Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems

• URL: http://arxiv.org/abs/2306.11279v1
• Date: Tue, 20 Jun 2023 04:16:53 GMT
• Title: Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems
• Authors: Ruixuan Sun, Ruoyan Kong, Qiao Jin, and Joseph A. Konstan
• Abstract summary: We find that users with different rating dispositions may use the recommender system differently. We find that such partitioning improves computational efficiency but also improves top-k performance and predictive accuracy.
• Score: 1.4279471205248533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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