# Personalized News Recommendation: Methods and Challenges

arXiv: https://arxiv.org/abs/2106.08934 · HTML: https://mlsanity.com/paper/2106.08934/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 30 of 30)

- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Prompt Learning for News Recommendation](https://mlsanity.com/paper/2304.05263/) (2023)
- [CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent](https://mlsanity.com/paper/2504.13192/) (2025)
- [News Recommendation with Attention Mechanism](https://mlsanity.com/paper/2402.07422/) (2024)
- [LKPNR: LLM and KG for Personalized News Recommendation Framework](https://mlsanity.com/paper/2308.12028/) (2023)
- [When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs](https://mlsanity.com/paper/2309.14488/) (2023)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations](https://mlsanity.com/paper/2408.03772/) (2024)
- [FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning](https://mlsanity.com/paper/2410.17555/) (2024)
- [Modeling User Viewing Flow Using Large Language Models for Article Recommendation](https://mlsanity.com/paper/2311.07619/) (2023)
- [News Recommendation with Category Description by a Large Language Model](https://mlsanity.com/paper/2405.13007/) (2024)
- [Active Human Feedback Collection via Neural Contextual Dueling Bandits](https://mlsanity.com/paper/2504.12016/) (2025)
- [Few-shot News Recommendation via Cross-lingual Transfer](https://mlsanity.com/paper/2207.14370/) (2022)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [Fairness Through Domain Awareness: Mitigating Popularity Bias For Music Discovery](https://mlsanity.com/paper/2308.14601/) (2023)
- [CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation](https://mlsanity.com/paper/2310.09401/) (2023)
- [DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation](https://mlsanity.com/paper/2406.00011/) (2024)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [D-HAN: Dynamic News Recommendation with Hierarchical Attention Network](https://mlsanity.com/paper/2112.10085/) (2021)
- [Simulating News Recommendation Ecosystem for Fun and Profit](https://mlsanity.com/paper/2305.14103/) (2023)
- [Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events](https://mlsanity.com/paper/2411.12880/) (2024)
- [DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems](https://mlsanity.com/paper/2302.01443/) (2023)
- [Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation](https://mlsanity.com/paper/2508.13064/) (2025)
- [Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit](https://mlsanity.com/paper/2509.02220/) (2025)
- [Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation](https://mlsanity.com/paper/2603.05780/) (2026)
- [TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation](https://mlsanity.com/paper/2605.13433/) (2026)
- [ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation](https://mlsanity.com/paper/2607.10910/) (2026)
- [Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB](https://mlsanity.com/paper/2607.26832/) (2026)

## Abstract (arXiv)

Personalized news recommendation is an important technique to help users find their interested news information and alleviate their information overload. It has been extensively studied over decades and has achieved notable success in improving users' news reading experience. However, there are still many unsolved problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation over the past years, in this paper we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This paper can provide up-to-date and comprehensive views to help readers understand the personalized news recommendation field. We hope this paper can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining.