A probabilistic topic-based ranking framework for location-sensitive domain information retrieval

Huajing Li, Zhisheng Li, Wang-chien Lee, Dik Lun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

It has been observed that many queries submitted to search engines are location-sensitive. Traditional search techniques fail to interpret the significance of such geographical clues and as such are unable to return highly relevant search results. Although there have been efforts in the literature to support location-aware information retrieval, critical challenges still remain in terms of search result quality and data scalability. In this paper, we propose an innovative probabilistic ranking framework for domain information retrieval where users are interested in a set of location-sensitive topics. Our proposed method recognizes the geographical distribution of topic influence in the process of ranking documents and models it accurately using probabilistic Gaussian Process classifiers. Additionally, we demonstrate the effectiveness of the proposed ranking framework by implementing it in a Web search service for NBA news. Extensive performance evaluation is performed on real Web document collections, which confirms that our proposed mechanism works significantly better (around 29.7% averagely using DCG20 measure) than other popular location-aware information retrieval techniques in ranking quality.

Original languageEnglish (US)
Title of host publicationProceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009
Pages331-338
Number of pages8
DOIs
StatePublished - Dec 28 2009
Event32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 - Boston, MA, United States
Duration: Jul 19 2009Jul 23 2009

Other

Other32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009
CountryUnited States
CityBoston, MA
Period7/19/097/23/09

Fingerprint

Information retrieval
Geographical distribution
Search engines
Scalability
Classifiers
Ranking

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

Cite this

Li, H., Li, Z., Lee, W., & Lee, D. L. (2009). A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. In Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 (pp. 331-338) https://doi.org/10.1145/1571941.1571999
Li, Huajing ; Li, Zhisheng ; Lee, Wang-chien ; Lee, Dik Lun. / A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009. 2009. pp. 331-338
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abstract = "It has been observed that many queries submitted to search engines are location-sensitive. Traditional search techniques fail to interpret the significance of such geographical clues and as such are unable to return highly relevant search results. Although there have been efforts in the literature to support location-aware information retrieval, critical challenges still remain in terms of search result quality and data scalability. In this paper, we propose an innovative probabilistic ranking framework for domain information retrieval where users are interested in a set of location-sensitive topics. Our proposed method recognizes the geographical distribution of topic influence in the process of ranking documents and models it accurately using probabilistic Gaussian Process classifiers. Additionally, we demonstrate the effectiveness of the proposed ranking framework by implementing it in a Web search service for NBA news. Extensive performance evaluation is performed on real Web document collections, which confirms that our proposed mechanism works significantly better (around 29.7{\%} averagely using DCG20 measure) than other popular location-aware information retrieval techniques in ranking quality.",
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Li, H, Li, Z, Lee, W & Lee, DL 2009, A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. in Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009. pp. 331-338, 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, Boston, MA, United States, 7/19/09. https://doi.org/10.1145/1571941.1571999

A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. / Li, Huajing; Li, Zhisheng; Lee, Wang-chien; Lee, Dik Lun.

Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009. 2009. p. 331-338.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Li H, Li Z, Lee W, Lee DL. A probabilistic topic-based ranking framework for location-sensitive domain information retrieval. In Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009. 2009. p. 331-338 https://doi.org/10.1145/1571941.1571999