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Query and content suggestion based on latent interest and topic class

Published: 19 May 2004 Publication History

Abstract

To improve the process of user information retrieval, we propose the concept of a latent semantic map (LSM), along with a method of generating this map. The novel aspect of the LSM is that it can archive user models and latent semantic analysis on one map to support instantaneous information retrieval. With this characteristic, the LSM can improve search engines in terms of not only user support but also search results.

References

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B. D. Davison, D. G. Deschenes and D. B. Lewanda. Finding Relevant Website Queries, In Proceeding of the 12th World Wide Web Conference (WWW12), pages 162--168, Budapest, Hungary, May 2003.
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L. Fitzpatrick and M. Dent. Automatic feedback using past queries: Social searching? In Proceedings of the 20th International ACM SIGIR, Philadelphia, PA, USA, July 1997.
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N. S. Glance. Community search assistant. In Artificial Intelligence for Web Search, pages 29--34. AAAI Press, July 2000.
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G. Jeh and J. Widom. SimRank: A measure of structuralcontext similarity. In Proceedings of the 8th ACM SIGKDD, Edmonton, Alberta, Canada, July 2002.
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N. Kawamae, H. Suzuki and O. Mizuno. Collaborative Filtering Via Bayesian Statistical Model, to appear.
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V. V. Raghavan and H. Sever. On the Reuse of Past Optimal Queries. In Proceedings of the 18th ACM I SIGIR'95, Seattle, WA, USA, July 1995.

Cited By

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  • (2016)A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test CollectionsAI 2016: Advances in Artificial Intelligence10.1007/978-3-319-50127-7_57(648-664)Online publication date: 29-Nov-2016

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Published In

cover image ACM Conferences
WWW Alt. '04: Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
May 2004
532 pages
ISBN:1581139128
DOI:10.1145/1013367
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2004

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Author Tags

  1. document categorization
  2. document suggestion
  3. information retrieval
  4. latent semantic map
  5. query suggestion

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Cited By

View all
  • (2016)A Topic Transition Map for Query Expansion: A Semantic Analysis of Click-Through Data and Test CollectionsAI 2016: Advances in Artificial Intelligence10.1007/978-3-319-50127-7_57(648-664)Online publication date: 29-Nov-2016

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