Saeed Amal
Mustafa Adam
Peter Brusilovsky
Tsvi Kuflik
ABSTRACT
Explainability is an important desired property of recommendation systems. We consider a graph-based recommendation approach, which generates detailed explanations to users in the form of labelled connecting relational paths and present a visualization interface intended to convey such detailed relational information in clear and intuitive manner. As initial evaluation, we performed a user study at an academic conference, recommending participants the users may be interested to meet (using <u>rsr.cloud</u>). The feedbacks were enthusiastic, indicating that the proposed visualizations of relational explanations are engaging and useful.
- Amal, S., Kiflik, T., and Minkov, E. (2017). Harvesting Entity-relation Social Networks from the Web: Potential and Challenges. In: Proceedings of the Conference on User Modeling, Adaptation and Personalization (UMAP) Google Scholar
Digital Library
- Brusilovsky, P., Oh, J. S., L'opez, C., Parra, D. and Jeng, W. (2017). Linking information and people in a social system for academic conferences. New Review of Hypermedia and Multimedia 23 (2) 1--31.. Google ScholarDigital Library
- Sopchoke S. Fukui K., and Nuamo M. (2018). Explainable Cross-Domain Recommendations through Relational Learning In the AAAI conference on Artificial IntelligenceGoogle Scholar
- Tang, J., Hu, X. & Liu, H. (2013). Social recommendation: a review. In: Soc. Netw. Anal. Min. 3: 1113Google ScholarCross Ref
Index Terms
- Enhancing explainability of social recommendation using 2D graphs and word cloud visualizations
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