From hits to niches?

Òscar Celma; Pedro Cano

doi:10.1145/1722149.1722154

August 24, 2008

From hits to niches?

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Key Points

Examine the impact of the popularity effect on music recommendation systems and assess how top-streamed artists shape artist similarity networks.
Evaluated artist popularity using aggregated total playcounts.
Applied the Long Tail model to rank artists and differentiate mainstream hits from niche music.
Implemented complex network analysis metrics to determine the structural influence of popular artists within similarity graphs.
Demonstrated that high playcount artists exert disproportionate structural centrality within artist similarity networks.
Observed that popularity bias directs recommendation pathways toward mainstream music, limiting network exposure for long-tail artists.

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Implication

Computational analysis reveals popularity bias across music recommendation graphs, indicating top artists dominate similarity networks over niche creators.

Abstract

This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists.

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Celma et al. (2008) studied this question.

synapsesocial.com/papers/6a16b8e5b082e78ad77b85c4 https://doi.org/10.1145/1722149.1722154

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From hits to niches?

Key Points

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Implication

Abstract

Discussion

Cite This Study

From hits to niches?

Key Points

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Implication

Abstract

Discussion

Cite This Study

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