APPLYING CROSS-TOPIC RELATIONSHIPS TO INCREMENTAL RELEVANCE FEEDBACK

Terry C H Lai, Stephen C F Chan, Korris F L Chung

2004

Abstract

General purpose search engines like Google and Yahoo define search topics for the purpose of document organization, yet their hierarchical structures cover only a portion of topic relationships. Search effectiveness can be improved by using search topic networks, in which topics are linked through semantic relations. In our search model, is-child and is-neighbor relations are defined as relations among search topics, which in turn can serve as search techniques; the is-child relation allows searching from general concepts, while the is-neighbor relation provides fresh information that can help users to identify search areas. This search model uses the Bayesian Networks and the incremental relevance feedback. Our experiments show that search models using the Bayesian Networks and the incremental relevance feedback improve search effectiveness.

References

  1. Aalbersberg, I. J., 1992. Incremental Relevance Feedback,15th Ann Int'I SIGIR 7892, Denmark, pp. 11- 22.
  2. Allan, J., 1996, Incremental Relevance Feedback for Information Filtering, SIGIR'96, Zurich, pp. 270-278.
  3. Chau, M., Zeng, D., Chen, H., 2001. Personalized Spiders for Web Search and Analysis, JCDL 7801, Virginia, USA, 24-28 Jun, pp. 79-87
  4. Ingwersen, P., 1992. Information Retrieval Interaction, Taylor-Graham.
  5. Iwayama, M., 2000. Relevance Feedback with a Small Number of Relevance Judgements: Incremental Feedback vs. Document Clustering, SIGIR'00, Athens, Greece, July, pp. 10-16.
  6. Jansen, B. J., Spink, A., Saracevic, T., 2000. A study and analysis of users queries on the Web, Information Processing & Management 36. 2., pp. 207-227.
  7. Jeh, G., Widom, J., 2003. Scaling Personalization Web Search, WWW2003, Hungary, 20-24 May, pp. 271- 279.
  8. Jensen F. V., 1999. The book, Bayesian Networks and Decision Graph, Springer.
  9. Kelly, D., Cool, C., 2002. The Effects of Topic Familiarity on Information Search Behavior, JCDL 7802, Portland, Oregon, USA, 13-17 Jul, pp. 74-75.
  10. Liu, B., Chin, C. W., Ng, H. T., 2003. Mining TopicSpecific Concepts and Definitions on the Web, WWW 2003, Budapest, Hungary, 20-24 May, pp. 251-260.
  11. Liu, F., Yu, C., Meng, W., 2002. Personalized Web Search by Mapping User Queries to Categories, CIKM 7802, Virginia, USA, 4-9 Nov, pp. 558-565.
  12. Page, L., Brin, S., Motwani, R., Winograd, T., 1998. The PageRank citation ranking: Bringing order to the Web. Technical report, Standford University Database Group, http://citeseer.nj.nec.com/368196.html.
  13. Pearl J., 1998. Probabilistic Reasoning in Intelligent Systems: networks of plausible inference, Morgan Kaufmann Publishers, San Mateo, CA.
  14. Pitkow, J., Shcutze, H., Cass, T., Cooley, R., 2002. Personalized Search, Communications of the ACM, Vol. 45, No. 9, pp.50-55.
  15. Popescul, A., Ungar, L. H, Pennock, D. M., Lawrence, S., 2001. Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Date Environment, UAI, Seattle, USA, 2-5 Aug 2-5, pp.437-444.
  16. Rocchio J. J., 1971. Relevance Feedback in Information Retrieval, Prentice-Hall Incorporation.
  17. Salton, G., and Buckley, C., 1990. Improving Retrieval Performance by Relevance Feedback, Journal of the America Society for Information Science, 41(4), pp. 288-297.
  18. White, R. W., Ruthven, I., Jose, J. M., 2002. The use of implicit evidence for relevance feedback in web retrieval, European Conference on Information Retrieval Research, UK, March, pp. 93-109.
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Paper Citation


in Harvard Style

C H Lai T., C F Chan S. and F L Chung K. (2004). APPLYING CROSS-TOPIC RELATIONSHIPS TO INCREMENTAL RELEVANCE FEEDBACK . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 972-8865-00-7, pages 356-363. DOI: 10.5220/0002620203560363


in Bibtex Style

@conference{iceis04,
author={Terry C H Lai and Stephen C F Chan and Korris F L Chung},
title={APPLYING CROSS-TOPIC RELATIONSHIPS TO INCREMENTAL RELEVANCE FEEDBACK},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2004},
pages={356-363},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002620203560363},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - APPLYING CROSS-TOPIC RELATIONSHIPS TO INCREMENTAL RELEVANCE FEEDBACK
SN - 972-8865-00-7
AU - C H Lai T.
AU - C F Chan S.
AU - F L Chung K.
PY - 2004
SP - 356
EP - 363
DO - 10.5220/0002620203560363