The proposed disentanglement techniques can be extended to other recommendation scenarios beyond session-based settings by adapting the framework to handle different types of user interactions and preferences. For example, in traditional collaborative filtering recommendation systems, where user-item interactions are the primary source of information, the disentanglement approach can be applied to separate the effects of user preferences and item characteristics. By disentangling these factors, the recommendation system can provide more personalized and accurate recommendations based on the underlying reasons for user choices. Additionally, in context-aware recommendation systems, where contextual information such as time, location, and device type influences user preferences, the disentanglement techniques can help in isolating the impact of different contextual factors on recommendations. This can lead to more effective and contextually relevant recommendations for users in diverse scenarios.