Learning Efficient Representations of Mouse Movements to Predict User Attention

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Journal Title
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Volume Title
Conference article in proceedings
Date
2020-07-25
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
1309-1318
Series
SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
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.
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Keywords
direct displays, mouse cursor, neural networks, online advertising, sponsored search, transfer learning, user attention
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Citation
Arapakis, I & Leiva, L A 2020, Learning Efficient Representations of Mouse Movements to Predict User Attention . in SIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . ACM, pp. 1309-1318, International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual, Online, China, 25/07/2020 . https://doi.org/10.1145/3397271.3401031