Knowledge Commons of National Science Library，CAS
| Identifying user intent through query refinements | |
| ZHANG Xiaojuan; LU Wei; Lu Wei (E-mail: reedwhu@gmail.com) | |
| 2013-09-25 | |
| Source Publication | Chinese Journal of Library and Information Science
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| ISSN | 1674-3393 |
| Volume | 6Issue:3Pages:1-14 |
| 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.; 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. |
| Keyword | Query Intent |
| Subject Area | 编辑出版 |
| URL | 查看原文 |
| Funding Organization | This 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 |
| Identifier | http://ir.las.ac.cn/handle/12502/6638 |
| Collection | Journal of Data and Information Science_Chinese Journal of Library and Information Science-2013 |
| Corresponding Author | Lu 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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