CS Events

PhD Defense Fairness in Recommender Systems
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Speaker: Yunqi Li

Location : CoRE 301

Committee: 

Prof. Yongfeng Zhang (Advisor)

Prof. Hao Wang

Prof. Amélie Marian

Prof. Yi Zhang (external)

Event Type: PhD Defense

Abstract: As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making, which gives rise to essential concerns regarding the fairness of such systems. Research on fair machine learning has mainly focused on classification and ranking tasks. Although recommendation algorithm can usually be considered as a type of ranking algorithm, the fairness concerns in recommender systems are more complicated and should be extended to multiple stakeholders. In specific, different from only concerning item exposure fairness in ranking problem, we should also attach importance to the fairness demands of users in recommender systems. To improve user-side fairness in recommendation, we have proposed three works which concentrate on user group-level fairness, user individual-level fairness, and enhancing fairness for cold-start users, respectively.

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Contact  Professor Yongfeng Zhang (Advisor)

Zoom: https://rutgers.zoom.us/j/92923403968?pwd=VitJZEQxU045UWYvOXFjczNOaFZZUT09