Deep Reinforcement Learning for Boosting Individual and Aggregate Diversity in Product Recommendation Systems

The significance of individual and aggregate diversities has been recognized as an important aspect of the recommendation algorithm. Traditional diversity-enhancing recommendation systems have predominantly optimized accuracy and diversity through static rating processes, neglecting the dynamic, long-term user-recommender interactions. This study proposes a novel approach employing deep reinforcement learning (DRL) to derive an optimal selection policy that enhances accuracy and individual and aggregate diversities through maximizing expected cumulative rewards. This approach employs Deep Reinforcement Learning (DRL) to create a dynamic recommendation list that integrates diversity using a Markov decision process based on user feedback and ratings. It calculates the rewards by examining the proximity of the recommended list to an ideal distribution across all recommendations, thereby promoting both individual and collective diversity in the items selected. The learning mechanism is powered by the Deep Deterministic Policy Gradient (DDPG) method to refine the selection strategy. Comprehensive tests on datasets from Alibaba and Grouplens confirm that our method achieves accuracy and substantially improves diversity compared to baselines.