Expanding controllability of hybrid recommender systems: From positive to negative relevance

Behnam Rahdari, Chun Hua Tsai, Peter Brusilovsky

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

1 Scopus citations

Abstract

A hybrid recommender system fuses multiple data sources, usually with static and nonadjustable weightings, to deliver recommendations. One limitation of this approach is the problem to match user preference in all situations. In this paper, we present two user-controllable hybrid recommender interfaces, which offer a set of sliders to dynamically tune the impact of different sources of relevance on the final ranking. Two user studies were performed to design and evaluate the proposed interfaces.

Original languageEnglish (US)
Title of host publicationProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
EditorsRoman Bartak, Keith Brawner
PublisherThe AAAI Press
Pages431-434
Number of pages4
ISBN (Electronic)9781577358053
StatePublished - 2019
Event32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019 - Sarasota, United States
Duration: May 19 2019May 22 2019

Publication series

NameProceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019

Conference

Conference32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
CountryUnited States
CitySarasota
Period5/19/195/22/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

Fingerprint Dive into the research topics of 'Expanding controllability of hybrid recommender systems: From positive to negative relevance'. Together they form a unique fingerprint.

Cite this