research-article

Post-ranking query suggestion by diversifying search results

Authors:

Li-wei HeAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 815 - 824

https://doi.org/10.1145/2009916.2010025

Published: 24 July 2011 Publication History

Abstract

Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of the search results from suggested queries while keeping the suggestion relevant. Our framework first retrieves a set of query candidates from search engine logs using random walk and other techniques. We then re-rank the suggested queries by ranking them in the order which maximizes the diversification function that measures the difference between the original search results and the results from suggested queries. The diversification function we proposed includes features like ODP category, URL and domain similarity and so on. One important outcome from our research which contradicts with most existing researches is that, with the increase of suggestion relevance, the similarity between the queries actually decreases. Experiments are conducted on a large set of human-labeled data, which is randomly sampled from a commercial search engine's log. Results indicate that the post-ranking framework significantly improves the relevance of suggested queries by comparing to existing models.

References

[1]

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong. Diversifying search results. In Proceedings of WSDM '09, pages 5--14, New York, NY, USA, 2009. ACM.

Digital Library

[2]

R. Baeza-yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In In International Workshop on Clustering Information over the Web (ClustWeb, in conjunction with EDBT), Creete, pages 588--596. Springer, 2004.

Digital Library

[3]

R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In Proceedings of KDD '07, pages 76--85, New York, NY, USA, 2007. ACM.

Digital Library

[4]

P. N. Bennett and N. Nguyen. Refined experts: improving classification in large taxonomies. In SIGIR '09, pages 11--18, New York, NY, USA, 2009. ACM.

Digital Library

[5]

J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In Proceedings of SIGIR '98, pages 335--336, New York, NY, USA, 1998. ACM.

Digital Library

[6]

W. Chu and S. S. Keerthi. New approaches to support vector ordinal regression. In Proceedings of ICML '05, pages 145--152, New York, NY, USA, 2005. ACM.

Digital Library

[7]

K. Crammer, Y. Singer, N. Cristianini, J. Shawe-taylor, and B. Williamson. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:2001, 2001.

Digital Library

[8]

H. Deng, I. King, and M. R. Lyu. Entropy-biased models for query representation on the click graph. In SIGIR '09, pages 339--346, New York, NY, USA, 2009. ACM.

Digital Library

[9]

J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189--1232, 2000.

[10]

K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst., 20:422--446, October 2002.

Digital Library

[11]

T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In SIGIR '05, pages 154--161, New York, NY, USA, 2005. ACM.

Digital Library

[12]

M. G. Kendall. A New Measure of Rank Correlation. Biometrika, 30(1/2):81--93, June 1938.

[13]

P. Li, C. J. C. Burges, and Q. Wu. Mcrank: Learning to rank using multiple classification and gradient boosting. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors, NIPS. MIT Press, 2007.

[14]

T.-Y. Liu. Learning to rank for information retrieval. Found. Trends Inf. Retr., 3:225--331, March 2009.

Digital Library

[15]

H. Ma, M. R. Lyu, and I. King. Diversifying query suggestion results. In AAAI, 2010.

[16]

H. Ma, H. Yang, I. King, and M. R. Lyu. Learning latent semantic relations from clickthrough data for query suggestion. In CIKM '08, pages 709--718, New York, NY, USA, 2008. ACM.

Digital Library

[17]

Q. Mei, D. Zhou, and K. Church. Query suggestion using hitting time. In CIKM '08, pages 469--478, New York, NY, USA, 2008. ACM.

Digital Library

[18]

K. Pearson. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine, 302:157--175, 1900.

[19]

Y. Song and L. He. Optimal rare query suggestion with implicit user feedback. In WWW '10, pages 901--910, New York, NY, USA, 2010. ACM.

Digital Library

[20]

H. Tong, C. Faloutsos, and J.-Y. Pan. Random walk with restart: fast solutions and applications. Knowl. Inf. Syst., 14:327--346, March 2008.

Digital Library

[21]

I. Tsochantaridis. Support vector machine learning for interdependent and structured output spaces. PhD thesis, Providence, RI, USA, 2005. AAI3174684.

Digital Library

[22]

X. Wang, H. Fang, and C. Zhai. Improve retrieval accuracy for difficult queries using negative feedback. In CIKM '07, pages 991--994, New York, NY, USA, 2007. ACM.

Digital Library

[23]

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Inf. Retr., 13:254--270, June 2010.

Digital Library

[24]

C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In CIKM '01, pages 403--410, New York, NY, USA, 2001. ACM.

Digital Library

[25]

C. Zhai and J. Lafferty. A risk minimization framework for information retrieval. Inf. Process. Manage., 42:31--55, January 2006.

Digital Library

Cited By

Brandsen AVerberne SLambers KWansleeben M(2022)Can BERT Dig It? Named Entity Recognition for Information Retrieval in the Archaeology DomainJournal on Computing and Cultural Heritage 10.1145/349784215:3(1-18)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1145/3497842
Mustar ALamprier SPiwowarski B(2021)On the Study of Transformers for Query SuggestionACM Transactions on Information Systems10.1145/347056240:1(1-27)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3470562
He YMao XWei WHuang H(2021)Question-formed Query Suggestion2021 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICKG52313.2021.00071(1-8)Online publication date: Dec-2021
https://doi.org/10.1109/ICKG52313.2021.00071
Show More Cited By

Index Terms

Post-ranking query suggestion by diversifying search results
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 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: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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

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Cited By

Brandsen AVerberne SLambers KWansleeben M(2022)Can BERT Dig It? Named Entity Recognition for Information Retrieval in the Archaeology DomainJournal on Computing and Cultural Heritage 10.1145/349784215:3(1-18)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1145/3497842
Mustar ALamprier SPiwowarski B(2021)On the Study of Transformers for Query SuggestionACM Transactions on Information Systems10.1145/347056240:1(1-27)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3470562
He YMao XWei WHuang H(2021)Question-formed Query Suggestion2021 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICKG52313.2021.00071(1-8)Online publication date: Dec-2021
https://doi.org/10.1109/ICKG52313.2021.00071
Reddy RVaishnavi CMamatha CAnanthakumaran S(2021)Location‐Aware Keyword Query Suggestion Techniques With Artificial Intelligence PerspectiveComputational Analysis and Deep Learning for Medical Care10.1002/9781119785750.ch2(35-51)Online publication date: 13-Aug-2021
https://doi.org/10.1002/9781119785750.ch2
Huang JWang HFan MZhuo ALi YGupta RLiu YShah MRajan STang JPrakash B(2020)Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu MapsProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403318(2677-2685)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403318
Li YHuang JFan MLei JWang HChen E(2020)Personalized Query Auto-Completion for Large-Scale POI Search at Baidu MapsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/339413719:5(1-16)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1145/3394137
Zhang XJiang XQin J(2020)Time-aware query suggestion diversification for temporally ambiguous queriesThe Electronic Library10.1108/EL-12-2019-0296ahead-of-print:ahead-of-printOnline publication date: 12-Aug-2020
https://doi.org/10.1108/EL-12-2019-0296
Liao ZSong YZhou D(2020)Query SuggestionQuery Understanding for Search Engines10.1007/978-3-030-58334-7_8(171-203)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_8
Chen JMao JLiu YZhang MMa S(2019)Investigating Query Reformulation Behavior of Search UsersInformation Retrieval10.1007/978-3-030-31624-2_4(39-51)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-31624-2_4
Cormode GDasgupta AGoyal ALee C(2018)An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logsPLOS ONE10.1371/journal.pone.019117513:1(e0191175)Online publication date: 18-Jan-2018
https://doi.org/10.1371/journal.pone.0191175
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