Abstract:

Music is ubiquitous in today's world, and with the rise of streaming platforms, listeners have access to more music than ever before. Music recommender systems aim to help users discover and retrieve music they like and enjoy. Such user-centric retrieval approaches need to capture aspects that influence the user's perception of and preference for music. These aspects include music content (descriptors extracted from the audio signal, such as tempo or acousticness), music context (external factors describing the track or artist, such as the lyrics of a track), user characteristics (long-term descriptors of the user, such as general music preferences), and user context (short-term, dynamic factors describing the user, such as the current activity, occasion, or emotional state). User models that incorporate these aspects provide a better picture of user preferences and enable improved personalization. However, comprehensive user models and recommender systems that can incorporate rich user models are rare. In this talk, I will present comprehensive user and item models and how these models can be used in recommender systems. Furthermore, I will outline our current work on the evaluation of recommender systems and psychology-informed recommender systems.

Followed by a reception with buffet. 

Please register here: