Context-Aware Music Recommendation with Metadata Awareness and Recurrent Neural Networks

Santana, Igor André Pegoraro; Domingues, Marcos Aurélio

doi:10.31577/cai_2022_3_834

Cited by 3 publications

(2 citation statements)

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“…Context-awareness in music recommender systems entails leveraging contextual cues to deliver personalized recommendations aligned with users' preferences and the circumstances of their music listening experiences. This entails extracting contextual insights from users' listening histories, encompassing attributes like tags, genres, and metadata, and leveraging them to enrich song embeddings [42]. Moreover, sophisticated feature selection algorithms, such as those rooted in genetic algorithms, facilitate compressing high-dimensional contextual data into more concise latent spaces.…”

Section: E Context-awarenessmentioning

confidence: 99%

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

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The study of Music Recommender Systems (MRS) has become a crucial field in the age of digital music consumption, influencing how people discover and interact with music. This comprehensive analysis examines the complex field of MRS research from 2005 to 2023, With the growing importance of music recommendation systems in enhancing user experience, it is crucial to comprehend their development. By utilizing rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies important themes and influential contributors but also emphasizes the complex and diverse nature of MRS. The trend of the field exhibits a significant increase between 2017 and 2021, with periodic oscillations that highlight its dynamic nature. This analysis offers a broad perspective by examining highly cited articles, current researchers, and local sources. Factorial analysis uncovers thematic clusters, highlighting collaborative filtering, user experience, emotion identification, and reinforcement learning. A scientific mapping analysis classifies research themes in different historical periods, with a focus on important areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalization, emotion recognition, personalization, user experience, and collaborative filtering in determining the future directions of research. Although there has been a recent decrease in general interest, the investigation of context-aware models and hybrid techniques offers encouraging opportunities for further inquiry. This research enhances our comprehension of MRS dynamics, leading to future improvements and developments in the field. Ultimately, it improves the music discovery experience for people globally.

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Section: E Context-awarenessmentioning

confidence: 99%

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

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“…However, a new problem has arisen: due to the sparsity of common data, it leads to a serious lack of performance of recommender systems.A number of problem-solving oriented approaches have been proposed by researchers [8] [9] .They have tried to incorporate other dimensions of information into recommender systems, such as acoustic information, text information, image information, etc. This makes a great breakthrough in music recommendation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sequential music recommendation based on self-attention mechanism and content characteristics

Bai

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Real music recommendation is a challenging problem, aiming at selecting items that users may be interested in from massive data. Recently, the recommendation algorithm based on deep learning has left a deep impression. However, most of the recommendation methods ignore the content features of music itself or simply splice it with other features, which to some extent affects the recommended system performance. This paper mainly introduces a music recommendation method C_Rec based on self-attention mechanism and convolution neural network. It uses a new method to fuse the features of music vocal music, which improves the scalability of the recommendation method. Experiments on the corresponding data sets show that our approach is progressiveness compared with some existing models.

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The Effect of Simulated Contextual Factors on Recipe Rating and Nutritional Intake Behaviour

Zhao,

Harvey,

Cameron

et al. 2024

Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval

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Despite the importance of context in Recommender Systems (RSs) more generally, and its clear applicability in the food domain, most existing research focuses on single contextual factors, and only considers simple extrinsic factors such as location and time. No RSs research has systematically explored the impact of multiple dynamic factors, or investigated the effect of emotion in determining people's eating, recipe rating and nutritional intake behaviour. To bridge these gaps, we conducted a comprehensive large-scale (n=397) crowdsourced experimental study to uncover the intricate relationship between various simulated contextual factors and users' subsequent recipe rating and implied nutritional intake behaviour. We further aimed to explore how these contextual factors can be incorporated to improve recommendation performance. Four distinct types of contextual factors were investigated: seasonal, emotional, busyness and physical activity, encompassing a total of seven elements. Our findings show that people's eating preferences and the likelihood of them choosing to eat healthy recipes vary depending on the simulated context they find themselves in. Moreover, we demonstrate how these contextual features can be used to significantly improve recipe rating prediction performance. Our research has implications for the future development of food RSs, and shows that emotion-aware systems could lead to better healthy food recommendations.

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Context-Aware Music Recommendation with Metadata Awareness and Recurrent Neural Networks

Cited by 3 publications

References 0 publications

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Sequential music recommendation based on self-attention mechanism and content characteristics

The Effect of Simulated Contextual Factors on Recipe Rating and Nutritional Intake Behaviour

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