Minimal interaction search in recommender systems

Branislav Kveton, Shlomo Berkovsky

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)


While numerous works study algorithms for predicting item ratings in recommender systems, the area of the user-recommender interaction remains largely under-explored. In this work, we look into user interaction with the recommendation list, aiming to devise a method that allows users to discover items of interest in a minimal number of interactions. We propose generalized linear search (GLS), a combination of linear and generalized searches that brings together the benefits of both approaches. We prove that GLS performs at least as well as generalized search and compare our method to several baselines and heuristics. Our evaluation shows that GLS is liked by the users and achieves the shortest interactions.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Intelligent User Interfaces, IUI'15
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)9781450333061
Publication statusPublished - 2015
Externally publishedYes
Event20th International Conference on Intelligent User Interfaces, IUI 2015 - Atlanta, United States
Duration: 29 Mar 20151 Apr 2015


Conference20th International Conference on Intelligent User Interfaces, IUI 2015
CountryUnited States


  • Human-computer interaction
  • interactive search
  • generalized binary search
  • recommender systems
  • active learning


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