We tackle the problem of predicting 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.
Temporal Recurrent Activation Networks / Manco, G.; Pirro', Giuseppe.; Ritacco, E.. - 2161:(2018). (Intervento presentato al convegno 26th Italian Symposium on Advanced Database Systems, SEBD 2018 tenutosi a Ethra Reserve, ita).
Temporal Recurrent Activation Networks
Pirro' Giuseppe.
;
2018
Abstract
We tackle the problem of predicting 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
