Topology of music recommendation networks

Cano, Pedro; Celma, Òscar; Koppenberger, Markus; Buldú, Javier M.

doi:10.1063/1.2137622

Cited by 70 publications

(53 citation statements)

References 24 publications

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“…For example, listeners are connected with the music groups they collected from music-sharing library (e.g. audioscrobbler.com) [20,21], web-users are connected with the webs they collected in a bookmark * Electronic address: zhutou@ustc.edu † Electronic address: yi-cheng.zhang@unifr.ch site (e.g. delicious) [22], customers are connected with the books they bought (e.g.…”

Section: Introductionmentioning

confidence: 99%

Bipartite network projection and personal recommendation

et al. 2007

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The one-mode projecting is extensively used to compress the bipartite networks. Since the onemode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the networkbased resource-allocation dynamics, we raise a weighting method, which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method in compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do personal recommendation?

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Section: Introductionmentioning

confidence: 99%

Bipartite network projection and personal recommendation

et al. 2007

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“…For example, networks where products are music groups is studied in Ref. [6]. Recommendations can be generated either through collaborative filtering or using content-based methods or by combination of these two methods.…”

Section: Introductionmentioning

confidence: 99%

Movies Recommendation Networks as Bipartite Graphs

Grujić

2008

Computational Science – ICCS 2008

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Abstract. In this paper we investigate the users' recommendation networks based on the large data set from the Internet Movie Database. We study networks based on two types of inputs: first (monopartite) generated directly from the recommendation lists on the website, and second (bipartite) generated through the users' habits. Using a threshold number of votes per movie to filter the data, we actually introduce a control parameter, and then by tuning this parameter we study its effect on the network structure. From the detailed analysis of both networks we find that certain robust topological features occur independently from the value of the control parameter. We also present a comparison of the network clustering and shortest paths on the graphs with a randomized network model based on the same data.

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“…Given specific types of artist-related data one wants to visualize, a specific mapping must be done onto these data "containers". Nodes (or vertices) and edges are also central to the science of complex networks (Barabási, 2002), and a number of works in this field have recently brought some light onto the manifold intertwinements of musical artists networks (Cano et al, 2006), (Teitelbaum et al, 2008). There are several applications for visualizing artist networks as twodimensional connected graphs.…”

Section: Related Workmentioning

confidence: 99%

Visualizing Networks of Music Artists With Rama

2009

Proceedings of the Fifth International Conference on Web Information Systems and Technologies

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Abstract:In this paper we present RAMA (Relational Artist MAps), a simple yet efficient interface to navigate through networks of music artists. RAMA is built upon a dataset of artist similarity and user-defined tags regarding 583.000 artists gathered from Last.fm. This third-party, publicly available, data about artists similarity and artists tags is used to produce a visualization of artists relations. RAMA provides two simultaneous layers of information: (i) a graph built from artist similarity data, and (ii) overlaid labels containing user-defined tags. Differing from existing artist network visualization tools, the proposed prototype emphasizes commonalities as well as main differences between artist categorizations derived from user-defined tags, hence providing enhanced browsing experiences to users.

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Topology of music recommendation networks

Cited by 70 publications

References 24 publications

Bipartite network projection and personal recommendation

Bipartite network projection and personal recommendation

Movies Recommendation Networks as Bipartite Graphs

Visualizing Networks of Music Artists With Rama

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