Personalized News Recommendation with Context Trees

Abstract

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.

References in corpus (1)

• A Contextual-Bandit Approach to Personalized News Article Recommendation

Cited by in corpus (10)

• Evaluation of Session-based Recommendation Algorithms
• Empirical Analysis of Session-Based Recommendation Algorithms
• A Survey on Popularity Bias in Recommender Systems
• Leveraging Large Language Models for Sequential Recommendation
• CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation
• Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences
• Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
• A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
• A Graph-based Method for Session-based Recommendations
• Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests