Considering emotions and contextual factors in music recommendation: a systematic literature review

Assunção, Willian Garcias de; Piccolo, Lara S. G.; Zaina, Luciana A. M.

doi:10.1007/s11042-022-12110-z

Cited by 19 publications

(4 citation statements)

References 112 publications

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“…A customeroriented personalized recommendation algorithm with high real-time performance and accuracy is proposed. Figure 3.1 shows the architecture of its standard architecture model [14]. The first layer is the mobile intelligent terminal interaction layer.…”

Section: Clustering Algorithmmentioning

confidence: 99%

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

Wu,

Sun

2024

SCPE

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As an effective solution to the problem of information overload, personalized recommendations have received widespread attention in the music field. A context-aware music recommendation algorithm combining classification and collaborative filtering is proposed based on user context information. Firstly, the similarity analysis of the user situation is carried out. A preliminary list of recommended songs is obtained by collaborative filtering. The machine learning method is used to classify music in different scenes to get the preferences of music types in different situations. Finally, the recommendation list obtained by collaborative filtering is combined with the music type preference obtained by the classification model and personalized music recommendations for users in different situations. This algorithm not only effectively reduces the complexity of the recommendation process. Experiments show that the proposed algorithm can effectively improve the accuracy of users' music recommendations.

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Section: Clustering Algorithmmentioning

confidence: 99%

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

Wu,

Sun

2024

SCPE

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“…Statistical summaries and moments of these signals over time windows are used to predict the user’s valence and arousal. For a more in-depth survey on emotion-aware music recommendation, we refer the reader to Assuncao et al [ 37 ].…”

Section: Related Workmentioning

confidence: 99%

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

Melchiorre,

Penz,

Ganhör

et al. 2023

Int J Multimed Info Retr

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Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of interaction is unidimensional, i.e., they provide lists of consecutive tracks, which are commonly inspected in sequential order by the user. The user experience with such systems is heavily affected by cognition biases (e.g., position bias, human tendency to pay more attention to first positions of ordered lists) as well as algorithmic biases (e.g., popularity bias, the tendency of recommender systems to overrepresent popular items). This may cause dissatisfaction among the users by disabling them to find novel music to enjoy. In light of such systems and biases, we propose an intelligent audiovisual music exploration system named EmoMTB . It allows the user to browse the entirety of a given collection in a free nonlinear fashion. The navigation is assisted by a set of personalized emotion-aware recommendations, which serve as starting points for the exploration experience. EmoMTB adopts the metaphor of a city, in which each track (visualized as a colored cube) represents one floor of a building. Highly similar tracks are located in the same building; moderately similar ones form neighborhoods that mostly correspond to genres. Tracks situated between distinct neighborhoods create a gradual transition between genres. Users can navigate this music city using their smartphones as control devices. They can explore districts of well-known music or decide to leave their comfort zone. In addition, EmoMTB integrates an emotion-aware music recommendation system that re-ranks the list of suggested starting points for exploration according to the user’s self-identified emotion or the collective emotion expressed in EmoMTB ’s Twitter channel. Evaluation of EmoMTB has been carried out in a threefold way: by quantifying the homogeneity of the clustering underlying the construction of the city, by measuring the accuracy of the emotion predictor, and by carrying out a web-based survey composed of open questions to obtain qualitative feedback from users.

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“…However, current music recommender systems for driving typically fall into one of three categories: content-based, collaborative filtering, or hybrid approaches [8]. Nevertheless, recent advancements have shown promising results in music recommenders that consider user emotions and contextual factors [4]. Researchers recognize the importance of incorporating user emotions and context to improve the personalization and effectiveness of music recommendations.…”

Section: Problem Definitionmentioning

confidence: 99%

ConCollA - A Smart Emotion-based Music Recommendation System for Drivers

Patel,

Padaria,

Mehta

et al. 2023

SCPE

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Music recommender system is an area of information retrieval system that suggests customized music recommendations to users based on their previous preferences and experiences with music. While existing systems often overlook the emotional state of the driver, we propose a hybrid music recommendation system - ConCollA to provide a personalized experience based on user emotions. By incorporating facial expression recognition, ConCollA accurately identifies the driver’s emotions using convolution neural network(CNN) model and suggests music tailored to their emotional state. ConCollA combines collaborative filtering, a novel content-based recommendation system named Mood Adjusted Average Similarity (MAAS), and apriori algorithm to generate personalized music recommendations. The performance of ConCollA is assessed using various evaluation parameters. The results show that proposed emotion-aware model outperforms a collaborative-based recommender system.

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Considering emotions and contextual factors in music recommendation: a systematic literature review

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Cited by 19 publications

References 112 publications

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

ConCollA - A Smart Emotion-based Music Recommendation System for Drivers

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