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Cluster Computing

, Volume 22, Supplement 1, pp 885–894 | Cite as

Resource recommendation via user tagging behavior analysis

  • Haibo LiuEmail author
Article
  • 306 Downloads

Abstract

Tag-based resource recommendation is an interesting and important research topic and has been applied to a wide range of applications. The user’s tagging behavior usually reflects his/her interests in social tagging systems, however most existing work can not fully consider the features of user’s tagging behavior, such as tag frequency, time and ordinal position in tag assignments. In this paper, we employ the combination of cluster analysis and data fitting for extracting the correlations between user interests and the three features, and then present a novel user interest model based on the features to compute the user interest degree. In addition, we propose a collaborative filtering based approach, in which top-k similar users are filtered by resource-interest-based profiles; resource similarities are obtained by tag-frequency-based profiles; the candidate resources are then ranked according to the user interest model, resource profile similarity and user profile similarity. The experiment results conducted on two real-world datasets demonstrate that the proposed approach outperforms the traditional collaborative filtering baselines.

Keywords

Tag-based resource recommendation User tagging behavior analysis User interest model Collaborative filtering Cluster analysis Data fitting 

Notes

Acknowledgements

The work reported in this paper is supported by the Natural Science Foundation of Hebei Province (F2015201140).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.School of Computer Science and TechnologyHebei UniversityBaodingChina

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