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Title: Identifying user intent through query refinements
Author: ZHANG Xiaojuan ; LU Wei
Source: Chinese Journal of Library and Information Science
Issued Date: 2013-09-25
Volume: 6, Issue:3, Pages:1-14
Keyword: Query intent
Subject: 编辑出版
Corresponding Author: Lu Wei (E-mail: reedwhu@gmail.com)
Abstract: 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.
English Abstract: 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.
Related URLs: 查看原文
Content Type: 期刊论文
URI: http://ir.las.ac.cn/handle/12502/6638
Appears in Collections:Chinese Journal of Library and Information Science-2013_期刊论文

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Recommended Citation:
ZHANG Xiaojuan,LU Wei. Identifying user intent through query refinements[J]. Chinese Journal of Library and Information Science,2013,6(3):1-14.
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