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2018 | OriginalPaper | Chapter

Predicting Temporal Activation Patterns via Recurrent Neural Networks

Authors : Giuseppe Manco, Giuseppe Pirrò, Ettore Ritacco

Published in: Foundations of Intelligent Systems

Publisher: Springer International Publishing

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Abstract

We tackle the problem of predict whether a target user (or group of users) will be active within an event stream before a time horizon. Our solution, called PATH, leverages recurrent neural networks to learn an embedding of the past events. The embedding allows to capture influence and susceptibility between users and places closer (the representation of) users that frequently get active in different event streams within a small time interval. We conduct an experimental evaluation on real world data and compare our approach with related work.

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Metadata
Title
Predicting Temporal Activation Patterns via Recurrent Neural Networks
Authors
Giuseppe Manco
Giuseppe Pirrò
Ettore Ritacco
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-01851-1_33

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