6 papers
Benevolent Bias in Multi-Turn Human-Agent Dialogue
Qianqi Liu, Jin Huang, Fethiye Irmak Dogan +1
Bias in human-agent interaction can manifest not only through hostile language but also as benevolent bias, whereby unequal treatment hides behind a warm, positive tone. To make it…
StARS: Socially Appropriate Robot Actions via a Recommender System-Driven Approach
Erencem Ozbey, Fethiye Irmak Dogan, Jin Huang +1
Social appropriateness in human-robot interaction (HRI) is not universal: different people can judge the same robot action differently in the same situation. To capture this inter-…
Toward Personalized Social Robots for Child Well-being: Data Requirement Principles from a Recommender-System Perspective
Jin Huang, Eric Nichols, Fethiye Irmak Dogan +1
Social robots are increasingly deployed in clinical settings to support the well-being of children, where effective support must be personalized to each child. Personalization, cho…
Reimagining Social Robots as Recommender Systems: Foundations, Framework, and Applications
Jin Huang, Fethiye Irmak Doğan, Hatice Gunes
Personalization in social robots refers to the ability of the robot to meet the needs and/or preferences of an individual user. Existing approaches typically rely on large language…
Revisiting Language Models in Neural News Recommender Systems
Yuyue Zhao, Jin Huang, David Vos +1
Neural news recommender systems (RSs) have integrated language models (LMs) to encode news articles with rich textual information into representations, thereby improving the recomm…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…