Publication: ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability
ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability
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Al-Hazwani, I., Luo, T., Inel, O., Ricci, F., El-Assady, M., & Bernard, J. (2024). ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability. Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization, 292–304. https://doi.org/10.1145/3631700.3665183
Abstract
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Abstract
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 en
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Al-Hazwani, I., Luo, T., Inel, O., Ricci, F., El-Assady, M., & Bernard, J. (2024). ScrollyPOI: A Narrative-Driven Interactive Recommender System for Points-of-Interest Exploration and Explainability. Proceedings of the ACM Conference on User Modeling, Adaptation and Personalization, 292–304. https://doi.org/10.1145/3631700.3665183
