research-article

The wisdom of advertisers: mining subgoals via query clustering

Authors:

Takehiro Yamamoto,

Katsumi TanakaAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 505 - 514

https://doi.org/10.1145/2396761.2396827

Published: 29 October 2012 Publication History

Abstract

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.

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Cited By

Hirano TShoji YYamamoto TDürst M(2023)Buy Eye-Mask Instead of Alarm Clock!: Graph-Based Approach to Identify Functionally Equal Alternative ProductsInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_27(265-279)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-48316-5_27
Déneckère RKornyshova EHug C(2021)A Framework for Comparative Analysis of Intention Mining ApproachesResearch Challenges in Information Science10.1007/978-3-030-75018-3_2(20-37)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75018-3_2
Pothirattanachaikul SYamamoto TFujita STajima ATanaka KYoshikawa M(2018)Mining Alternative Actions from Community Q&A CorpusJournal of Information Processing10.2197/ipsjjip.26.42726(427-438)Online publication date: 2018
https://doi.org/10.2197/ipsjjip.26.427
Show More Cited By

Index Terms

The wisdom of advertisers: mining subgoals via query clustering
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 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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Publication History

Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Hirano TShoji YYamamoto TDürst M(2023)Buy Eye-Mask Instead of Alarm Clock!: Graph-Based Approach to Identify Functionally Equal Alternative ProductsInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_27(265-279)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-3-031-48316-5_27
Déneckère RKornyshova EHug C(2021)A Framework for Comparative Analysis of Intention Mining ApproachesResearch Challenges in Information Science10.1007/978-3-030-75018-3_2(20-37)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75018-3_2
Pothirattanachaikul SYamamoto TFujita STajima ATanaka KYoshikawa M(2018)Mining Alternative Actions from Community Q&A CorpusJournal of Information Processing10.2197/ipsjjip.26.42726(427-438)Online publication date: 2018
https://doi.org/10.2197/ipsjjip.26.427
Pothirattanachaikul SYamamoto TFujita STajima ATanaka KSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Mining alternative actions from community Q&A corpus for task-oriented web searchProceedings of the International Conference on Web Intelligence10.1145/3106426.3106461(607-614)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106461
Wang TLu W(2015)Constructing Complex Search Tasks with Coherent Subtask Search GoalsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/274254715:2(1-29)Online publication date: 11-Dec-2015
https://dl.acm.org/doi/10.1145/2742547
Ren XWang YYu XYan JChen ZHan JCarterette BDiaz FCastillo CMetzler D(2014)Heterogeneous graph-based intent learning with queries, web pages and Wikipedia conceptsProceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2556222(23-32)Online publication date: 24-Feb-2014
https://dl.acm.org/doi/10.1145/2556195.2556222

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