# Personalized News Recommendation with Context Trees

arXiv: https://arxiv.org/abs/1303.0665 · HTML: https://mlsanity.com/paper/1303.0665/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2013.

## Built on by (most cited first, 11 of 11)

- [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://mlsanity.com/paper/2305.06566/) (2023)
- [A Survey on Popularity Bias in Recommender Systems](https://mlsanity.com/paper/2308.01118/) (2023)
- [Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation](https://mlsanity.com/paper/2107.05124/) (2021)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [Personalization for Web-based Services using Offline Reinforcement Learning](https://mlsanity.com/paper/2102.05612/) (2021)
- [Interpretable Personalized Experimentation](https://mlsanity.com/paper/2111.03267/) (2021)
- [Sequential Nature of Recommender Systems Disrupts the Evaluation Process](https://mlsanity.com/paper/2205.13681/) (2022)
- [Learning Student Interest Trajectory for MOOCThread Recommendation](https://mlsanity.com/paper/2101.05625/) (2021)
- [D-HAN: Dynamic News Recommendation with Hierarchical Attention Network](https://mlsanity.com/paper/2112.10085/) (2021)
- [Practical Policy Optimization with Personalized Experimentation](https://mlsanity.com/paper/2303.17648/) (2023)
- [ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation](https://mlsanity.com/paper/2604.14114/) (2026)

## Abstract (arXiv)

The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. In addition, they are subject to trends and preference changes over time, and in many cases we do not have sufficient information to profile the reader. In this paper, we introduce a class of news recommendation systems based on context trees. They can provide high-quality news recommendation to anonymous visitors based on present browsing behaviour. We show that context-tree recommender systems provide good prediction accuracy and recommendation novelty, and they are sufficiently flexible to capture the unique properties of news articles.