Real-time recommendation of diverse related articles

Sofiane Abbar, Sihem Amer-Yahia, Piotr Indyk, Sepideh Mahabadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Citations (Scopus)

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 realtime 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. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish
Title of host publicationWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
Pages1-11
Number of pages11
Publication statusPublished - 1 Dec 2013
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: 13 May 201317 May 2013

Publication series

NameWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Other

Other22nd International Conference on World Wide Web, WWW 2013
CountryBrazil
CityRio de Janeiro
Period13/5/1317/5/13

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Keywords

  • Diversity
  • Recommender system
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Abbar, S., Amer-Yahia, S., Indyk, P., & Mahabadi, S. (2013). Real-time recommendation of diverse related articles. In WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web (pp. 1-11). (WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web).