Resolving the Cold-Start Issue in Recommender Systems with Reinforcement Learning
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Resolving the Cold-Start Issue in Recommender Systems with Reinforcement Learning
Authors:
Anunay Singh1 , Anveet Pal2 , Ashish Baghel3
1Department of Computer Science & Engineering, Medi-Caps University, Indore (M.P.) 2Department of Computer Science & Engineering, Medi-Caps University, Indore (M.P.) 3Department of Computer Science & Engineering, Medi-Caps University, Indore (M.P.)
Abstract - The cold-start problem faced by recommender systems is a serious problem, mainly because of the lack of historical data for new users or items. Traditional recommendation techniques, such as collaborative filtering and content-based filtering, are prone to fail in making good recommendations under such conditions. This paper explores the use of reinforcement learning (RL) as a remedy for cold-start problems based on active learning methods and multi-armed bandit models. We propose a novel RL-based approach that learns user preferences incrementally from interaction and improves recommendations in an exploration-exploitation setting. The setting shows improved performance in personalization and user engagement compared to baseline methods. This paper also provides the first-order implications of the cold-start problem in the real world and assesses the challenges faced by industries impacted by this problem.
Keywords - Recommender systems, cold-start problem, reinforcement learning, Markov decision processes, deep reinforcement learning, user modelling
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