Social bookmark weighting for search and recommendation

Social bookmark weighting for search and recommendation Social bookmarking enables knowledge sharing and efficient discovery on the web, where users can collaborate together by tagging documents of interests. A lot of attention was given lately for utilizing social bookmarking data to enhance traditional IR tasks. Yet, much less attention was given to the problem of estimating the effectiveness of an individual bookmark for the specific tasks. In this work, we propose a novel framework for social bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually for several IR tasks. We show that by weighting bookmarks according to their estimated quality, we can significantly improve social search effectiveness. We further demonstrate that using the same framework, we can derive solutions to several recommendation tasks such as tag recommendation, user recommendation, and document recommendation. Empirical evaluation on real data gathered from two large bookmarking systems demonstrates the effectiveness of the new social bookmark weighting framework. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Social bookmark weighting for search and recommendation

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Publisher
Springer-Verlag
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-010-0211-9
Publisher site
See Article on Publisher Site

Abstract

Social bookmarking enables knowledge sharing and efficient discovery on the web, where users can collaborate together by tagging documents of interests. A lot of attention was given lately for utilizing social bookmarking data to enhance traditional IR tasks. Yet, much less attention was given to the problem of estimating the effectiveness of an individual bookmark for the specific tasks. In this work, we propose a novel framework for social bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually for several IR tasks. We show that by weighting bookmarks according to their estimated quality, we can significantly improve social search effectiveness. We further demonstrate that using the same framework, we can derive solutions to several recommendation tasks such as tag recommendation, user recommendation, and document recommendation. Empirical evaluation on real data gathered from two large bookmarking systems demonstrates the effectiveness of the new social bookmark weighting framework.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2010

References

  • Fast sparse matrix multiplication
    Yuster, R.; Zwick, U.

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