Predicting Temporal Activation Patterns via Recurrent Neural Networks

被引：2

作者：

Manco, Giuseppe ^{[1
]}

Pirro, Giuseppe ^{[1
]}

Ritacco, Ettore ^{[1
]}

机构：

[1] ICAR, CNR, Via Pietro Bucci 8-9C, I-87036 Arcavacata Di Rende, CS, Italy

来源：

FOUNDATIONS OF INTELLIGENT SYSTEMS (ISMIS 2018) | 2018年 / 11177卷

关键词：

D O I：

10.1007/978-3-030-01851-1_33

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：347 / 356

页数：10

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