Abstract
Search engine users usually strive to reformulate their queries in the search process to gain useful information. It is hard for search engines to understand users’ search intents and return appropriate results if they submit improper or ambiguous queries. Therefore, query reformulation is a bottleneck issue in the usability of search engines. Modern search engines normally provide users with some query suggestions for references. To help users to better learn their information needs, it is of vital importance to investigate users’ reformulation behaviors thoroughly. In this paper, we conduct a detailed investigation of users’ session-level reformulation behavior on a large-scale session dataset and discover some interesting findings that previous work may not notice before: (1) Intent ambiguity may be the direct cause of long sessions rather than the complexity of users’ information needs; (2) Both the added and the deleted terms in a reformulation step can be influenced by the clicked results to a greater extent than the skipped ones; (3) Users’ specification actions are more likely to be inspired by the result snippets or the landing pages, while the generalization behaviors are impacted largely by the result titles. We further discuss some concerns about the existing query suggestion task and give some suggestions on the potential research questions for future work. We hope that this work could provide assistance for the researchers who are interested in the relative domain.
This work is supported by Natural Science Foundation of China (Grant No. 61622208, 61532011, 61672311) and National Key Basic Research Program (2015CB358700).
This research is partly supported by the Tsinghua-Sogou Tiangong Institute for Intelligent Computing.
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Notes
- 1.
To access the dataset, please contact chenjia0831@gmail.com.
- 2.
- 3.
Other researchers can use the testing set for system performance evaluation.
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Chen, J., Mao, J., Liu, Y., Zhang, M., Ma, S. (2019). Investigating Query Reformulation Behavior of Search Users. In: Zhang, Q., Liao, X., Ren, Z. (eds) Information Retrieval. CCIR 2019. Lecture Notes in Computer Science(), vol 11772. Springer, Cham. https://doi.org/10.1007/978-3-030-31624-2_4
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