personalization

Challenges and Future Directions for Integration of Large Language Models into Socio-technical Systems

Helma Torkamaan, Steffen Steinert, Maria Soledad Pera, Olya Kudina, Samuel Kernan Freire, Himanshu Verma, Sage Kelly, Marie-Therese Sekwenz, Jie Yang, Karolien van Nunen, Martijn Warnier, Frances Brazier, Oscar Oviedo-Trespalacios Abstract Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration […]

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

Helma Torkamaan, , Catalin-Mihai Barbu, Jürgen 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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