Real-Time Recommendation of Diverse Related Articles
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Indyk_Real-time recommendation.pdf
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1.01 MB
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Author(s)
Indyk, Piotr
Mahabadi, Sepideh
Abbar, Sofiane
Amer-Yahia, Sihem
Date Issued
May 2013
Journal
Proceedings of the 22nd international conference on World Wide Web (WWW '13)
Publisher
Association for Computing Machinery (ACM)
Citation
Sofiane Abbar, Sihem Amer-Yahia, Piotr Indyk, and Sepideh Mahabadi. 2013. Real-time recommendation of diverse related articles. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1-12.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2488388.2488390
