A Multimodal Music Recommendation System with Listeners' Personality and Physiological Signals

Abstract

This preliminary study explored multiple information sources for music recommendation system (MRS), including users' personality traits measured by the Ten-Item Personality Inventory (TIPI) and physiological signals recorded by a wearable wristband. A dataset of 23 participants and 628 song listening records were obtained from a user experiment, with matched personality, physiological signals as well as music acoustic features. Based on the dataset, a machine learning experiment with four regression algorithms was conducted to compare recommendation performances across different combinations of feature sets. Results show that personality features contributed significantly to the improvement of recommender accuracy, while physiological features contributed less. Analysis of top features in the best performing model revealed the importance of some physiological features. Future studies are called for to further investigate multimodal MRS through exploiting user properties and context data.