Aayesha, A., Afzaal, M., & Neidhardt, J. (2024). User Experience of Recommender System: A User Study of Social-aware Fashion Recommendations System. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 356–361). https://doi.org/10.1145/3631700.3664896
UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
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Event date:
1-Jul-2024 - 4-Jul-2024
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Event place:
Cagliari, Italy
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Number of Pages:
6
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Peer reviewed:
Yes
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Keywords:
Social-aware recommendations; User attributes impact; User Experience of recommender system
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Abstract:
User experience, which encompasses users' feelings and perceptions, is regarded as a key element in the evaluation of recommender systems. The existing literature extensively works on recommendation generation strategies with focus on the accuracy by considering objective aspects of the system. Although some of the current works considered subjective aspects of the recommendation systems from a user-centric perspective to evaluate the recommender system, however, a comprehensive analysis that could investigate factors to improve user experience was of limited focus. In this paper, we propose a methodology that provides a comprehensive multi-perspective analysis of a social-aware fashion recommender system and analyses the impact of user's personal attributes and profiles on their experiences in various aspects of system use. A user study was conducted to realize the proposed methodology. The obtained insights highlighted that user experiences vary not only from the perspective of using a recommender system but also by varying their personal attributes (age, gender, hobby) and profiles.
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Project title:
Christian Doppler Labor für Weiterentwicklung des State-of-the-Art von Recommender-Systemen in mehreren Domänen: CDL Neidhardt (Christian Doppler Forschungsgesells)
