Novelty learning via collaborative proximity filtering

Arun Kumar, Paul Schrater

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

3 Scopus citations


The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience. However, spontaneous changes in user preferences are ubiquitous in many domains like media consumption and key factors that drive changes in preferences are not directly observable. These latent sources of preference change pose new challenges. When systems do not track and adapt to users' tastes, users lose confidence and trust, increasing the risk of user churn. We meet these challenges by developing a model of novelty preferences that learns and tracks latent user tastes. We combine three innovations: A new measure of item similarity based on patterns of consumption co-occurrence; model for spontaneous changes in preferences; and a learning agent that tracks each user's dynamic preferences and learns individualized policies for variety. The resulting framework adaptively provides users with novelty tailored to their preferences for change per se.

Original languageEnglish (US)
Title of host publicationIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450343480
StatePublished - Mar 7 2017
Event22nd International Conference on Intelligent User Interfaces, IUI 2017 - Limassol, Cyprus
Duration: Mar 13 2017Mar 16 2017

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI


Other22nd International Conference on Intelligent User Interfaces, IUI 2017


  • Boredom
  • Implicit preferences
  • Latent tastes
  • Novelty
  • Recommender systems
  • User behaviors
  • User preferences


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