Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements

Lukas Brückner, Ioannis Arapakis, Luis A. Leiva

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
70 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherACM
Pages1969-1972
Number of pages4
ISBN (Electronic)9781450368599
DOIs
Publication statusPublished - 19 Oct 2020
MoE publication typeA4 Article in a conference publication
EventACM International Conference on Information and Knowledge Management - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020
Conference number: 29

Conference

ConferenceACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM
Country/TerritoryIreland
CityVirtual, Online
Period19/10/202023/10/2020

Keywords

  • deep learning
  • mouse cursor tracking
  • query abandonment

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