# MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels

arXiv: https://arxiv.org/abs/2401.12593 · HTML: https://mlsanity.com/paper/2401.12593/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [User-centered Evaluation of Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2103.06364/) (2021)
- [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://mlsanity.com/paper/2008.09273/) (2020)
- [Reducing Disparate Exposure in Ranking: A Learning To Rank Approach](https://mlsanity.com/paper/1805.08716/) (2018)
- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning](https://mlsanity.com/paper/2201.00140/) (2022)
- [CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems](https://mlsanity.com/paper/2204.08085/) (2022)
- [Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation](https://mlsanity.com/paper/2103.02590/) (2021)
- [Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation](https://mlsanity.com/paper/2105.02951/) (2021)
- [Joint Multisided Exposure Fairness for Recommendation](https://mlsanity.com/paper/2205.00048/) (2022)
- [Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2110.15097/) (2021)
- [Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations](https://mlsanity.com/paper/2006.04282/) (2020)
- [Fair Multi-Stakeholder News Recommender System with Hypergraph ranking](https://mlsanity.com/paper/2012.00387/) (2020)
- [What is fair? Exploring the artists' perspective on the fairness of music streaming platforms](https://mlsanity.com/paper/2106.02415/) (2021)
- [Long-Tail Session-based Recommendation from Calibration](https://mlsanity.com/paper/2112.02581/) (2021)
- [Multi-Objective Recommender Systems: Survey and Challenges](https://mlsanity.com/paper/2210.10309/) (2022)
- [Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations](https://mlsanity.com/paper/2307.00654/) (2023)

## Built on by (most cited first, 1 of 1)

- [OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System](https://mlsanity.com/paper/2504.07108/) (2025)

## Abstract (arXiv)

Multi-Objective Recommender Systems (MORSs) emerged as a paradigm to guarantee multiple (often conflicting) goals. Besides accuracy, a MORS can operate at the global level, where additional beyond-accuracy goals are met for the system as a whole, or at the individual level, meaning that the recommendations are tailored to the needs of each user. The state-of-the-art MORSs either operate at the global or individual level, without assuming the co-existence of the two perspectives. In this study, we show that when global and individual objectives co-exist, MORSs are not able to meet both types of goals. To overcome this issue, we present an approach that regulates the recommendation lists so as to guarantee both global and individual perspectives, while preserving its effectiveness. Specifically, as individual perspective, we tackle genre calibration and, as global perspective, provider fairness. We validate our approach on two real-world datasets, publicly released with this paper.