# News Recommender System: A review of recent progress, challenges, and opportunities

arXiv: https://arxiv.org/abs/2009.04964 · HTML: https://mlsanity.com/paper/2009.04964/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 24 of 24)

- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Active Reward Learning from Multiple Teachers](https://mlsanity.com/paper/2303.00894/) (2023)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Performance Prediction in Major League Baseball by Long Short-Term Memory Networks](https://mlsanity.com/paper/2206.09654/) (2022)
- [Docent: A content-based recommendation system to discover contemporary art](https://mlsanity.com/paper/2207.05648/) (2022)
- [Understanding the Relation of User and News Representations in Content-Based Neural News Recommendation](https://mlsanity.com/paper/2207.14704/) (2022)
- [BERT4Loc: BERT for Location -- POI Recommender System](https://mlsanity.com/paper/2208.01375/) (2022)
- [Navigating News Narratives: A Media Bias Analysis Dataset](https://mlsanity.com/paper/2312.00168/) (2023)
- [Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time](https://mlsanity.com/paper/2312.03750/) (2023)
- [Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity](https://mlsanity.com/paper/2410.12086/) (2024)
- [Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events](https://mlsanity.com/paper/2411.12880/) (2024)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Explicit User Manipulation in Reinforcement Learning Based Recommender Systems](https://mlsanity.com/paper/2203.10629/) (2022)
- [Hybrid moderation in the newsroom: Recommending featured posts to content moderators](https://mlsanity.com/paper/2307.07317/) (2023)
- [A Novel Method for News Article Event-Based Embedding](https://mlsanity.com/paper/2405.13071/) (2024)
- [Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation](https://mlsanity.com/paper/2407.18937/) (2024)
- [Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit](https://mlsanity.com/paper/2509.02220/) (2025)
- [Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation](https://mlsanity.com/paper/2510.09136/) (2025)
- [Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks](https://mlsanity.com/paper/2601.04019/) (2026)
- [A Gated Hybrid Contrastive Collaborative Filtering Recommendation](https://mlsanity.com/paper/2604.27117/) (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)
- [From Entity Mentions to Tone: An LLM-Based Pipeline for Media Bias Analysis](https://mlsanity.com/paper/2608.17454/) (2026)

## Abstract (arXiv)

Nowadays, more and more news readers tend to read news online where they have access to millions of news articles from multiple sources. In order to help users to find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that users might be interested in. In this paper, we highlight the major challenges faced by the news recommendation domain and identify the possible solutions from the state-of-the-art. Due to the rapid growth of building recommender systems using deep learning models, we divide our discussion in two parts. In the first part, we present an overview of the conventional recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in NRS. In the second part, we explain the deep learning-based recommendation solutions applied in NRS. Different from previous surveys, we also study the effects of news recommendations on user behavior and try to suggest the possible remedies to mitigate these effects. By providing the state-of-the-art knowledge, this survey can help researchers and practical professionals in their understanding of developments in news recommendation algorithms. It also sheds light on potential new directions