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

arXiv: https://arxiv.org/abs/2306.11279 · HTML: https://mlsanity.com/paper/2306.11279/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace](https://mlsanity.com/paper/2302.10527/) (2023)
- [Multi-Objective Personalization in Multi-Stakeholder Organizational Bulk E-mail: A Field Experiment](https://mlsanity.com/paper/2302.11156/) (2023)
- [Improving Collaborative Filtering based Recommenders using Topic Modelling](https://mlsanity.com/paper/1402.6238/) (2014)
- [Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models](https://mlsanity.com/paper/2306.07455/) (2023)

## Built on by (most cited first, 3 of 3)

- [Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model](https://mlsanity.com/paper/2402.11948/) (2024)
- [Organizational Bulk Email Systems: Their Role and Performance in Remote Work](https://mlsanity.com/paper/2308.05085/) (2023)
- [What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders](https://mlsanity.com/paper/2401.11632/) (2024)

## Abstract (arXiv)

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.