Conversational Recommendation System with Unsupervised Learning
This addresses the barrier of high data requirements for launching domain-specific conversational agents, potentially benefiting e-commerce and customer service applications.
The paper tackles the problem of building conversational recommendation systems without requiring extensive labeled data or hand-written rules, demonstrating a virtual sales agent that learns to interact, answer questions, and make recommendations using unsupervised deep learning methods.
We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.