Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements

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.

Keywords

deep learning, mouse cursor tracking, query abandonment

DOI

10.1145/3340531.3412126

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

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-2020123160271

Collections

[article-cris] Sähkötekniikan korkeakoulu / ELEC

Show all metadata

Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements

Access rights

URL

Journal Title

Journal ISSN

Volume Title

Authors

Date

Department

Major/Subject

Mcode

Degree programme

Language

Pages

Series

Abstract

Description

Keywords

Other note

DOI

Citation

Permanent link to this item

Collections