Interacting with Recommender Systems
Title | Interacting with Recommender Systems |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Authors | Jannach, Dietmar, Nunes, Ingrid, Jugovac, Michael |
Conference Name | Proceedings of the 22Nd International Conference on Intelligent User Interfaces Companion |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4893-5 |
Keywords | Human Behavior, human factors, Interaction design, pubcrawl, recommender systems, resilience, Resiliency, Scalability |
Abstract | Automated recommendations have become a common feature of modern online services and mobile apps. In many practical applications, the means provided for users to interact with recommender systems (e.g., to state explicit preferences or to provide feedback on the recommendations) are, however, very limited. In order to improve such systems and consequently user satisfaction, much research work has been done over the years to build richer and more intelligent user interfaces for recommender systems. In this tutorial, we provide a comprehensive overview of existing approaches to user interaction aspects of recommender systems, with a special focus on explanation interfaces. We also provide examples of real-world systems that implement advanced interaction mechanisms and discuss open challenges in the field. |
URL | https://dl.acm.org/citation.cfm?doid=3030024.3030027 |
DOI | 10.1145/3030024.3030027 |
Citation Key | jannach_interacting_2017 |