Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System

In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framework consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage.



Titolo: Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System
Autori: 
BASILE, PIERPAOLO
SEMERARO, Giovanni
mostra contributor esterni 
Data di pubblicazione: 2018
Rivista: 
INTELLIGENZA ARTIFICIALE  
Handle: http://hdl.handle.net/11586/232048
Appare nelle tipologie:1.1 Articolo in rivista

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