Abstract
Hybrid social recommender systems use social relevance from multiple sources to recommend relevant items or people to users. To make hybrid recommendations more transparent and controllable, several researchers have explored interactive hybrid recommender interfaces, which allow for a user-driven fusion of recommendation sources. In this field of work, the intelligent user interface has been investigated as an approach to increase transparency and improve the user experience. In this paper, we attempt to further promote the transparency of recommendations by augmenting an interactive hybrid recommender interface with several types of explanations. We evaluate user behavior patterns and subjective feedback by a within-subject study (N=33). Results from the evaluation show the effectiveness of the proposed explanation models. The result of post-treatment survey indicates a significant improvement in the perception of explainability, but such improvement comes with a lower degree of perceived controllability.
Original language | English (US) |
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Pages | 391-396 |
Number of pages | 6 |
DOIs | |
State | Published - 2019 |
Event | 24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States Duration: Mar 17 2019 → Mar 20 2019 |
Conference
Conference | 24th ACM International Conference on Intelligent User Interfaces, IUI 2019 |
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Country/Territory | United States |
City | Marina del Ray |
Period | 3/17/19 → 3/20/19 |
All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction