Author ORCID Identifier
Document Type
Article
Publication Date
3-20-2019
Publication Title
IUI Workshops’
Volume
19
Abstract
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.
Recommended Citation
Tsai, Chun-Hua; Rahdari, Behnam; and Brusilovsky, Peter, "Exploring User-Controlled Hybrid Recommendation in Conference Contexts" (2019). Information Systems and Quantitative Analysis Faculty Publications. 130.
https://digitalcommons.unomaha.edu/isqafacpub/130
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
This is an open access publication that is licensed under Creative Commons Attribution and can be accessed at https://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-8.pdf or https://ceur-ws.org/Vol-2327/