Identifying user intent through query refinements
ZHANG Xiaojuan; LU Wei; Lu Wei (E-mail: reedwhu@gmail.com)
2013-09-25
Source PublicationChinese Journal of Library and Information Science
ISSN1674-3393
Volume6Issue:3Pages:1-14
AbstractPurpose: In this paper, we attempt to use query refinements to identify users’ search intents and seek a method for intent clustering based on real world query data.

Design/methodology/approach: An experiment has been conducted to analyze selected search sessions from the American Online (AOL) query logs with a two-stage approach. The first stage is to identify underlying intent by combining query co-occurrence information with query expression similarity. The work in the second stage is to cluster identified results by constructing query vectors through performing random walks on a Markov graph.

Findings: Average correctness for identifying search intent is 0.74. Precision, recall, F-score values for intent clustering are 0.73, 0.72 and 0.71, respectively. The results indicate that combining session co-occurrence information and query expression similarity can further filter noises and our clustering method is more suitable for sparse data.

Research limitations: We use the time-out threshold (15-minute) method to group queries in one session, but a user may have multiple search goals at the same time and the multi-task behavior of a user is hard to capture in a session defined based on time notions.

Practical implications: This study provides insights into the ways of understanding users’ search intents by analyzing their queries and refinements from a new perspective. The results will help search engine developers to identify user intents.

Originality/value: We propose a new method to identify users’ search intents by combining session co-occurrence information and query expression similarity, and a new method for clustering sparse data.; Purpose: In this paper, we attempt to use query refinements to identify users’ search intents and seek a method for intent clustering based on real world query data.

Design/methodology/approach: An experiment has been conducted to analyze selected search sessions from the American Online (AOL) query logs with a two-stage approach. The first stage is to identify underlying intent by combining query co-occurrence information with query expression similarity. The work in the second stage is to cluster identified results by constructing query vectors through performing random walks on a Markov graph.

Findings: Average correctness for identifying search intent is 0.74. Precision, recall, F-score values for intent clustering are 0.73, 0.72 and 0.71, respectively. The results indicate that combining session co-occurrence information and query expression similarity can further filter noises and our clustering method is more suitable for sparse data.

Research limitations: We use the time-out threshold (15-minute) method to group queries in one session, but a user may have multiple search goals at the same time and the multi-task behavior of a user is hard to capture in a session defined based on time notions.

Practical implications: This study provides insights into the ways of understanding users’ search intents by analyzing their queries and refinements from a new perspective. The results will help search engine developers to identify user intents.

Originality/value: We propose a new method to identify users’ search intents by combining session co-occurrence information and query expression similarity, and a new method for clustering sparse data.
KeywordQuery Intent
Subject Area编辑出版
URL查看原文
Funding OrganizationThis work is jointly supported by the National Natural Science Foundation of China (Grant No.: 71173164) and the National Key Technology R&D Program of the Ministry of Science and Technology of China (Grant No.: 2012BAH33F03).
Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/6638
CollectionJournal of Data and Information Science_Chinese Journal of Library and Information Science-2013
Corresponding AuthorLu Wei (E-mail: reedwhu@gmail.com)
Recommended Citation
GB/T 7714
ZHANG Xiaojuan,LU Wei,Lu Wei . Identifying user intent through query refinements[J]. Chinese Journal of Library and Information Science,2013,6(3):1-14.
APA ZHANG Xiaojuan,LU Wei,&Lu Wei .(2013).Identifying user intent through query refinements.Chinese Journal of Library and Information Science,6(3),1-14.
MLA ZHANG Xiaojuan,et al."Identifying user intent through query refinements".Chinese Journal of Library and Information Science 6.3(2013):1-14.
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