Recommender Systems Today and Tomorrow
In the final episode of our Recommender Systems season, we explore the growing questions of trust, manipulation, privacy, fairness, sustainability, and user control. From fake reviews and shilling attacks to explainable recommendations and user-selected algorithms, we look at what happens when recommender systems must answer not only for what they recommend, but for the consequences of those choices.
Guests
Kyle Polich: Kyle is the founder of Data Skeptic, a popular podcast about artificial intelligence, machine learning, and data science. Outside of hosting the show, he runs a boutique consulting group that helps small and medium enterprise companies deploy data driven automated solutions in the cloud.
Kunal Mukherjee: I am a Postdoctoral Research Associate in the Department of Computer Science at Virginia Tech (Blacksburg, VA), working closely with Dr. Murat Kantarcıoğlu. I received my M.S. and Ph.D. in Computer Science from The University of Texas at Dallas, where I worked in the UTD SysSec Lab with Kangkook Jee and Murat Kantarcıoğlu.
Roan Schellingerhout: Hi there 👋 I’m Roan, a PhD student at Maastricht University supervised by Nava Tintarev and Francesco Barile.
Aditya Chichani: Aditya Chichani is a Senior Machine Learning Engineer at Walmart, where he designs and launches production-grade ML models and scalable systems that power search for millions of shoppers. His work focuses on ranking, retrieval, intent understanding, and bridging gaps in product attribute comprehension. Several initiatives he has led have driven significant GMV lifts and relevance gains across Walmart Search. Before Walmart, Aditya worked as a Software Engineer at Barclays, developing scalable microservices and real-time payment solutions for major clients such as Amazon. He holds a Master's degree in Electrical Engineering & Computer Sciences (EECS) from UC Berkeley, where he specialized in Data Science. He also spent time at the Berkeley Artificial Intelligence Research (BAIR) Lab, working on few-shot learning in NLP using semi-supervised methods under [Prof. Michael Mahoney](https://scholar.google.com/citations?user=QXyvv94AAAAJ&hl=en) and Francisco Utrera. Aditya has received Excellence Awards at Walmart and Barclays, as well as the Fung Excellence Scholarship at UC Berkeley. Beyond work, he mentors UC Berkeley affinity groups in AI and Data Science and stays active in the research community through conferences such as SIGIR, ICDM, CIKM, and RecSys.
Antonio Purificato: I am a second year PhD student in Data Science at Sapienza University of Rome in the Department of Computer, Control and Management Engineering. I am working under the guidance of Professor Fabrizio Silvestri. My research interests include Graph Neural Networks and training with noisy labels, with a focus on the environmental impact of deep learning algorithms. Prior to this, I completed my Master’s Degree in Artificial Intelligence and Robotics at Sapienza University of Rome.
Anas Buhayh: Anas Buhayh is a PhD student at the Recommender Systems Lab at the University of Colorado Boulder, where his research investigates alternative architectures and technologies for building responsible and governable AI systems. His work emphasizes the importance of addressing the needs of stakeholders in sociotechnical systems. Anas also volunteers as a consultant for recommender system design and development. A Fulbright alumnus from Libya, Anas holds a master’s degree in Information Management from Central Michigan University and a bachelor’s degree in Automation from the College of Computer Technologies in Tripoli, Libya. His professional experience includes roles in monitoring and evaluation with the World Health Organization and the International Rescue Committee, where he focused on aligning project outcomes with stakeholder needs. Anas is passionate about data analytics, machine learning, and systems design. In his free time, he enjoys programming, playing the guitar, and staying active.
Fuyuan Lyu: I’m currently a Ph.D. candidate at McGill University and Mila. I am fortunate to be advised by Prof. Jin L.C. Guo and Prof. Xue Liu. Before joining McGill, I earned a bachelor's degree in Computer Science and the Zhiyuan Honour Degree in Engineering from Shanghai Jiao Tong University (SJTU). I was advised by Prof. Li Jiang for my bachelor's thesis and worked with Prof. Xiaokang Yang. I also worked with Prof. Weichen Liu as a visiting research student at Nanyang Technological University (NTU), who inspired me to pursue high-quality research. I am passionate about building data-centric AI. Specifically, I study (i) how to model and select features and (ii) how to provide high-quality labels for both deep learning and foundation models fully automatically. I am eager to free humankind from repetitive, tedious work and enable us to focus on more creative tasks. I am actively working on applications in a variety of domains, including advertising, finance, and software engineering.
Rebecca Salganik: Rebecca Salganik is a computer scientist and musician whose research bridges artificial intelligence, fairness, and music discovery. She is currently pursuing a PhD in Computer Science at the University of Rochester in New York under Prof. Jian Kang, focusing on Individual Fairness in Graphs and Discovery in Recommender Systems. She completed her M.Sc. at Université de Montréal (MILA), where her thesis on fairness-aware music recommendation explored how to mitigate popularity bias in algorithmic systems—read it here. Her research, published at top conferences including ECIR, KDD, FAccT, and RecSys, examines how Graph Neural Networks, Fairness, and Music Recommender Systems can be designed to amplify diverse voices and create more equitable pathways for artistic expression. Rebecca has held research positions at Pinterest, Pandora Labs, Lyft, and MIT Lincoln Labs, and she organizes DEFirst, a reading group focused on fairness in information retrieval. Her background as a classically trained vocalist from McGill University deeply informs her interdisciplinary approach to building ethical AI systems that respect the nuances of musical expression.
Robin Burke: Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. He joined the Department of Information Science in 2019 from the School of Computing at DePaul University. Dr Burke obtained his PhD in Computer Science from Northwestern University in 1993, an MPhil in Computer Science from Yale University in 1990 and a BS in Computer Science from Harvey Mudd College in 1986. In addition to DePaul University, he has held positions at the University of Chicago, the University of California – Irvine, California State University – Fullerton and University College Dublin. He was co-director of DePaul’s Center for Web Intelligence, and a member of DePaul’s interdisciplinary Data Mining and Predictive Analytics Center and also of Studio χ, DePaul’s digital humanities center. Professor Burke is the author of more than 70 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.