# Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation

arXiv: https://arxiv.org/abs/2211.01328 · HTML: https://mlsanity.com/paper/2211.01328/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System](https://mlsanity.com/paper/2010.15363/) (2020)
- [FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms](https://mlsanity.com/paper/2002.10764/) (2020)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [Exploring Author Gender in Book Rating and Recommendation](https://mlsanity.com/paper/1808.07586/) (2018)
- [FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems](https://mlsanity.com/paper/2005.01148/) (2020)
- [Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge](https://mlsanity.com/paper/2208.06089/) (2022)
- [A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems](https://mlsanity.com/paper/1708.09088/) (2017)
- [Recent Advances in Diversified Recommendation](https://mlsanity.com/paper/1905.06589/) (2019)
- [User-item matching for recommendation fairness](https://mlsanity.com/paper/2009.14474/) (2020)
- [MatRec: Matrix Factorization for Highly Skewed Dataset](https://mlsanity.com/paper/2011.04395/) (2020)
- [Data Context Adaptation for Accurate Recommendation with Additional Information](https://mlsanity.com/paper/1908.08469/) (2019)
- [Recommendation under Capacity Constraints](https://mlsanity.com/paper/1701.05228/) (2017)

## Built on by (most cited first, 2 of 2)

- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation](https://mlsanity.com/paper/2408.02156/) (2024)

## Abstract (arXiv)

When recommending personalized top-k items to users, how can we recommend the items diversely to them while satisfying their needs? Aggregately diversified recommender systems aim to recommend a variety of items across whole users without sacrificing the recommendation accuracy. They increase the exposure opportunities of various items, which in turn increase potential revenue of sellers as well as user satisfaction. However, it is challenging to tackle aggregate-level diversity with a matrix factorization (MF), one of the most common recommendation model, since skewed real world data lead to skewed recommendation results of MF. In this work, we propose DivMF (Diversely Regularized Matrix Factorization), a novel matrix factorization method for aggregately diversified recommendation. DivMF regularizes a score matrix of an MF model to maximize coverage and entropy of top-k recommendation lists to aggregately diversify the recommendation results. We also propose an unmasking mechanism and carefully designed mi i-batch learning technique for accurate and efficient training. Extensive experiments on real-world datasets show that DivMF achieves the state-of-the-art performance in aggregately diversified recommendation.