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Exploring External Knowledge Base for Personalized Search in Collaborative Tagging Systems

  • Dong ZhouEmail author
  • Xuan Wu
  • Wenyu Zhao
  • Séamus Lawless
  • Jianxun Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)

Abstract

Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services, especially those utilizing collaborative tagging data. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user’s past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user’s past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model leverages recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted by utilizing real-world collaborative tagging data show that the methods proposed in the current paper outperform several non-personalized methods as well as existing personalized search methods by utilizing user models solely constructed from usage histories.

Keywords

Personalized search Collaborative tagging systems Latent semantic models Word embeddings Query expansion 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (61300129, 61572187 and 61272063), Scientific Research Fund of Hunan Provincial Education Department of China (16K030), Hunan Provincial Innovation Foundation For Postgraduate (CX2016B575). This research was also supported by the ADAPT Centre for Digital Content Technology, which is funded under the Science Foundation Ireland Research Centres Programme (13/RC/2106) and is co-funded under the European Regional Development Fund.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  • Dong Zhou
    • 1
    Email author
  • Xuan Wu
    • 1
  • Wenyu Zhao
    • 1
  • Séamus Lawless
    • 2
  • Jianxun Liu
    • 1
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.ADAPT Centre, Knowledge and Date Engineering Group, School of Computer Science and StatisticsTrinity College DublinDublin 2Ireland

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