The insights from disentangling ID and modality effects can be leveraged to develop more general recommendation frameworks by incorporating a broader range of user behaviors and preferences. By understanding the distinct roles of item co-occurrence patterns and fine-grained preferences in user interactions, recommendation systems can be designed to adapt to various user contexts and preferences. For example, the disentanglement techniques can be used to create hybrid recommendation models that combine collaborative filtering with content-based approaches, leveraging both user-item interactions and item features for more accurate recommendations. Additionally, the insights from disentangling ID and modality effects can inform the development of explainable recommendation systems that provide transparent and interpretable recommendations to users, enhancing user trust and satisfaction with the recommendation process.