Real-Time Recommendation of Diverse Related Articles

Abstract

News articles typically drive a lot of traffic in the form of comments posted by users on a news site. Such user-generated content tends to carry additional information such as entities and sentiment. In general, when articles are recommended to users, only popularity (e.g., most shared and most commented), recency, and sometimes (manual) editors' picks (based on daily hot topics), are considered. We formalize a novel recommendation problem where the goal is to find the closest most diverse articles to the one the user is currently browsing. Our diversity measure incorporates entities and sentiment extracted from comments. Given the real-time nature of our recommendations, we explore the applicability of nearest neighbor algorithms to solve the problem. Our user study on real opinion articles from aljazeera.net and reuters.com validates the use of entities and sentiment extracted from articles and their comments to achieve news diversity when compared to content-based diversity. Finally, our performance experiments show the real-time feasibility of our solution.