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

TL;DR

This paper introduces a novel music recommendation model that combines human memory-inspired techniques with audio analysis to better suggest both familiar and new tracks, enhancing prediction accuracy.

Contribution

It proposes a new approach that integrates audio features with cognitive-inspired memory models to improve sequential music recommendation, especially for new tracks.

Findings

Model effectively predicts activation of new tracks.

Incorporates audio data into memory-based recommendation.

Demonstrates improved recommendation accuracy.

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.