Interaction Modeling and Fairness in Recommendation

Title: Interaction Modeling and Fairness in Recommendation

Abstract: Recommender systems are ubiquitous: they connect us to jobs, news, media, and friends, fundamentally shaping our experiences. Two challenges in modern recommenders motivate much of my ongoing research: (i) how to carefully model user-item interactions that are essential for driving these systems; and (ii) how to combat unfairness and bias that are seemingly inherent in recommenders. In this talk I will present recent work in my lab on sequential hypergraphs to tackle the first challenge, and then highlight a series of works on combating bias. I'll conclude with thoughts on important challenges and next steps.

Bio: James Caverlee is a Professor at Texas A&M University in the Department of Computer Science and Engineering. His research targets topics from recommender systems, social media, information retrieval, data mining, and emerging networked information systems. His group has been supported by NSF, DARPA, AFOSR, Amazon, and Google, among others. Caverlee was general co-chair of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020), and has been a senior program committee member of venues like KDD, SIGIR, SDM, WSDM, and ICWSM.

To attend this talk via Zoom, click here. Participants will need a passcode to attend this event. If you need the passcode for this series, please see the event advertisement on the seminars email list or reach out to Hamed Zamani. For any questions about this event with the Center for Intelligent Information Retrieval, please contact Hamed Zamani.