trust

Beyond Algorithmic Fairness in Recommender Systems

Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan Abstract Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of …

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How can they know that? A study of factors affecting the creepiness of recommendations

Helma Torkamaan, , Catalin-Mihai Barbu, J├╝rgen Ziegler Abstract Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, …

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