research-article

Query similarity by projecting the query-flow graph

Authors:

Ilaria Bordino,

Carlos Castillo,

Aristides GionisAuthors Info & Claims

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 515 - 522

https://doi.org/10.1145/1835449.1835536

Published: 19 July 2010 Publication History

Abstract

Defining a measure of similarity between queries is an interesting and difficult problem. A reliable query-similarity measure can be used in a variety of applications such as query recommendation, query expansion, and advertising.

In this paper, we exploit the information present in query logs in order to develop a measure of semantic similarity between queries. Our approach relies on the concept of the query-flow graph. The query-flow graph aggregates query reformulations from many users: nodes in the graph represent queries, and two queries are connected if they are likely to appear as part of the same search goal. Our query similarity measure is obtained by projecting the graph (or appropriate subgraphs of it) on a low-dimensional Euclidean space. Our experiments show that the measure we obtain captures a notion of semantic similarity between queries and it is useful for diversifying query recommendations.

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Cited By

Shi LSong GCheng GLiu X(2020)A user-based aggregation topic model for understanding user’s preference and intention in social networkNeurocomputing10.1016/j.neucom.2020.06.099413(1-13)Online publication date: Nov-2020
https://doi.org/10.1016/j.neucom.2020.06.099
Yang WWang KRuan NGao WJia WZhao WLiu NZhang Y(2019)Time-Sync Video Tag Extraction Using Semantic Association GraphACM Transactions on Knowledge Discovery from Data10.1145/333293213:4(1-24)Online publication date: 2-Jul-2019
https://dl.acm.org/doi/10.1145/3332932
Liang SYilmaz EKanoulas E(2019)Collaboratively Tracking Interests for User Clustering in Streams of Short TextsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.283221131:2(257-272)Online publication date: 1-Feb-2019
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Index Terms

Query similarity by projecting the query-flow graph
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

Copyright © 2010 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Shi LSong GCheng GLiu X(2020)A user-based aggregation topic model for understanding user’s preference and intention in social networkNeurocomputing10.1016/j.neucom.2020.06.099413(1-13)Online publication date: Nov-2020
https://doi.org/10.1016/j.neucom.2020.06.099
Yang WWang KRuan NGao WJia WZhao WLiu NZhang Y(2019)Time-Sync Video Tag Extraction Using Semantic Association GraphACM Transactions on Knowledge Discovery from Data10.1145/333293213:4(1-24)Online publication date: 2-Jul-2019
https://dl.acm.org/doi/10.1145/3332932
Liang SYilmaz EKanoulas E(2019)Collaboratively Tracking Interests for User Clustering in Streams of Short TextsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.283221131:2(257-272)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TKDE.2018.2832211
Ou GWakabayashi KSatoh T(2019)Searching Behavior Analysis of Online Shopping Based on Information Content of Query Words2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI.2019.00020(43-48)Online publication date: Jul-2019
https://doi.org/10.1109/IIAI-AAI.2019.00020
Nozaki YWatanabe FSatoh T(2018)Analysis of Item Selection Behavior in Online ShoppingProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282849(41-45)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282849
Nozaki YSatoh T(2018)Search Log Analysis Method of Online Shopping Sites for Navigating Item CategoriesProceedings of the 20th International Conference on Information Integration and Web-based Applications & Services10.1145/3282373.3282408(87-95)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3282373.3282408
Ren YTomko MSalim FChan JClarke CSanderson M(2018)A Location-Query-Browse Graph for Contextual RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276605930:2(204-218)Online publication date: 1-Feb-2018
https://doi.org/10.1109/TKDE.2017.2766059
Nozaki YSatoh T(2018)Category Classification Methods Reflecting Item Search Behaviors on Online Shopping Sites2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI.2018.00017(32-37)Online publication date: Jul-2018
https://doi.org/10.1109/IIAI-AAI.2018.00017
Zhou KYang Q(2018)LDA-PSTR: A Topic Modeling Method for Short TextAdvanced Data Mining and Applications10.1007/978-3-030-05090-0_29(339-352)Online publication date: 29-Dec-2018
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Liang SRen ZYilmaz EKanoulas ESingh SMarkovitch S(2017)Collaborative user clustering for short text streamsProceedings of the Thirty-First AAAI Conference on Artificial Intelligence10.5555/3298023.3298077(3504-3510)Online publication date: 4-Feb-2017
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