1. Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System.
- Author
-
Basile, Pierpaolo, Greco, Claudio, Suglia, Alessandro, Semeraro, Giovanni, Ferilli, Stefano, and Lisi, Francesca Alessandra
- Subjects
DEEP learning ,RECOMMENDER systems ,REINFORCEMENT learning ,SUPERVISED learning ,MACHINE learning - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF