Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
This addresses the problem of improving recommendation accuracy for users engaging in varied scenarios like homepages and search pages, though it appears incremental as it builds on existing multi-scenario learning methods.
The paper tackles the challenge of modeling inconsistent user interests across multiple recommendation scenarios by proposing a reinforcement learning approach that models interest evolution, which surpasses state-of-the-art methods in multi-scenario recommendation tasks.
In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would reflect different aspects users focus on. However, the user interests may be inconsistent in different scenarios, due to differences in decision-making processes and preference expression. This variability complicates unified modeling, making multi-scenario learning a significant challenge. To address this, we propose a novel reinforcement learning approach that models user preferences across scenarios by modeling user interest evolution across multiple scenarios. Our method employs Double Q-learning to enhance next-item prediction accuracy and optimizes contrastive learning loss using Q-value to make model performance better. Experimental results demonstrate that our approach surpasses state-of-the-art methods in multi-scenario recommendation tasks. Our work offers a fresh perspective on multi-scenario modeling and highlights promising directions for future research.