Recommender systems are playing a crucial role in modern society, enabling individuals and organizations to personalize their experiences. These algorithms analyze vast amounts of user data on preferences, behaviour, and demographics to offer tailored recommendations for products, services, and content. By facilitating more personalized flows of information, recommender systems can enable users to find content that is relevant to them, connect with like-minded individuals, and discover new opportunities by encouraging exploration.
In this special in-person event, SRI Research Lead Ashton Anderson will host a talk by Mounia Lalmas, senior director of research at Spotify, on what principles are required for designing successful recommender systems. Lalmas will share insights from her research aimed at fulfilling Spotify’s mission to match audiences and creators in ways that are personally relevant, using techniques such as machine learning and metric validation that encompass search and recommendation functions, and are built through sustained considerations based in understanding a user’s journey, optimizing for the right metric, and thinking about diversity.
Venue
Rotman School of Management, University of Toronto, Room 1065.
Entrance: 95 St. George Street, Toronto ON M5S 2E8
About Mounia Lalmas
Mounia Lalmas is a senior director of research at Spotify, and the head of tech research in personalization, where she leads an interdisciplinary team of research scientists, working on personalization. Lalmas also holds an honorary professorship at University College London, and an additional appointment as a Distinguished Research Fellow at the University of Amsterdam. Before that, she was a director of research at Yahoo, where she led a team of researchers working on advertising quality. She also worked with various teams at Yahoo on topics related to user engagement in the context of news, search, and user-generated content. Prior to this, she held a Microsoft Research/RAEng Research Chair at the School of Computing Science, University of Glasgow. Before that, she was a professor of information retrieval in the Department of Computer Science at Queen Mary, University of London. Lalmas is regularly a senior programme committee member at conferences such as WSDM, KDD, WWW and SIGIR. She was programme co-chair for SIGIR 2015, WWW 2018 and WSDM 2020, and is one of CIKM 2023 programme co-chairs.
About Ashton Anderson
Ashton Anderson is an assistant professor in the Department of Computer Science at the University of Toronto, a faculty affiliate with the Vector Institute, and a research lead at the Schwartz Reisman Institute for Technology and Society. Anderson’s research in computational social science encompasses a diverse range of questions at the intersection of AI, data, and society. His work has appeared in venues including the Proceedings of the National Academy of Sciences, Management Science, and the International Conference on Machine Learning. In 2021, he co-authored a white paper published by Schwartz Reisman Institute on the impact of recommender systems on the consumption of music.
About the Schwartz Reisman Institute
Located at the University of Toronto, the Schwartz Reisman Institute for Technology and Society’s mission is to deepen our knowledge of technologies, societies, and what it means to be human by integrating research across traditional boundaries and building human-centred solutions that really make a difference. The integrative research we conduct rethinks technology’s role in society, the contemporary needs of human communities, and the systems that govern them. We’re investigating how best to align technology with human values and deploy it accordingly. The human-centred solutions we build are actionable and practical, highlighting the potential of emerging technologies to serve the public good while protecting citizens and societies from their misuse. We want to make sure powerful technologies truly make the world a better place—for everyone.
Mounia Lalmas