Deep learning and hierarchical reinforcement learning for modeling a conversational recommender system

Pierpaolo Basile, Claudio Greco, Alessandro Suglia, Giovanni Semeraro

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)125-141
Number of pages17
JournalIntelligenza Artificiale
Volume12
Issue number2
DOIs
Publication statusPublished - 2018

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