2016/09/22 by Yueming Sun, Sun, Yueming, Yi Zhang +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1610.01546
arxiv created 2016/09/22 · openalex publication_date 2016/09/22 · arxiv updated 2016/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.