Towards Intent-Aware Contextual Music Recommendation

Volokhin, Sergey; Agichtein, Eugene

doi:10.1145/3209978.3210154

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…Wang et al [126] present a probabilistic model to integrate contextual information with music content analysis. The contextual information is retrieved using data on user activity, with information coming from a mobile device, as in [12,121]. User activity is inferred in real-time, and music is recommended based on the analysis of music content.…”

Section: Rq-11: What Approaches Are Used To Recommend Music Consideri...mentioning

confidence: 99%

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

Assunção

Piccolo

Zaina

2022

Multimed Tools Appl

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Section: Rq-11: What Approaches Are Used To Recommend Music Consideri...mentioning

confidence: 99%

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

Assunção

Piccolo

Zaina

2022

Multimed Tools Appl

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“…Then we calculated 24-dimensional features for which elements are means and standard deviations of the features shown in Table 2. Since it has been reported that audio features in Table 2 can represent basic information of audio tracks [42], these features can be effective for estimation of the popularity of artists. In addition, we applied doc2vec [43] to the biography of each artist on the Web page of Spotify and obtained 100-dimensional textual features.…”

Section: A Settingsmentioning

confidence: 99%

Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists

et al. 2020

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A novel trial for estimating popularity of artists in music streaming services (MSS) is presented in this paper. The main contribution of this paper is to improve extensibility for using multi-modal features to accurately analyze latent relationships between artists. In the proposed method, a novel framework to construct a network is derived by collaboratively using social metadata and multi-modal features via canonical correlation analysis. Different from conventional methods that do not use multi-modal features, the proposed method can construct a network that can capture social metadata and multi-modal features, i.e., a context-aware network. For effectively analyzing the context-aware network, a novel framework to realize popularity estimation of artists is developed based on network analysis. The proposed method enables effective utilization of the network structure by extracting node features via a node embedding algorithm. By constructing an estimator that can distinguish differences between the node features, the proposed method can archive accurate popularity estimation of artists. Experimental results using multiple real-world datasets that contain artists in various genres in Spotify, one of the largest MSS, are presented. Quantitative and qualitative evaluations show that our method is effective for both classifying and regressing the popularity.

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“…With the rise of music service, music recommendation technology related to music service also has a lot of research achievements [10][11][12][13]. Many music stations now offer not only basic music services but also the ability to push personalized playlists to users, notably Pandora and Last.fm.…”

Section: Introductionmentioning

confidence: 99%

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

Wang

2022

Mobile Information Systems

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Music service is one of the diversified network services offered by people in the Internet era. Various music websites provide many tracks to meet people’s music needs. Hundreds of millions of music of various genres at home and abroad, and there is a severe problem of information asymmetry between users and music. As a branch of the information filtering system, the recommendation system can predict users’ preferences, increase flow, and drive consumption. A personalized music recommendation system can effectively provide people with a list of favorite tracks. Recently, many researchers have paid attention to heterogeneous networks because of their rich semantics information. Research has confirmed that rich relationship information in heterogeneous networks can improve the recommendation effect. Therefore, under the platform of a heterogeneous network, this paper divides the digraph set of track characteristics into several clusters with maximum heterogeneity, which makes the digraph of track characteristics in each cluster isomorphic to the maximum extent. When matching similarity, only searching in the cluster with the highest similarity to the target user can match a sufficient amount of applicable tracks, thus improving the efficiency of music recommendations to users. Experimental results show that the proposed algorithm has a high recall, precision, and F1 and can recommend personalized track lists to users to meet their music needs.

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Towards Intent-Aware Contextual Music Recommendation

Cited by 14 publications

References 6 publications

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

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

Context-Aware Network Analysis of Music Streaming Services for Popularity Estimation of Artists

AI-Based Music Recommendation Algorithm under Heterogeneous Network Platform

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