Personalizing the listening experience (slides)
Bandit algorithms have gained increased attention in recommender systems, as they provide effective and scalable recommendations. These algorithms use reward functions, usually based on a numeric variable such as click-through rates, as the basis for optimization. Detecting and understanding implicit measures of user satisfaction are essential for enhancing recommendation quality. When users interact with a recommendation system, they leave behind fine grained traces of interaction signals, which contain valuable information that could help gauging user satisfaction. Quantifying such a notion of satisfaction from implicit signals involves understanding the diverse needs of users and their expectations of what is a successful streaming session. Such needs often include how users feel, and the expectations that music recommended to them align with their mood or their intent of the moment. To fulfill these, Spotify provide users with curated playlists, ranging from ''sleep" to ''run". In addition, and to account for the diverse user interests and plethora of musics, genre playlists have been made available to users, ranging from rap, pop, jazz, to niche ones. As a result, millions of playlists are available to users to listen to based on their intent and needs. Given such a heterogeneity in user needs, and the different intents of content (playlists), it becomes important to consider both user and content behavior to formalize the notion of satisfaction, and in turn design the appropriate reward models to capture these. This talk will describe methods explored to provide a more informed reward function, that account for the type of user and the type of content.
Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in the Personalization mission of Spotify, where she leads an interdisciplinary team of research scientists working on personalization and discovery. Mounia also holds an honorary professorship at University College London. Before that, she was a Director of Research at Yahoo, where she led a team of researchers working on advertising quality for Gemini, Yahoo native advertising platform. 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 Professor of Information Retrieval at the Department of Computer Science at Queen Mary, University of London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now music. She has given numerous talks and tutorials on these and related topics. She is regularly a senior programme committee member at conferences such as WSDM, WWW and SIGIR. She was co-programme chair for SIGIR 2015 and WWW 2018. She is also the co-author of a book written as the outcome of her WWW 2013 tutorial on "measuring user engagement".