Who Tags What? An Analysis Framework - Archive ouverte HAL Access content directly
Journal Articles Proceedings of the VLDB Endowment (PVLDB) Year : 2012

Who Tags What? An Analysis Framework

Abstract

The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three components, user, item (e.g., photos in Flickr), and tags (i.e., words or phrases). Analyzing how tags are assigned by certain users to certain items has important implications in helping users search for desired information. In this paper, we explore common analysis tasks and propose a dual mining framework for social tagging behavior mining. This framework is centered around two opposing measures, similarity and diversity, being applied to one or more tagging components, and therefore enables a wide range of analysis scenarios such as characterizing similar users tagging diverse items with similar tags, or diverse users tagging similar items with diverse tags, etc. By adopting different concrete measures for similarity and diversity in the framework, we show that a wide range of concrete analysis problems can be defined and they are NP-Complete in general. We design efficient algorithms for solving many of those problems and demonstrate, through comprehensive experiments over real data, that our algorithms significantly out-perform the exact brute-force approach without compromising analysis result quality.
Fichier principal
Vignette du fichier
p1567_mahashwetadas_vldb2012.pdf (736.63 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-00922883 , version 1 (31-12-2013)

Identifiers

  • HAL Id : hal-00922883 , version 1

Cite

Mahashweta Das, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das, Cong Yu. Who Tags What? An Analysis Framework. Proceedings of the VLDB Endowment (PVLDB), 2012, 5 (11), pp.1567-1578. ⟨hal-00922883⟩
140 View
77 Download

Share

Gmail Facebook Twitter LinkedIn More