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Building Tag-Aware Groups for Music High-Order Ranking and Topic Discovery

Building Tag-Aware Groups for Music High-Order Ranking and Topic Discovery
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Author(s): Dimitrios Rafailidis (Aristotle University, Greece), Alexandros Nanopoulos (University of Hildesheim, Germany)and Yannis Manolopoulos (Aristotle University, Greece)
Copyright: 2012
Pages: 18
Source title: Methods and Innovations for Multimedia Database Content Management
Source Author(s)/Editor(s): Shu-Ching Chen (University of Missouri-Kansas City, United States)and Mei-Ling Shyu (University of Miami, USA)
DOI: 10.4018/978-1-4666-1791-9.ch013

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Abstract

In popular music information retrieval systems, users have the opportunity to tag musical objects to express their personal preferences, thus providing valuable insights about the formulation of user groups/communities. In this article, the authors focus on the analysis of social tagging data to reveal coherent groups characterized by their users, tags and music objects (e.g., songs and artists), which allows for the expression of discovered groups in a multi-aspect way. For each group, this study reveals the most prominent users, tags, and music objects using a generalization of the popular web-ranking concept in the social data domain. Experimenting with real data, the authors’ results show that each Tag-Aware group corresponds to a specific music topic, and additionally, a three way ranking analysis is performed inside each group. Building Tag-Aware groups is crucial to offer ways to add structure in the unstructured nature of tags.

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