Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
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Journal Title
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A4 Artikkeli konferenssijulkaisussa
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Date
2020-10-19
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Language
en
Pages
4
1969-1972
1969-1972
Series
CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Abstract
Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that mouse cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance.Description
Keywords
deep learning, mouse cursor tracking, query abandonment
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Citation
Brückner, L, Arapakis, I & Leiva, L A 2020, Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements . in CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management . ACM, pp. 1969-1972, ACM International Conference on Information and Knowledge Management, Virtual, Online, Ireland, 19/10/2020 . https://doi.org/10.1145/3340531.3412126
