Recommending More Suitable Music Based on Users’ Real Context

Qing, Yang; Zhan, Le; Han, Lei; Zhang, Jingwei; Bi, Zhongqin

doi:10.1007/978-3-030-12981-1_8

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Depending on how groups are considered, GRSs can involve the aggregation of the outputs of recommender systems for individuals (aggregated predictions) [19,20] or simultaneously consider the preferences of all members, by aggregating them to a group profile, for which recommendations are made (aggregated models) [21]. The first approach is the most commonly applied and the one used in more recent studies.…”

Section: State Of the Artmentioning

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 Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the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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Section: State Of the Artmentioning

confidence: 99%

“…From these contextual factors, they are able to detect mood, artists, and genres. Yang et al [19] proposed a Bayesian approach to find social relationships between users, and this social context is used to detect their favorite categories. In addition, user previous behavior, mood, and so on are included as context.…”

Section: Context-aware Recommender Systemsmentioning

confidence: 99%

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

2021

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

et al. 2021

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The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specific time of day, the user’s physical activity, and geolocation, among many others. This context information is generally obtained by electronic devices used by the user to listen to music such as smartphones and other secondary devices such as wearables and Internet of Things (IoT) devices. The objective of this paper is to present a systematic literature review to analyze recent work to date in the field of context-aware recommender systems and specifically in the domain of music recommendation. This paper aims to analyze and classify the type of contextual information, the electronic devices used to collect it, the main outstanding challenges and the possible opportunities for future research directions.

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Recommending More Suitable Music Based on Users’ Real Context

Cited by 2 publications

References 22 publications

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

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

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

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