Emotion-Aware Music Recommendation

People often listen to songs that match their mood. Thus, an AI music recommendation system that is aware of the user’s emotions is likely to provide a superior user experience to one that is unaware. In this paper, we present an emotion-aware music recommendation system. Multiple models are discussed and evaluated for affect identification from a live image of the user. We propose two models: DRViT, which applies dynamic routing to vision transformers, and InvNet50, which uses involution. All considered models are trained and evaluated on the AffectNet dataset. Each model outputs the user’s estimated valence and arousal under the circumplex model of affect. These values are compared to the valence and arousal values for songs in a Spotify dataset, and the top-five closest-matching songs are presented to the user. Experimental results of the models and user testing are presented

GEMRec: A graph-based emotion-aware music recommendation approach

© Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score

Emotion-Aware Music Recommendation System: Enhancing User Experience Through Real-Time Emotional Context

This study addresses the deficiency in conventional music recommendation systems by focusing on the vital role of emotions in shaping users music choices. These systems often disregard the emotional context, relying predominantly on past listening behavior and failing to consider the dynamic and evolving nature of users emotional preferences. This gap leads to several limitations. Users may receive recommendations that do not match their current mood, which diminishes the quality of their music experience. Furthermore, without accounting for emotions, the systems might overlook undiscovered or lesser-known songs that have a profound emotional impact on users. To combat these limitations, this research introduces an AI model that incorporates emotional context into the song recommendation process. By accurately detecting users real-time emotions, the model can generate personalized song recommendations that align with the users emotional state. This approach aims to enhance the user experience by offering music that resonates with their current mood, elicits the desired emotions, and creates a more immersive and meaningful listening experience. By considering emotional context in the song recommendation process, the proposed model offers an opportunity for a more personalized and emotionally resonant musical journey.Comment: 6 page

Sequence-based context-aware music recommendation

© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect users’ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users’ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users’ music listening sequences using neural network models. Based on the learned representations, it then infers and models users’ general and contextual preferences for music from users’ historical listening records. Finally, music pieces in accordance with user’s preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data

ネガティブサンプリングを用いたコンテキスト情報に基づく楽曲推薦システムに関する研究

首都大学東

Detecting Music-Induced Emotion Based on Acoustic Analysis and Physiological Sensing: A Multimodal Approach

The subjectivity of listeners’ emotional responses to music is at the crux of optimizing emotion-aware music recommendation. To address this challenge, we constructed a new multimodal dataset (“HKU956”) with aligned peripheral physiological signals (i.e., heart rate, skin conductance, blood volume pulse, skin temperature) and self-reported emotion collected from 30 participants, as well as original audio of 956 music pieces listened to by the participants. A comprehensive set of features was extracted from physiological signals using methods in physiological computing. This study then compared performances of three feature sets (i.e., acoustic, physiological, and combined) on the task of classifying music-induced emotion. Moreover, the classifiers were also trained on subgroups of users with different Big-Five personality traits for further customized modeling. The results reveal that (1) physiological features contribute to improving performance on valence classification with statistical significance; (2) classification models built for users in different personality groups could sometimes further improve arousal prediction; and (3) the multimodal classifier outperformed single-modality ones on valence classification for most user groups. This study contributes to designing music retrieval systems which incorporate user physiological data and model listeners’ emotional responses to music in a customized manner