Incorporating Query Reformulating Behavior into Web Search Evaluation

Jia Chen, Yiqun Liu*, Jiaxin Mao, Fan Zhang, Tetsuya Sakai, Weizhi Ma, Min Zhang, Shaoping Ma

*この研究の対応する著者

研究成果: Conference contribution

抄録

While batch evaluation plays a central part in Information Retrieval (IR) research, most evaluation metrics are based on user models which mainly focus on browsing and clicking behaviors. As users' perceived satisfaction may also be impacted by their search intent, constructing different user models across various search intent may help design better evaluation metrics. However, user intents are usually unobservable in practice. As query reformulating behaviors may reflect their search intents to a certain extent and highly correlate with users' perceived satisfaction for a specific query, these observable factors may be beneficial for the design of evaluation metrics. How to incorporate the search intent behind query reformulation into user behavior and satisfaction models remains under-investigated. To investigate the relationships among query reformulations, search intent, and user satisfaction, we explore a publicly available web search dataset and find that query reformulations can be a good proxy for inferring user intent, and therefore, reformulating actions may be beneficial for designing better web search effectiveness metrics. A group of Reformulation-Aware Metrics (RAMs) is then proposed to improve existing click model-based metrics. Experimental results on two public session datasets have shown that RAMs have significantly higher correlations with user satisfaction than existing evaluation metrics. In the robustness test, we have found that RAMs can achieve good performance when only a small proportion of satisfaction training labels are available. We further show that RAMs can be directly applied in a new dataset for offline evaluation once trained. This work shows the possibility of designing better evaluation metrics by incorporating fine-grained search context factors.

本文言語English
ホスト出版物のタイトルCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
出版社Association for Computing Machinery
ページ171-180
ページ数10
ISBN(電子版)9781450384469
DOI
出版ステータスPublished - 2021 10月 26
イベント30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
継続期間: 2021 11月 12021 11月 5

出版物シリーズ

名前International Conference on Information and Knowledge Management, Proceedings

Conference

Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
国/地域Australia
CityVirtual, Online
Period21/11/121/11/5

ASJC Scopus subject areas

  • ビジネス、管理および会計(全般)
  • 決定科学(全般)

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