Abstract
Recommender Systems (RecSys) are essential tools in sectors like e-commerce, entertainment, and social media, providing personalized user experiences. Their impact is also growing in education, healthcare, tourism, transport, and logistics, enhancing decision-making and user engagement. Hence, designing modern RecSys requires a multi-disciplinary approach, incorporating machine learning, information retrieval, and human-computer interaction (HCI). This tutorial focuses on human-centric RecSys design, emphasizing both computational methods and user-centered principles. Participants will learn fundamental concepts, advanced algorithms, and practical implementation, with case studies linking visual arts and healthcare applications.
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CITATION STYLE
Yilma, B. A. (2024). Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and Healthcare. In RecSys 2024 - Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1274–1276). Association for Computing Machinery, Inc. https://doi.org/10.1145/3640457.3687091
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