Cited By
View all- Yoshida MArase Y(2015)Trend Query Classification using Label PropagationTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.30.16130:1(161-171)Online publication date: 2015
Intent feature discovery using Q&A corpus and web data
Article No.: 40, Pages 1 - 7
Abstract
User intent in Web search environment is defined as user's information need, and believed to be found by analyzing past data such as queries, click histories and user profiles. However, users may have different intents even in the same queries. In this paper, we attempt to discover the characteristics of intent through finding its features. Our assumption is that if a user expresses the query which clearly points out to certain intent, s/he can reach an intended Web page using that query. We conceptualize this functionality of intent features using intent evolution procedure, called multiple intent model. We collect candidate intent features using Web Q&A corpus analysis, and suggest the automated judgment method using search engine indexes powered by Click Chain Model to demonstrate the adaptability of candidate intent features. Experimental results show that intent features can be extracted efficiently and provide evidences toward intent discovery without human supervision.
References
[1]
P. Anick. Using Terminological Feedback for Web Search Refinement - A Log-based Study. In Proceedings of the 26th International ACM SIGIR conference on Research and Development in Information Retrieval (2003), 88--95.
[2]
P. Anick, V. Murthi and S. Sebastian. Similar Term Discovery using Web Search. In LREC'08 (2008), 1209--1213.
[3]
A. Z. Broder. A Taxonomy of Web Search. SIGIR Forum (2002), 36 (2), 3--10.
[4]
W. Chang, P. Pantel, A-M. Popescu and E. Gabrilovich. Towards Intent-Driven Bidterm Suggestion. In Proceedings of the 18th International World Wide Web Conference (2009), 1093--1094.
[5]
P-A. Chirita, C. S. Firan and W. Nejdl. Personalized Query Expansion for the Web. In Proceedings of the 30th International ACM SIGIR conference on Research and Development in Information Retrieval (2007), 7--14.
[6]
S. Gollapudi and A. Sharma. An Axiomatic Approach for Result Diversification. In Proceedings of the 18th International World Wide Web Conference (2009), 381--390.
[7]
B. M. Fonseca, P. Golgher, B. Pôssas, B. Ribeiro-Neto and N. Ziviani. Concept-Based Interactive Query Expansion. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (2005), 696--703.
[8]
Q. Guo, and E. Agichtein. Exploring Mouse Movements for Inferring Query Intent. In Proceedings of the 31st International SIGIR Conference on Research and Development in Information Retrieval (2008), 707--708.
[9]
F. Guo, C. Liu, A. Kannan, T. Minka, M. Taylor, Y-M. Wang and C. Faloutsos. Click Chain Model in Web Search. In Proceedings of the 18th International World Wide Web Conference (2009), 11--20.
[10]
B. J. Jansen, D. L. Booth, and A. Spink. Determining the informational, navigational, and transactional intent of Web queries. Information Process and Management (2008), 44 (3), 1251--1266.
[11]
B. Kules, R. Capra, M. Banta and T. Sierra. What Do Exploratory Searchers Look at in a Faceted Search Interface?. In Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries (2009), 313--322.
[12]
U. Lee, Z. Liu, and J. Cho. Automatic identification of user goals in Web search. In Proceedings of the 14th International Conference on World Wide Web (2005), 391--400.
[13]
Y. Li, R. Krishnamurthy, S. Vaithyanathan, and H. V. Jagadish. Getting work done on the Web: Supporting transactional queries. In Proceedings of the 29st International SIGIR Conference on Research and Development in Information Retrieval (2006), 557--564.
[14]
D. Metzler, and W. B. Croft, "Analysis of Statistical Question Classification for Fact-based Questions", Information Retrieval, 8 (3), 2004, pp. 481--504.
[15]
R. W. White, S. T. Dumais and J. Teeban. Characterizing the Influence of Domain Expertise on Web Search Behavior. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM2009), 132--141
[16]
R. W. White, B. Kules, S. M. Drucker and M. C. Schraefel. Supporting Exploratory Search. In Communications of the ACM (2006), 49 (4), 37--39.
[17]
M. Yamaguchi, H. Oshima, S. Oyama and K. Tanaka. Unsupervised Discovery of Coordinate Terms for Multiple Aspects from Search Engine Query Logs. In Proceedings of IEEE Web Intelligence (2008), 249--257.
[18]
S. Yoon, A. Jatowt and K. Tanaka. Intent-based Categorization of Search Results Using Questions from Web Q&A Corpus. In Proceedings of the 10th International Conference on Web Information Systems Engineering (2009), 145--158.
Index Terms
- Intent feature discovery using Q&A corpus and web data
Recommendations
Detecting Intent of Web Queries Using Questions and Answers in CQA Corpus
WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01Detecting intent in Web search activity is important task for finding relevant Web information. However extracting intents from users' queries is difficult as users express their intent by issuing short and often ambiguous queries, yet at the same time ...
Characterizing commercial intent
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementUnderstanding the intent underlying user's queries may help personalize search results and therefore improve user satisfaction. We develop a methodology for using the content of search engine result pages (SERPs) along with the information obtained from ...
Impact of query intent and search context on clickthrough behavior in sponsored search
Implicit feedback techniques may be used for query intent detection, taking advantage of user behavior to understand their interests and preferences. In sponsored search, a primary concern is the user's interest in purchasing or utilizing a commercial ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2010
550 pages
ISBN:9781605588933
DOI:10.1145/2108616
- Conference Chairs:
- Kwan-Ho You,
- Sang-Won Lee,
- General Chairs:
- Won Kim,
- Dongho Won
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]
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 January 2010
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
ICUIMC '10
Sponsor:
ICUIMC '10: The 4th International Conference on Ubiquitous Information Management and Communication
January 14 - 15, 2010
Suwon, Republic of Korea
Acceptance Rates
Overall Acceptance Rate 251 of 941 submissions, 27%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 77Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 22 Dec 2024
Other Metrics
Citations
Cited By
View all- Yoshida MArase Y(2015)Trend Query Classification using Label PropagationTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.30.16130:1(161-171)Online publication date: 2015
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in