"i like to explore sometimes": Adapting to dynamic user novelty preferences

Komal Kapoor, Vikas Kumar, Loren Terveen, Joseph A. Konstan, Paul Schrater

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

59 Scopus citations

Abstract

Studies have shown that the recommendation of unseen, novel or serendipitous items is crucial for a satisfying and engaging user experience. As a result, recent developments in recommendation research have increasingly focused towards introducing novelty in user recommendation lists. While, existing solutions aim to find the right balance between the similarity and novelty of the recommended items, they largely ignore the user needs for novelty. In this paper, we show that there are large individual and temporal differences in the users' novelty preferences. We develop a regression model to predict these dynamic novelty preferences of users using features derived from their past interactions. Finally, we describe an adaptive recommender, adaNov-R, that adapts to the user needs for novel items and show that the model achieves better recommendation performance on a metric that considers both novel and familiar items.

Original languageEnglish (US)
Title of host publicationRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages19-26
Number of pages8
ISBN (Electronic)9781450336925
DOIs
StatePublished - Sep 16 2015
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: Sep 16 2015Sep 20 2015

Publication series

NameRecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Other

Other9th ACM Conference on Recommender Systems, RecSys 2015
CountryAustria
CityVienna
Period9/16/159/20/15

Keywords

  • Diversity
  • Dynamic user preferences
  • Novelty
  • Recommendation systems

Cite this