Abstract
Smartphones have become an indispensable part of our lives. Understanding user behaviors based on smartphone usage data is therefore critical to many applications. In this paper, we propose to address a novel task called Intention2Text which attempts to capture user intents based on smartphone usage log. The goal of Intention2Text is to learn a deep learning model taking mobile context logs as input and generate sentences as output for describing mobile user intentions. So far, we have developed an attentive sequence-to-sequence recurrent neural network for the Intention2Text task as a fundamental model. Also, various model encoding/decoding strategies are introduced and considered. The experiments based on a real community question dataset are conducted to verify the effectiveness of the proposed framework.
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Notes
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VoiceOver API for iOS and Accessibility Service API for Android.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yu, CH., Chen, HY., Leu, FY., Fan, YC. (2020). Understanding Mobile User Intent Using Attentive Sequence-to-Sequence RNNs. In: Deng, DJ., Pang, AC., Lin, CC. (eds) Wireless Internet. WiCON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 317. Springer, Cham. https://doi.org/10.1007/978-3-030-52988-8_5
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