# Personalized Re-ranking for Improving Diversity in Live Recommender Systems

arXiv: https://arxiv.org/abs/2004.06390 · HTML: https://mlsanity.com/paper/2004.06390/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [TADACap: Time-series Adaptive Domain-Aware Captioning](https://mlsanity.com/paper/2504.11441/) (2025)
- [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender](https://mlsanity.com/paper/2306.00009/) (2023)
- [What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders](https://mlsanity.com/paper/2401.11632/) (2024)
- [PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction](https://mlsanity.com/paper/2607.29000/) (2026)

## Abstract (arXiv)

Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the users. However, in practice, list-wise recommendation is implemented as top-N recommendation. Top-N recommendation selects the first N items from candidates to display. The list is generated by a ranking function, which is learned from labeled data to optimize accuracy.However, top-N recommendation may lead to suboptimal, as it focuses on accuracy of each individual item independently and overlooks mutual influence between items. Therefore, we propose a personalized re-ranking model for improving diversity of the recommendation list in real recommender systems. The proposed re-ranking model can be easily deployed as a follow-up component after any existing ranking function. The re-ranking model improves the diversity by employing personalized Determinental Point Process (DPP). DPP has been applied in some recommender systems to improve the diversity and increase the user engagement.However, DPP does not take into account the fact that users may have individual propensities to the diversity. To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user. We implement and deploy the personalized DPP model on alarge scale industrial recommender system. Experimental results on both offline and online demonstrate the efficiency of our proposed re-ranking model.