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Context-based ranking in folksonomies

Published: 29 June 2009 Publication History

Abstract

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.

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Cited By

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  • (2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
  • (2017)Annotator Expertise and Information Quality in Annotation-based RetrievalProceedings of the 22nd Australasian Document Computing Symposium10.1145/3166072.3166075(1-8)Online publication date: 7-Dec-2017
  • (2012)Context determines contentProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web10.1145/2365934.2365938(17-24)Online publication date: 9-Sep-2012
  • Show More Cited By

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cover image ACM Conferences
HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia
June 2009
410 pages
ISBN:9781605584867
DOI:10.1145/1557914
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 29 June 2009

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Author Tags

  1. adaptation
  2. context
  3. folksonomies
  4. ranking
  5. search
  6. social media

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Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
  • (2017)Annotator Expertise and Information Quality in Annotation-based RetrievalProceedings of the 22nd Australasian Document Computing Symposium10.1145/3166072.3166075(1-8)Online publication date: 7-Dec-2017
  • (2012)Context determines contentProceedings of the 4th ACM RecSys workshop on Recommender systems and the social web10.1145/2365934.2365938(17-24)Online publication date: 9-Sep-2012
  • (2012)Overview and analysis of personal and social tagging context to construct user modelsProceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation10.1145/2162102.2162106(14-21)Online publication date: 14-Feb-2012
  • (2012)COnTagProceedings of the 2012 IEEE 19th International Conference on Web Services10.1109/ICWS.2012.120(568-575)Online publication date: 24-Jun-2012
  • (2012)A knowledge-tracing model of learning from a social tagging systemUser Modeling and User-Adapted Interaction10.1007/s11257-012-9132-123:2-3(139-168)Online publication date: 3-Nov-2012
  • (2010)Leveraging search and content exploration by exploiting context in folksonomy systemsThe New Review of Hypermedia and Multimedia10.1080/13614568.2010.49719316:1-2(33-70)Online publication date: 1-Apr-2010

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