Handling Cold-Start Collaborative Filtering with Reinforcement Learning
This addresses the challenge of making accurate recommendations for new users in recommender systems, which is an incremental improvement in a domain-specific context.
The paper tackles the cold-start problem in recommender systems by proposing a novel approach using Deep Q Networks to learn optimal interview questions for new users, resulting in improved recommendations for cold-start users in movie recommender systems.
A major challenge in recommender systems is handling new users, whom are also called $\textit{cold-start}$ users. In this paper, we propose a novel approach for learning an optimal series of questions with which to interview cold-start users for movie recommender systems. We propose learning interview questions using Deep Q Networks to create user profiles to make better recommendations to cold-start users. While our proposed system is trained using a movie recommender system, our Deep Q Network model should generalize across various types of recommender systems.