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Clustering queries for better document ranking

Authors:

Yi Liu,

Liangjie Zhang,

Ruihua Song,

Jian-Yun Nie,

Ji-Rong WenAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1569 - 1572

https://doi.org/10.1145/1645953.1646174

Published: 02 November 2009 Publication History

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Abstract

Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.

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Choi DKim TMin MLee JLee SHanzo LChung MLee SCho K(2011)An approach to use query-related web context on document rankingProceedings of the 5th International Conference on Ubiquitous Information Management and Communication10.1145/1968613.1968618(1-7)Online publication date: 21-Feb-2011
https://dl.acm.org/doi/10.1145/1968613.1968618
Bedi PBanati HThukral A(2010)Social Semantic Retrieval and Ranking of eResourcesProceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing10.1109/ARTCom.2010.8(343-347)Online publication date: 16-Oct-2010
https://dl.acm.org/doi/10.1109/ARTCom.2010.8

Index Terms

Clustering queries for better document ranking
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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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Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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Choi DKim TMin MLee JLee SHanzo LChung MLee SCho K(2011)An approach to use query-related web context on document rankingProceedings of the 5th International Conference on Ubiquitous Information Management and Communication10.1145/1968613.1968618(1-7)Online publication date: 21-Feb-2011
https://dl.acm.org/doi/10.1145/1968613.1968618
Bedi PBanati HThukral A(2010)Social Semantic Retrieval and Ranking of eResourcesProceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing10.1109/ARTCom.2010.8(343-347)Online publication date: 16-Oct-2010
https://dl.acm.org/doi/10.1109/ARTCom.2010.8

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Finding similar queries based on query representation analysis

A Query-Dependent Ranking Approach for Search Engines

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Personalized Concept-Based Clustering of Search Engine Queries

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A Query-Dependent Ranking Approach for Search Engines

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