Toward better interactions in recommender systems: Cycling and serpentining approaches for top-N item lists

Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn Willemsen, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Scopus citations

Abstract

Current recommender systems often show the same mosthighly recommended items again and again ignoring the feedback that users neither rate nor click on those items. We conduct an online field experiment to test two ways of manipulating top-N recommendations with the goal of improving user experience: cycling the top-N recommendation based on their past presentation and serpentining the top-N list mixing the best items into later recommendation requests. We find interesting tensions between opt-outs and activities, user perceived accuracy and freshness. Cycling within the same session might be a "love it or hate it" recommender property because users in it have a higher opt-out rate but engage in more activities. Cycling across sessions and serpentining increase user activities without significantly affecting opt-out rates. Users perceive more change and freshness but less accuracy and familiarity. Combining cycling and serpentining does not work as well as each individual manipulation separately. These two ways of manipulations on top-N list demonstrate some attractive properties but also call for innovative approaches to overcome their potential costs.
Original languageEnglish (US)
Title of host publication[CSCW2017]Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
Pages1444-1453
Number of pages10
DOIs
StatePublished - 2017

Publication series

Name[CSCW2017]Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

Keywords

  • field experiment
  • recommender systems
  • user study

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