Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Exploiting Query Proximity and Graph Profiling Method for Tag-based Personalized Search in Folksonomy

질의어의 근접성 정보 및 그래프 프로파일링 기법을 이용한 태그 기반 개인화 검색

  • 한기준 (한국과학기술원 지식서비스공학과) ;
  • 장진철 (한국과학기술원 지식서비스공학과) ;
  • 이문용 (한국과학기술원 지식서비스공학과)
  • Received : 2014.07.15
  • Accepted : 2014.09.16
  • Published : 2014.12.15
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Abstract

Folksonomy data, which is derived from social tagging systems, is a useful source for understanding a user's intention and interest. Using the folksonomy data, it is possible to create an accurate user profile which can be utilized to build a personalized search system. However there are limitations in some of the traditional methods such as Vector Space Model(VSM) for user profiling and similarity computation. This paper suggests a novel method with graph-based user and document profile which uses the proximity information of query terms to improve personalized search. We demonstrate the performance of the suggested method by comparing its performance with several state-of-the-art VSM based personalization models in two different folksonomy datasets. The results show that the proposed model constantly outperforms the other state-of-the-art personalization models. Furthermore, the parameter sensitivity results show that the proposed model is parameter-free in that it is not affected by the idiosyncratic nature of datasets.

최근 폭소노미라고 불리는 데이터들이 사용자의 의도 파악 및 흥미를 분석하는 데에 매우 유용하게 쓰이고 있다. 본 논문은 폭소노미 데이터를 이용한 개인화 검색에서, 기존의 벡터 기반 프로파일링 및 유사도 계산 모델의 한계점을 지적하고, 이러한 한계를 극복하기 위한 방법으로 그래프 기반의 프로파일링 및 유사도 계산법을 제안한다. 최종적으로 그래프 기반의 개인화 검색 모델에 추가적으로 질의어간의 근접성까지 고려한 보다 발전된 개인화 검색 기법을 제안하였다. 본 연구에서는 복수의 데이터셋을 사용한 객관적인 성능 평가 실험을 통해 제안한 모델이 기존의 벡터 스페이스 모델에 기반한 프로파일링 기법 및 프로파일 간의 유사도 계산 기법보다 더 뛰어난 개인화 검색 결과를 제공함을 확인하였다. 또한 추가적인 파라미터 실험을 통하여, 제안하는 모델은 어떠한 형태의 데이터셋에도 쉽게 적용가능함을 보였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. J. Pitcow, H. Schutze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel, "Personalized Search," Communications of the ACM, Vol. 45, No. 9, pp. 50-55, 2002.
  2. C. M. A. Yeung, N. Gibbins, and N. Shadbolt, "A Study of User Profile Generation from Folksonomies," Proc. of the Social Web and Knowledge Management Workshop, WWW Conf., 2008.
  3. S. Bao, G. Xue, X. Wu, Y, Yu, B. Fei, and Z. Su, "Optimizing Web Search Using Social Annotations," Proc. of WWW, pp. 501-510, 2007.
  4. S. Xu, S. Bao, B. Fei, Z. Su, and Y. Yu, "Exploring Folksonomy for Personalized Search," Proc. of SIGIR, pp. 155-162, 2008.
  5. M. G. Noll, and C. Meinel, "Web Search Personalization Via Social Bookmarking and Tagging," Proc. of ISWC, pp. 367-380, 2007.
  6. D. Vallet, I. Cantador, and J. M. Jose, "Personalizing Web Search with Folksonomy-based User and Document Profiles," Proc. of ECIR, pp. 420-431, 2010.
  7. T. Tao, and C. Zhai, "An Exploration of Proximity Measures in Information Retrieval," Proc. of SIGIR, pp. 295-302, 2007.
  8. P. A. Chirita, C. S. Firan, and W. Nejdl, "Personalized Query Expansion for the Web," Proc. of SIGIR, pp. 7-14, 2007.
  9. P. A. Chirita, W. Nejdl, R. Paiu, and C. Kohlschutter, "Using ODP Metadata to Personalized Search," Proc. of SIGIR, pp. 178-185, 2005.
  10. B. Tan, X. Shen, and C. Zhai, "Mining Long-term Search History to Improve Search Accuracy," Proc. of KDD, pp. 718-723, 2006.
  11. K. Sugiyama, K. Hatano, M. Yoshikawa, and S. Uemura, "User-Oriented Adaptive Web Information Retrieval Based on Implicit Observations," Proc. of ApWeb, pp. 636-643, 2004.
  12. J. T. Sun, H. J. Zeng, H. Liu, Y. Lu, and Z. Chen, "CubeSVD: A Novel Approach to Personalized Web Search," Proc. of WWW, pp. 382-390, 2005.
  13. Z. Dou, R. Song, J. R. Wen, "A Large-scale Evaluation and Analysis of Personalized Search Strategies," Proc. of WWW, pp. 581-590, 2007.
  14. Y. Cai, Q. Li, "Personalized Search by Tagbased User Profile and Resource Profile in Collaborative Tagging Systems," Proc. of CIKM, pp. 969-978, 2010.
  15. M. Daoud, L. Tamine, M. Bouhanem, "A Personalized Graph-based Document Ranking Model Using a Semantic User Profile," Proc. of UMAP, pp. 171-182, 2010.
  16. J. Zhao, Y. Yun, "A Proximity Language Model for Information Retrieval," Proc. of SIGIR, pp. 291-298, 2009.
  17. G. Levi, "A Note on the Derivation of Maximal Common Sub-graphs of Two Directed or Undirected Graphs," Calcolo, Vol. 9, No. 4, pp. 341-354, 1973. https://doi.org/10.1007/BF02575586
  18. H. Bunke, X. Jiang, and A. Kandel, "On the Minimum Common Supergraph of Two Graph," Computing, Vol. 65, No. 1, pp. 13-25, 2000.
  19. A. Shepitsen, J. Gemmell, B. Mobasher, R. Burke, "Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering," Proc. of RecSys, pp. 259-266, 2008.
  20. S. Lee, M. Ko, K. Han, J. Lee, "On Finding Fine-Granularity User Communities by Profile Decomposition," Proc. of ASONAM, pp. 631-639, 2012.