Less Can Be More: Exploring Population Rating Dispositions with
  Partitioned Models in Recommender Systems
Ruixuan Sun Ruoyan Kong Qiao Jin Joseph A. Konstan
In this study, we partition users by rating disposition - looking first at
their percentage of negative ratings, and then at the general use of the rating
scale. We hypothesize that users with different rating dispositions may use the
recommender system differently and therefore the agreement with their past
ratings may be less predictive of the future agreement.
  We use data from a large movie rating website to explore whether users should
be grouped by disposition, focusing on identifying their various rating
distributions that may hurt recommender effectiveness. We find that such
partitioning not only improves computational efficiency but also improves top-k
performance and predictive accuracy. Though such effects are largest for the
user-based KNN CF, smaller for item-based KNN CF, and smallest for latent
factor algorithms such as SVD.