Publication: Towards Personal Explanations for Recommender Systems: A Study on the Impact of Familiarity and Urgency
Towards Personal Explanations for Recommender Systems: A Study on the Impact of Familiarity and Urgency
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Al-Hazwani, I., Ahmed, N., El-Assady, M., & Bernard, J. (2024). Towards Personal Explanations for Recommender Systems: A Study on the Impact of Familiarity and Urgency. Adjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction, 1–8. https://doi.org/10.1145/3677045.3685430
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Recommender systems shape online experiences, but their complexity often hinders user understanding. Explainable recommender systems address this by providing meaningful explanations. Our research explores how explanations can be tailored to individual users and contexts. Through an online survey with 16 participants, we investigated how different levels of domain familiarity and decision-making urgency influence the perception of four types of explanations and their corresponding qualities. Findings shows that transparency and comple
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Al-Hazwani, I., Ahmed, N., El-Assady, M., & Bernard, J. (2024). Towards Personal Explanations for Recommender Systems: A Study on the Impact of Familiarity and Urgency. Adjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction, 1–8. https://doi.org/10.1145/3677045.3685430
