Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review

Murciego, Álvaro Lozano; Jiménez-Bravo, Diego M.; Román, Adrián Valera; Paz, Juan F. De; García, María N. Moreno

doi:10.3390/electronics10131555

Cited by 23 publications

(6 citation statements)

References 140 publications

(81 reference statements)

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“…Collaborative filtering music recommender systems are mainly divided into a user-based and item-based approach to give predictions. Contextual factors impact users' choices of music [11] which inspires the context-aware methods to absorb various contextual information, such as locations [12], weather [11], time [13] and user-related social context [14]. Even though the above methods have undergone many improvements, some insurmountable defects remain.…”

Section: A Music Recommendationmentioning

confidence: 99%

Graph-Based Attentive Sequential Model With Metadata for Music Recommendation

et al. 2022

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Massive music data and diverse listening behaviors have caused great difficulties for existing methods in user-personalized recommendation scenarios. Most previous music recommendation models extract features from temporal relationships among sequential listening records and ignore the utilization of additional information, such as music's singer and album. Especially, a piece of music is commonly created by a specific musician and belongs to a particular album. Singer and album information, regarded as music metadata, can be utilized as important auxiliary information among different music pieces and may considerably influence the user's choices of music. In this paper, we focus on the music sequential recommendation task with the consideration of the additional information and propose a novel Graphbased Attentive Sequential model with Metadata (GASM), which incorporates metadata to enrich music representations and effectively mine the user's listening behavior patterns. Specifically, we first use a directed listening graph to model the relations between various kinds of nodes (user, music, singer, album) and then adopt the graph neural networks to learn their latent representation vectors. After that, we decompose the user's preference for music into long-term, short-term and dynamic components with personalized attention networks. Finally, GASM integrates three types of preferences to predict the next (new) music in accordance with the user's music taste. Extensive experiments have been conducted on three real-world datasets, and the results show that the proposed method GASM achieves better performance than baselines.

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Section: A Music Recommendationmentioning

confidence: 99%

Graph-Based Attentive Sequential Model With Metadata for Music Recommendation

et al. 2022

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“…However, this value may require normalization if the number of items recommended in the lists varies. This is done in comparison to the ideal DCG (iDCG) given by, Equation (4), obtaining the normalized DCG (nDCG) using Equation (5).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…It has been found that context is widely addressed in the literature about GRS, especially in the area of music, since it has great relevance in determining the group's situation and establishing recommendations [5].…”

Section: Introductionmentioning

confidence: 99%

“…We have exclusively used the precision and rank evaluation metrics, which, as indicated in the works [5,15], are gaining popularity over the rest, since the error metrics do not provide very useful information when analysing ordered lists. The reason lies in the fact that, in a list of recommendations, and especially in an aggregate recommendation for several users in a group, it is not as important to get accurate predictions as it is to choose those elements that are relevant for the majority and be able to present them to the users as soon as possible.…”

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confidence: 99%

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Context-Aware Music Recommender Systems for Groups: A Comparative Study

2021

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Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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“…This general classification can be adapted to the application fields. For instance, in a musical application, the context classification includes features related to the device with which users interact and focuses on the achievement and the activity of users [48].…”

Section: Introductionmentioning

confidence: 99%

Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias

et al. 2022

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In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising.

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Context-Aware Recommender Systems in the Music Domain: A Systematic Literature Review

Cited by 23 publications

References 140 publications

Graph-Based Attentive Sequential Model With Metadata for Music Recommendation

Graph-Based Attentive Sequential Model With Metadata for Music Recommendation

Context-Aware Music Recommender Systems for Groups: A Comparative Study

Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias

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