# Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality

arXiv: https://arxiv.org/abs/1812.11422 · HTML: https://mlsanity.com/paper/1812.11422/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders](https://mlsanity.com/paper/2308.12256/) (2023)
- [Hierarchical Latent Relation Modeling for Collaborative Metric Learning](https://mlsanity.com/paper/2108.04655/) (2021)
- [G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation](https://mlsanity.com/paper/2508.05709/) (2025)
- [Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks](https://mlsanity.com/paper/2102.09635/) (2021)

## Abstract (arXiv)

Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user preferences in order to recommend more positive items at the top of recommendation-lists. We build upon existing machine-learning model to incorporate the contextual information provided by negative user preference. With experimental evaluations on two openly available datasets, we show that our method is able to improve recommendation quality: by improving accuracy and at the same time reducing the number of negative items at the top of recommendation-lists. Our work demonstrates the value of the contextual information provided by negative feedback, and can also be extended to signed social networks and link prediction in other networks.