Learning Efficient Representations of Mouse Movements to Predict User Attention

Ioannis Arapakis, Luis A. Leiva

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

2 Citations (Scopus)
112 Downloads (Pure)

Abstract

Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that often requires domain expertise. We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements. Our models are trained over raw mouse cursor data and achieve competitive performance. We conclude that neural network models should be adopted for downstream tasks involving mouse cursor movements, since they can provide an invaluable implicit feedback signal for re-ranking and evaluation.

Original languageEnglish
Title of host publicationSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherACM
Pages1309-1318
Number of pages10
ISBN (Electronic)9781450380164
DOIs
Publication statusPublished - 25 Jul 2020
MoE publication typeA4 Article in a conference publication
EventInternational ACM SIGIR Conference on Research and Development in Information Retrieval - Virtual, Online, China
Duration: 25 Jul 202030 Jul 2020
Conference number: 43

Conference

ConferenceInternational ACM SIGIR Conference on Research and Development in Information Retrieval
Abbreviated titleSIGIR
CountryChina
CityVirtual, Online
Period25/07/202030/07/2020

Keywords

  • direct displays
  • mouse cursor
  • neural networks
  • online advertising
  • sponsored search
  • transfer learning
  • user attention

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