34 citations · 49 across the 7 of their papers we have counts for
7 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…
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic
Jin Huang, Harrie Oosterhuis, Maarten de Rijke
User interactions with recommender systems (RSs) are affected by user selection bias, e.g., users are more likely to rate popular items (popularity bias) or items that they expect…