한국전자거래학회지 (The Journal of Society for e-Business Studies)

  • 제15권3호
  • /
  • Pages.131-145
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  • 2010
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  • 2288-3908(pISSN)
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  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
권호 표지

개념 망을 통한 전자 카탈로그의 시맨틱 검색 및 추천

Semantic Search and Recommendation of e-Catalog Documents through Concept Network

  • 이재원 (서울대학교 전기컴퓨터 공학부) ;
  • 박성찬 (서울대학교 전기컴퓨터 공학부) ;
  • 이상근 (서울대학교 전기컴퓨터 공학부) ;
  • 박재휘 (서울대학교 전기컴퓨터 공학부) ;
  • 김한준 (서울시립대학교 전자전기컴퓨터 공학부) ;
  • 이상구 (서울대학교 전기컴퓨터 공학부)
  • 투고 : 2010.05.14
  • 심사 : 2010.06.24
  • 발행 : 2010.08.31
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초록

현재까지, 사용자의 요구에 맞는 카탈로그 문서를 제공하기 위해 널리 사용되고 있는 패러다임은 키워드 검색 혹은 협업적 필터링 기반 추천이다. 일반적으로 사용자의 질의어는 짧기 때문에, 사용자의 요구(질의어, 선호도)에 적합한 카탈로그 문서를 제공하는 것은 쉽지 않다. 이를 극복하기 위해 다양한 기법들이 제안되었으나, 이전 연구들은 색인어 매칭을 기반으로 하고 있다. 기존 베이지안 신념 망을 이용한 방법은 사용자의 요구 및 카탈로그 문서들을 연관성이 높은 개념들로 표현하였다. 하지만 개념들이 카탈로그 문서에서 추출된 색인어로 구성되어 있기 때문에 개념간의 관계 정보를 잘 표현하지 못하였다. 이에 본 연구는 베이지안 신념 망을 확장하여, 사용자의 요구 및 카탈로그 문서들을 웹 디렉토리에서 추출한 개념(혹은 카테고리) 망으로 표현한다. 개념 망을 이용함으로써, 사용자의 요구와 카탈로그 문서간의 개념 매칭도를 계산하는 것이 가능하다. 즉, 사용자의 질의어와 카탈로그 문서의 색인어가 일치하지 않을지라도, 개념적으로 관련성이 높은 문서를 검색하는 것이 가능하다. 또한 사용자간의 개념적 유사도를 계산함으로써, 시맨틱 기반의 협업적 필터링 추천이 가능하다.

Until now, popular paradigms to provide e-catalog documents that are adapted to users' needs are keyword search or collaborative filtering based recommendation. Since users' queries are too short to represent what users want, it is hard to provide the users with e-catalog documents that are adapted to their needs(i.e., queries and preferences). Although various techniques have beenproposed to overcome this problem, they are based on index term matching. A conventional Bayesian belief network-based approach represents the users' needs and e-catalog documents with their corresponding concepts. However, since the concepts are the index terms that are extracted from the e-catalog documents, it is hard to represent relationships between concepts. In our work, we extend the conventional Bayesian belief network based approach to represent users' needs and e-catalog documents with a concept network which is derived from the Web directory. By exploiting the concept network, it is possible to search conceptually relevant e-catalog documents although they do not contain the index terms of queries. Furthermore, by computing the conceptual similarity between users, we can exploit a semantic collaborative filtering technique for recommending e-catalog documents.

키워드

참고문헌

  1. Baeza-Yates, R. and Ribeiro-Neto, B., Modern Information Retrieval, Addison Wesley, 1999.
  2. Ribeiro, B. A. and Muntz, R., "A Belief Network Model for IR," In Proceeding of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR '96), 1996, pp. 253-260.
  3. Lenat, D. and Guha, R., Building Large Knowledge Based Systems, Addison Wesley, 1990.
  4. Chirita, P. A., Nejdl, W., Paiu, R., and Kohlschutter, C., "Using ODP Metadata to Personalized Search," In Proceeding of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR '05), 2005, pp. 178-185.
  5. Vogel, D., Bickel, S., Haider, P., Schimpfky, R., Siemen, P., Bridges, S., and Scheffer, T., "Classifying Search Engine Queries using the Web as Background Knowledge," SIGKDD Explorations Newsletter, Vol. 7, No. 2, 2005, pp. 117-122. https://doi.org/10.1145/1117454.1117469
  6. Shen, D., Sun, J., Yang, Q., and Chen, Z., "Building Bridges for Web Query Classification," In Proceeding of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR '06), 2006, pp. 131-138.
  7. Liu, F., Yu, C., and Meng, W., "Personalized Web Search for Improving Retrieval Effectiveness," IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 1, 2004, pp. 28-40. https://doi.org/10.1109/TKDE.2004.1264820
  8. Cosley, D., Lawrence, S., and Pennock, D., "REFEREE : An Open Framework for Practical Testing of Recommender Systems Using Researchindex," In Proceeding of 28th International Conference on Very Large Databases(VLDB 2002), 2002, pp. 35-46.
  9. Pazzani, M. and Bilsus, D., "Learning and Revising User Profile : the Identification of Interesting Web Sites," Machine Learning, Vol. 27, No. 3, 1997, pp. 313-331. https://doi.org/10.1023/A:1007369909943
  10. Li, Y., Lu, L., and Xuefeng, L., "A Hybrid Collaborative Filtering Method for Multiple Interests and Multiple Content Recommendation in E-Commerce," Expert Systems with Applications, Vol. 28, No. 1, 2005, pp. 67-77. https://doi.org/10.1016/j.eswa.2004.08.013
  11. Wang, J., Vries, A. P., and Reinders, M. J., "Unifying User-based and Itembased Collaborative Filtering Approaches by Similarity Fusion," In Proceeding of 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2006), 2006, pp. 501-508.
  12. Deshpande, M. and Karypis, G., "Item-based Top-n Recommendation Algorithm," ACM Transaction on Information Systems, Vol. 22, No. 1, 2004, pp. 143-177. https://doi.org/10.1145/963770.963776
  13. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., "Item-based Collaborative Filtering Recommendation Algorithms," In Proceeding of 10th International Conference on World Wide Web(WWW 2001), 2001, pp. 285-295.
  14. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J., "An Algorithmic Framework for Performing Collaborative Filtering," In Proceeding of 22th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 1999), 1999, pp. 230-237.
  15. Jin, R., Chai, J. Y., and Si, L., "An Automatic Weighting Scheme for Collaborative Filtering," In Proceeding of 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR 2004), 2004, pp. 337-344.
  16. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., and Riedel, J., "Group-Lens : An Open Architecture for Collaborative Filtering of Netnews," In Proceeding of the ACM Conference on Computer Supported Cooperative Work (CSCW 1994), 1994, pp. 175-186.
  17. Mild, A. and Natter, M., "A Critical View on Recommendation Systems," Working Paper Series, 2001, pp. 1-16.
  18. Mobasher, B., Jin, X., and Zhou, Y., "Semantically Enhanced Collaborative Filtering on the Web," Lecture Notes in Computer Science, Vol. 3209, 2004, pp. 57-76.
  19. Porter, M. F., "An Algorithm for Suffix Stripping," Readings in Information Retrieval, 1997, pp. 313-316.
  20. Wong, S. and Yao, Y., "On Modeling Information Retrieval with Probabilistic Inference," ACM Transactions on Information Systems, Vol. 13, No. 1, 1995, pp. 38-68. https://doi.org/10.1145/195705.195713
  21. Pitkow, J., Schutze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., and Breuel, T., "Personalized Search," Communication of the ACM, Vol. 45, No. 9, 2002, pp. 50-55.
  22. Pretschner, A. and Gauch, S., "Ontology-based Personalized Search," In Proceeding of the 11th IEEE International Conference on Tools with Artificial Intelligence, 1999, pp. 391-398.
  23. Speretta, M. and Gauch, S., "Personalized Search based on User Search Histories," In Proceeding of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, 2005, pp. 622-628.
  24. Sieg, A., Mobasher, B., and Burke, R., "Web Search Personalization with Ontological User Profiles," In Proceeding of the 16th ACM Conference on Information and Knowledge Management (CIKM '07), 2007, pp. 525-534.
  25. Xu, S., Bao, S., Fei, B., Su, Z., and Yu, Y., "Exploring Folksonomy for Personalized Search," In Proceeding of the 31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR '08), 2008, pp. 155-162.