Exploring user-controlled hybrid recommendation in conference contexts

Chun Hua Tsai, Behnam Rahdari, Peter Brusilovsky

Research output: Contribution to journalConference articlepeer-review


A hybrid recommender system fuses multiple data sources to deliver recommendations. One challenge of this approach is to match the changing user preferences with a list of static recommendations. In this paper, we present two user-controllable hybrid recommender interfaces, Relevance Tuner (for people recommendation) and Paper Tuner (for paper recommendation), which offer a set of sliders to tune the multiple relevance sources on the final recommendation ranking on-the-fly. We deployed the user interfaces to a real-world international academic conference with a field study. The result of the log analysis showed the conference attendees did adopt the interface in exploring the hybrid recommendations. The finding provided evidence in supporting the proposed controllable interface can be deployed to a broader set of conference context.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
StatePublished - Jan 1 2019
Event2019 Joint ACM IUI Workshops, ACMIUI-WS 2019 - Los Angeles, United States
Duration: Mar 20 2019 → …

All Science Journal Classification (ASJC) codes

  • General Computer Science


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