"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation

Abstract

On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed approaches, such as Adaptive Control of Thought-Rational (ACT-R), to successfully improve the prediction of the most relevant tracks for the next user session. However, one limitation of using a model inspired by human memory (or the past), is that it struggles to recommend new tracks that users have not previously listened to. To bridge this gap, here we propose a model that leverages audio information to predict in advance the ACT-R-like activation of new tracks and incorporates them into the recommendation scoring process. We demonstrate the empirical effectiveness of the proposed model using proprietary data, which we publicly release along with the model's source code to foster future research in this field.

References in corpus (4)

• Current Challenges and Visions in Music Recommender Systems Research
• Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization
• A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
• Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect