Assessing and predicting vertical intent for web queries

Ke Zhou; Ronan Cummins; Martin Halvey; Mounia Lalmas; Joemon M. Jose

doi:10.1007/978-3-642-28997-2_50

Assessing and predicting vertical intent for web queries

Ke Zhou, Ronan Cummins, Martin Halvey, Mounia Lalmas, Joemon M. Jose

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

9 Citations (Scopus)

Abstract

Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.

Original language	English
Title of host publication	Advances in Information Retrieval
Subtitle of host publication	34th European Conference on IR Research
Editors	Ricardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri
Place of Publication	Heidelberg
Publisher	Springer
Pages	499-502
Number of pages	4
ISBN (Print)	9783642289965
DOIs	https://doi.org/10.1007/978-3-642-28997-2_50
Publication status	Published - 27 Mar 2012
Externally published	Yes
Event	34th European conference on Advances in Information Retrieval - Barcelona, Spain Duration: 1 Apr 2012 → 5 Apr 2012

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	7724
ISSN (Print)	0302-9743

Conference

Conference	34th European conference on Advances in Information Retrieval
Abbreviated title	ECIR 2012
Country/Territory	Spain
City	Barcelona
Period	1/04/12 → 5/04/12

Keywords

vertical intent
search interface
web search engines

Access to Document

10.1007/978-3-642-28997-2_50

Cite this

Zhou, K., Cummins, R., Halvey, M., Lalmas, M., & Jose, J. M. (2012). Assessing and predicting vertical intent for web queries. In R. Baeza-Yates, A. P. de Vries, H. Zaragoza, B. B. Cambazoglu, V. Murdock, R. Lempel, & F. Silvestri (Eds.), Advances in Information Retrieval: 34th European Conference on IR Research (pp. 499-502). (Lecture Notes in Computer Science; Vol. 7724). Springer. https://doi.org/10.1007/978-3-642-28997-2_50

Zhou, Ke ; Cummins, Ronan ; Halvey, Martin et al. / Assessing and predicting vertical intent for web queries. Advances in Information Retrieval: 34th European Conference on IR Research. editor / Ricardo Baeza-Yates ; Arjen P. de Vries ; Hugo Zaragoza ; B. Barla Cambazoglu ; Vanessa Murdock ; Ronny Lempel ; Fabrizio Silvestri. Heidelberg : Springer, 2012. pp. 499-502 (Lecture Notes in Computer Science).

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abstract = "Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.",

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author = "Ke Zhou and Ronan Cummins and Martin Halvey and Mounia Lalmas and Jose, {Joemon M.}",

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Zhou, K, Cummins, R, Halvey, M, Lalmas, M & Jose, JM 2012, Assessing and predicting vertical intent for web queries. in R Baeza-Yates, AP de Vries, H Zaragoza, BB Cambazoglu, V Murdock, R Lempel & F Silvestri (eds), Advances in Information Retrieval: 34th European Conference on IR Research. Lecture Notes in Computer Science, vol. 7724, Springer, Heidelberg, pp. 499-502, 34th European conference on Advances in Information Retrieval, Barcelona, Spain, 1/04/12. https://doi.org/10.1007/978-3-642-28997-2_50

Assessing and predicting vertical intent for web queries. / Zhou, Ke; Cummins, Ronan; Halvey, Martin et al.

Advances in Information Retrieval: 34th European Conference on IR Research. ed. / Ricardo Baeza-Yates; Arjen P. de Vries; Hugo Zaragoza; B. Barla Cambazoglu; Vanessa Murdock; Ronny Lempel; Fabrizio Silvestri. Heidelberg : Springer, 2012. p. 499-502 (Lecture Notes in Computer Science; Vol. 7724).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Cummins, Ronan

AU - Halvey, Martin

AU - Lalmas, Mounia

AU - Jose, Joemon M.

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N2 - Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.

AB - Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.

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A2 - Silvestri, Fabrizio

PB - Springer

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Zhou K, Cummins R, Halvey M, Lalmas M, Jose JM. Assessing and predicting vertical intent for web queries. In Baeza-Yates R, de Vries AP, Zaragoza H, Cambazoglu BB, Murdock V, Lempel R, Silvestri F, editors, Advances in Information Retrieval: 34th European Conference on IR Research. Heidelberg: Springer. 2012. p. 499-502. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-28997-2_50

Assessing and predicting vertical intent for web queries

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Keywords

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