Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems
Ruixuan Sun, Ruoyan Kong, Qiao Jin, Joseph A. Konstan · 2023
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.