Personalized News Recommendation with Context Trees
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2013.
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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 0; 2025: 0; 2026: 1.
11 papers.
- 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Modelscited by 54
- 2023A Survey on Popularity Bias in Recommender Systemscited by 42
- 2021Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendationcited by 7
- 2022RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendationscited by 7
- 2021Personalization for Web-based Services using Offline Reinforcement Learningcited by 3
- 2021Interpretable Personalized Experimentationcited by 3
- 2022Sequential Nature of Recommender Systems Disrupts the Evaluation Processcited by 3
- 2021Learning Student Interest Trajectory for MOOCThread Recommendationcited by 2
- 2021D-HAN: Dynamic News Recommendation with Hierarchical Attention Networkcited by 1
- 2023Practical Policy Optimization with Personalized Experimentationcited by 0
- 2026ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendationcited by 0
- 2026ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation15 Apr 2026
- 2023A Survey on Popularity Bias in Recommender Systems2 Aug 2023
- 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models11 May 2023
- 2023Practical Policy Optimization with Personalized Experimentation30 Mar 2023
- 2022RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations17 Sep 2022
- 2022Sequential Nature of Recommender Systems Disrupts the Evaluation Process26 May 2022
- 2021D-HAN: Dynamic News Recommendation with Hierarchical Attention Network19 Dec 2021
- 2021Interpretable Personalized Experimentation5 Nov 2021
- 2021Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation11 Jul 2021
- 2021Personalization for Web-based Services using Offline Reinforcement Learning10 Feb 2021
- 2021Learning Student Interest Trajectory for MOOCThread Recommendation10 Jan 2021