Recommender Systems

Responsável: Marcelo Manzato

Abstract: In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. These systems typically operate on top of some representation of the users’ profiles, which combine their historical preferences and the immediate context of their interaction with the system (e.g., through their purchases, clicks, ratings). This course offers an overview of the field of recommender systems, including an account of classic approaches to produce and evaluate recommendations, as well as of new trends and research directions in the field. As such, the course is suitable for computer science researchers and practitioners interested in building effective and efficient recommender systems.

Biography: Rodrygo Santos is an assistant professor at the Department of Computer Science of the Federal University of Minas Gerais, Brazil. He received his PhD from the University of Glasgow, UK (2013). He was a visiting researcher at the Terrier Team of the University of Glasgow (2008) and at the search quality team of Google Brazil (2012), and a postdoctoral research associate within the Brazilian National Institute of Science and Technology for the Web (2013). He is a member of the ACM SIGIR, a CNPq research fellow, and an affiliate member of the Brazilian Academy of Sciences. His research interests encompass large-scale search and recommendation in various domains, including the Web, social media, and enterprises. He is a regular speaker and program committee member of several premiere conferences in IR, including SIGIR, CIKM, WWW, WSDM, RecSys, ECIR, ICTIR, AIRS, and SPIRE. He received the best paper award at the 2015 ACM Conference on Recommender Systems. He served as sponsorship co-chair for WSDM 2016 and currently serves as a member of the editorial board of ACM Transactions on Social Computing and Foundations and Trends in Information Retrieval.