To extend the design of EasyRL4Rec to support multi-agent or hierarchical RL approaches in recommender systems, the following modifications and enhancements can be considered:
Multi-Agent Environment: Introduce a framework within EasyRL4Rec that allows for the creation of multi-agent environments where multiple agents interact with each other and the environment. This would enable the development of collaborative filtering and group recommendation systems.
Hierarchical RL Modules: Incorporate hierarchical RL modules into EasyRL4Rec to support the modeling of complex decision-making processes with multiple levels of abstraction. This would enable the implementation of hierarchical recommendation strategies that consider both short-term and long-term user preferences.
Communication Protocols: Implement communication protocols between agents in EasyRL4Rec to facilitate information sharing and coordination in multi-agent systems. This would enable agents to exchange recommendations, feedback, and insights to improve overall system performance.
Policy Fusion Techniques: Develop policy fusion techniques within EasyRL4Rec to combine the recommendations generated by multiple agents or levels of hierarchy. This would allow for the integration of diverse recommendation strategies to enhance the overall quality of recommendations.
Evaluation Metrics: Extend the evaluation metrics in EasyRL4Rec to assess the performance of multi-agent and hierarchical RL approaches in recommender systems. This would involve defining new metrics that capture the collaborative and hierarchical aspects of the system.
Scalability and Efficiency: Ensure that the design extensions for multi-agent and hierarchical RL in EasyRL4Rec are scalable and efficient, capable of handling large-scale recommender systems with multiple agents and complex decision-making processes.
By incorporating these design extensions, EasyRL4Rec can provide a comprehensive framework for developing and evaluating multi-agent and hierarchical RL approaches in recommender systems, enabling researchers and practitioners to explore advanced recommendation strategies that leverage collaborative and hierarchical decision-making paradigms.