Context-based Ranking in Folksonomies

被引：0
|
作者：
Abel, Fabian [1 ]
Baldoni, Matteo
Baroglio, Cristina
Henze, Nicola [1 ]
Krause, Daniel [1 ]
Patti, Viviana
机构：
[1] Leibniz Univ Hannover, IVS Semant Web Grp, D-30167 Hannover, Germany
来源：
20TH ACM CONFERENCE ON HYPERTEXT AND HYPERMEDIA (HYPERTEXT 2009) | 2009年
关键词：
Social Media; Search; Ranking; Folksonomies; Context; Adaptation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the advent of Web 2.0 tagging became a popular feature. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. Clicking on a tag enables the users to explore related content. In this paper we investigate how such tag-based queries, initialized by the clicking activity, can be enhanced with automatically produced contextual information so that the search result better fits to the actual aims of the user. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking users, tags, and resources in a contextualized way.
引用
收藏
页码：209 / 218
页数：10
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