CS Events

PhD Defense Causal Collaborative Filtering
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Speaker: Shuyuan Xu

Location : CoRE 305

Committee: 

Prof. Yongfeng Zhang (advisor)

Prof. Hao Wang

Prof. Desheng Zhang

Prof. Hamed Zamani (external)

Event Type: PhD Defense

Abstract: In the era of information explosion, recommender systems have become essential for fulfilling users' personalized and complex demands across various services like e-commerce and social media. Collaborative filtering algorithms, fundamental to these systems, traditionally leverage similarities between users and items to provide recommendations, focusing on mining correlative patterns. However, this dissertation introduces causal collaborative filtering methods based on the structural causal model framework to address issues like Simpson's paradox, confounding bias, and echo chambers. These methods shift from correlative to causal learning by formulating recommendations as "what if" questions and applying causal inference techniques. The dissertation presents a comprehensive approach that mitigates various challenges through different types of causal graphs and inference techniques, providing a significant advancement in the field of recommender systems.

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Contact  Professor Yongfeng Zhang