Scalable Preference Learning from Data Streams

Fabon Dzogang, Thomas Lansdall-Welfare, Saatviga Sudhahar, Nello Cristianini

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)


We study the task of learning the preferences of online readers of news, based on their past choices. Previous work has shown that it is possible to model this situation as a competition between articles, where the most appealing articles of the day are those selected by the most users. The appeal of an article can be computed from its textual content, and the evaluation function can be learned from training data. In this paper, we show how this task can benefit from an efficient algorithm, based on hashing representations, which enables it to be deployed on high intensity data streams. We demonstrate the effectiveness of this approach on four real world news streams, compare it with standard approaches, and describe a new online demonstration based on this technology.
Original languageEnglish
Title of host publicationWWW '15 Companion Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery (ACM)
ISBN (Print)978-1-4503-3473-0
Publication statusPublished - May 2015
EventNewsWWW 2015 – 2nd workshop on Web and Data Science for News Publishing - WWW'15, Florence, Italy
Duration: 18 May 201522 May 2015


ConferenceNewsWWW 2015 – 2nd workshop on Web and Data Science for News Publishing


  • News popularity
  • Online learning
  • News appeal
  • Learning to rank
  • Hashing trick


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