Reinforcement Learning-Based Adaptive Ensemble for Sequential Recommendation

Theoretically, proper ensemble of multiple different models can improve the recommendation effectiveness. Unfortunately, most of the existing ensemble methods which depend on static aggregation strategies are not suitable for sequential recommendation, because they fail to distinguish the differences in predictive capabilities of base models among various users and contexts. In addition, most of the existing diversity measures, which are used in ensemble learning to strengthen the generalization performance, are designed for classification or regression tasks and not suitable for the ranking tasks in recommender systems. To tackle the above problems, we propose a Reinforcement Learning-based Adaptive Ensemble framework for sequential Recommendation (RLAERec), to effectively aggregate different base models for sequential recommendation. Specifically, we model the ensemble process as a Markov decision process (MDP) and design a dynamic weight allocation method according to constantly changing user behavior patterns and feedback. In addition, we propose an adaptive diversity measure for ranking ensemble learning, which can further enhance the ensemble performance. Extensive experiments on four real-world datasets demonstrate the effectiveness of the proposed model.