# D-RDW: Diversity-Driven Random Walks for News Recommender Systems

arXiv: https://arxiv.org/abs/2508.13035 · HTML: https://mlsanity.com/paper/2508.13035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2025.

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

- [Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit](https://mlsanity.com/paper/2509.02220/) (2025)

## Abstract (arXiv)

This paper introduces Diversity-Driven RandomWalks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations. D-RDW is a societal recommender, which combines the diversification capabilities of the traditional random walk algorithms with customizable target distributions of news article properties. In doing so, our model provides a transparent approach for editors to incorporate norms and values into the recommendation process. D-RDW shows enhanced performance across key diversity metrics that consider the articles' sentiment and political party mentions when compared to state-of-the-art neural models. Furthermore, D-RDW proves to be more computationally efficient than existing approaches.