A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Abstract

Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

Harmonizing minds and machines: survey on transformative power of machine learning in music

“…DL methods have also been applied to music recommendation, such as using multi-layer perceptrons (MLP) and RNN. These methods effectively capture the complex patterns in the user-item interactions and have achieved state-of-the-art performance on music recommendation benchmarks (Lin et al, 2019;Schedl, 2019;Dang et al, 2021;Wang et al, 2021;Singh et al, 2022). In addition, Schindler et al (2012) proposed to use metadata information from songs to improve the accuracy of music recommendations and Kim et al (2020) used DL for music recommendation by using user listening history and audio features of songs as input.…”

The impact of COVID-19 on online music listening behaviors in light of listeners’ social interactions

“…For example, during the COVID-19 pandemic, researchers have formulated a better way to recommend music based on users' emotions by reading users' facial expressions or obtaining direct input from them [109]. Studies have also shown that emotion-aware algorithms can utilize the information provided by COVID-19 cases and outperform baseline algorithms [112]. Typically, contextaware music recommendation systems are concerned with studying users' musical listening behaviors in the context of the real-world and utilizing that information as input for the system.…”

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

“…At the same time, with the advent of multi-aspect temporal-textual embedding in author linking [14], many recommendation systems based on text emotion have also emerged. In particular, it should be noted that some deep learning models, such as context-aware models [15] and EmoDNN [16], make deep learning-based emotion understanding more accurate, which further improves the personalization of the recommendation system. In 2023, Baqach, A et al proposed a new method called CLAS (CNN-LSTM-Attention-SVM) based on CNNs, LSTMs, Attention mechanisms, and support vector machines [17].…”