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 proceedingConference contribution book

7 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.
LanguageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication34th European Conference on IR Research
EditorsRicardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri
Place of PublicationHeidelberg
PublisherSpringer
Pages499-502
Number of pages4
ISBN (Print)9783642289965
DOIs
Publication statusPublished - 27 Mar 2012
Externally publishedYes
Event34th European conference on Advances in Information Retrieval - Barcelona, Spain
Duration: 1 Apr 20125 Apr 2012

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7724
ISSN (Print)0302-9743

Conference

Conference34th European conference on Advances in Information Retrieval
Abbreviated titleECIR 2012
CountrySpain
CityBarcelona
Period1/04/125/04/12

Fingerprint

Blogs
Search engines

Keywords

  • vertical intent
  • search interface
  • web search engines

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). Heidelberg: Springer. https://doi.org/10.1007/978-3-642-28997-2_50
Zhou, Ke ; Cummins, Ronan ; Halvey, Martin ; Lalmas, Mounia ; Jose, Joemon M. / 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).
@inproceedings{4d62a96341f04859b621e133557aac8d,
title = "Assessing and predicting vertical intent for web queries",
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.",
keywords = "vertical intent, search interface, web search engines",
author = "Ke Zhou and Ronan Cummins and Martin Halvey and Mounia Lalmas and Jose, {Joemon M.}",
year = "2012",
month = "3",
day = "27",
doi = "10.1007/978-3-642-28997-2_50",
language = "English",
isbn = "9783642289965",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "499--502",
editor = "Ricardo Baeza-Yates and {de Vries}, {Arjen P.} and Hugo Zaragoza and Cambazoglu, {B. Barla} and Vanessa Murdock and Ronny Lempel and Fabrizio Silvestri",
booktitle = "Advances in Information Retrieval",

}

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; Lalmas, Mounia; Jose, Joemon M.

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 proceedingConference contribution book

TY - GEN

T1 - Assessing and predicting vertical intent for web queries

AU - Zhou, Ke

AU - Cummins, Ronan

AU - Halvey, Martin

AU - Lalmas, Mounia

AU - Jose, Joemon M.

PY - 2012/3/27

Y1 - 2012/3/27

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.

KW - vertical intent

KW - search interface

KW - web search engines

UR - https://link.springer.com/

U2 - 10.1007/978-3-642-28997-2_50

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

M3 - Conference contribution book

SN - 9783642289965

T3 - Lecture Notes in Computer Science

SP - 499

EP - 502

BT - Advances in Information Retrieval

A2 - Baeza-Yates, Ricardo

A2 - de Vries, Arjen P.

A2 - Zaragoza, Hugo

A2 - Cambazoglu, B. Barla

A2 - Murdock, Vanessa

A2 - Lempel, Ronny

A2 - Silvestri, Fabrizio

PB - Springer

CY - Heidelberg

ER -

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). https://doi.org/10.1007/978-3-642-28997-2_50