# Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit

arXiv: https://arxiv.org/abs/2509.02220 · HTML: https://mlsanity.com/paper/2509.02220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [xLSTM: Extended Long Short-Term Memory](https://mlsanity.com/paper/2405.04517/) (2024)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Jointly Learning Explainable Rules for Recommendation with Knowledge Graph](https://mlsanity.com/paper/1903.03714/) (2019)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [News Recommender System: A review of recent progress, challenges, and opportunities](https://mlsanity.com/paper/2009.04964/) (2020)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [Result Diversification in Search and Recommendation: A Survey](https://mlsanity.com/paper/2212.14464/) (2022)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics](https://mlsanity.com/paper/2010.14531/) (2020)
- [Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network](https://mlsanity.com/paper/2102.03787/) (2021)
- [Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation](https://mlsanity.com/paper/2307.16089/) (2023)
- [Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces](https://mlsanity.com/paper/2101.06141/) (2021)
- [Diversifying Multi-aspect Search Results Using Simpson's Diversity Index](https://mlsanity.com/paper/2105.10075/) (2021)
- [Diversity in News Recommendations](https://mlsanity.com/paper/2005.09495/) (2020)
- [End-to-end Learnable Diversity-aware News Recommendation](https://mlsanity.com/paper/2204.00539/) (2022)
- [Characterizing Information Diets of Social Media Users](https://mlsanity.com/paper/1704.01442/) (2017)
- [D-RDW: Diversity-Driven Random Walks for News Recommender Systems](https://mlsanity.com/paper/2508.13035/) (2025)

## Abstract (arXiv)

News recommendations are complex, with diversity playing a vital role. So far, existing literature predominantly focuses on specific aspects of news diversity, such as viewpoints. In this paper, we introduce multi-aspect diversification in four distinct recommendation modes and outline the nuanced challenges in diversifying lists, sequences, summaries, and interactions. Our proposed research direction combines symbolic and subsymbolic artificial intelligence, leveraging both knowledge graphs and rule learning. We plan to evaluate our models using user studies to not only capture behavior but also their perceived experience. Our vision to balance news consumption points to other positive effects for users (e.g., increased serendipity) and society (e.g., decreased polarization).