommender system, where some users may receive better or worse recommendations depending, mostly, on the quantity and the quality of the information the system knows about the user. Specifically, the inconsistencies of the user impose a lower bound on the error the system may achieve when predicting ratings for that particular user. In this work, we analyse how the consistency of user ratings (coherence) may predict the performance of recommendation methods. More specifically, our results show that our definition of coherence is correlated with the so-called magic barrier, and thus, it could be used to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using a public dataset, where the magic barrier is not available, in which we obtain similar performance improvements.