To extend the proposed methods to handle more complex neighborhood structures, such as hierarchical or dynamic neighborhoods, several modifications and enhancements can be considered.
Hierarchical Neighborhoods:
Introduce a multi-level treatment representation approach that can capture hierarchical relationships within the neighborhood structure. This can involve encoding different levels of neighbors and their interactions to provide a more comprehensive representation of the neighborhood effect.
Develop a mechanism to dynamically adjust the treatment representation based on the hierarchical relationships within the neighborhood. This adaptive approach can help in capturing the varying influences of different levels of neighbors on the target user-item pair.
Dynamic Neighborhoods:
Implement a dynamic treatment representation that can adapt to changes in the neighborhood structure over time. This can involve incorporating temporal information to capture the evolving relationships between users and items in the neighborhood.
Utilize reinforcement learning techniques to learn the optimal treatment representation in real-time, considering the dynamic nature of the neighborhood effect. This adaptive learning approach can enhance the model's ability to handle changing neighborhood dynamics.
By incorporating these strategies, the proposed methods can be extended to effectively handle more complex neighborhood structures, providing a more accurate representation of the neighborhood effect in recommender systems.