Visualizing networks of music artists with RAMA

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Luís Sarmento (Author) Other; Fabien Gouyon (Author) FEUP; Bruno G. Costa (Author) Other; Eugénio Oliveira (Author) FEUP

Conference proceedings International

Title: WEBIST 2009: PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES

Pages: 232-237

5th International Conference on Web Information Systems and Technologies

Lisbon, PORTUGAL, MAR 23-26, 2009

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Informatics

Other information

Authenticus ID: P-003-SD1

Resumo (PT): 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.

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6