Learning Efficient Representations of Mouse Movements to Predict User Attention

Ioannis Arapakis, Luis A. Leiva

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

4 Sitaatiot (Scopus)
127 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoSIGIR 2020 - Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
KustantajaACM
Sivut1309-1318
Sivumäärä10
ISBN (elektroninen)9781450380164
DOI - pysyväislinkit
TilaJulkaistu - 25 heinäkuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational ACM SIGIR Conference on Research and Development in Information Retrieval - Virtual, Online, Kiina
Kesto: 25 heinäkuuta 202030 heinäkuuta 2020
Konferenssinumero: 43

Conference

ConferenceInternational ACM SIGIR Conference on Research and Development in Information Retrieval
LyhennettäSIGIR
MaaKiina
KaupunkiVirtual, Online
Ajanjakso25/07/202030/07/2020

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