Interactive Conversational Search and Recommendation by Deep Reinforcement Learning
Talk, UC Berkeley School of Information, Berkeley, California
By enabling retrieval and recommender systems to dynamically obtain user preferences through conversations with users, conversational search and recommendation have become increasingly popular in recent years. This process starts with receiving a request from the user and continues with asking clarifying questions or suggesting some possible items or documents by the system. In this way, the system can get valuable feedback from users to accurately determine the users’ needs. This process repeats until the search or recommendation is successful, or the user accepts defeat.