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Personalized Recommendation via Relevance Propagation on Social Tagging Graph

  • Huiming Li
  • Hao Li
  • Zimu Zhang
  • Hao WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8505)

Abstract

This paper presents a novel random walk based relevance propagation model for personalized recommendation in social tagging systems. In the model, the tags are used to express the profiles of both users and resources, and then candidates of resources are recommended to the users based on the profile relevance between them. In particular, how the users to find the resources of interest is modeled as a random walk by which the relevance spreads in User-Resource-Tag relation graph. Experimental results on two real datasets collected from social media systems show the merits of the proposed approach.

Keywords

Random Walk Recommender System User Profile Baseline Method Personalized Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the Applied Basic Research Project of Yunnan Province(2013FB009).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingPeople’s Republic of China

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