Enhancing click models with mouse movement information

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• 作者：Zeyang Liu ; Jiaxin Mao ; Chao Wang ; Qingyao Ai ; Yiqun Liu…
• 关键词：Mouse movement ; Click model ; Search engine ; Federated search
• 刊名：Information Retrieval Journal
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：20
• 期：1
• 页码：53-80
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Storage and Retrieval; Document Preparation and Text Processing; Data Mining and Knowledge Discovery; Data Structures, Cryptology and Information Theory; Pattern Recognition;
• 出版者：Springer Netherlands
• ISSN：1573-7659
• 卷排序：20

文摘

User interactions in Web search, in particular, clicks, provide valuable hints on document relevance; but the signals are very noisy. In order to better understand user click behaviors and to infer the implied relevance, various click models have been proposed, each relying on some hypotheses and involving different hidden events (e.g. examination). In almost all the existing click models, it is assumed that clicks are the only observable evidence and the examinations of documents are deduced from it. However, with an increasing number of embedded heterogeneous components (e.g. verticals) on Search Engine Result Pages, click information is not sufficient to draw a complete picture of process of user examination, especially in federated search scenario. In practice, we can also collect mouse movement information, which has proven to have a strong correlation with examination. In this paper, we propose to incorporate mouse movement information into existing click models to enhance the estimation of examination. The enhanced click models are shown to have a better ability to predict both user clicks and document relevance, than the original models. The collection of mouse movement information has been implemented in a commercial search engine, showing the feasibility of the approach in practice.