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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| ISBN | 979-8-4007-0466-6 |
| Page Range | 292 - 304 |
| Event Title | ACM Conference on User Modeling, Adaptation and Personalization (UMAP) |
| Event Type | conference |
| Event Location | Cagliari, Italy |
| Event Start Date | July 1 - 2024 |
| Event End Date | July 4 - 2024 |
| Series Name | Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization |
| Publisher | ACM Digital library |
| Abstract Text | Recommender systems can help web users find more relevant content, improve their online experience, and support them in the discovery of new Points-of-Interest (POI). Yet, challenges persist in dealing with the cold-start problem and in recommendation explainability. To address these, we have created ScrollyPOI, an interactive POI recommender system based on Data Humanism principles. Utilizing scrollytelling, we address the cold-start problem by engaging users in reflecting on previous positive experiences. Additionally, ScrollyPOI enhances explainability through input and output explanations. The system uses stacked bar charts and word clouds to explain how user preferences inform recommendations (input). Finally, ScrollyPOI employs a multi-layered approach to explain why specific POIs are recommended (output). We have evaluated ScrollyPOI’s interface and experience through a preliminary study, highlighting its potential for transparent explanations in the POI recommendation domain. Our findings underscore ScrollyPOI’s efficacy in collecting preferences and enhancing recommendation transparency, positioning it as a platform for studying explainability goals in the POI domain. |
| Free access at | DOI |
| Digital Object Identifier | 10.1145/3631700.3665183 |
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