Future Work

Incorporating dynamic and contextual information - Many current group recommender systems rely on static information, such as user preferences or historical data. However, incorporating dynamic and contextual information, such as real-time feedback, social interactions, or environmental factors, could lead to more personalized and effective recommendations.

Exploring new recommendation techniques - Current group recommender systems rely on collaborative filtering or content-based approaches. Future work could explore new recommendation techniques, such as knowledge-based or hybrid approaches, that could better handle complex group dynamics and preferences.

Adapting to different group sizes structures - Group recommender systems need to handle different group structures, as well as changes in group membership over time. Future work could investigate ways to adapt the recommendation process to these different contexts and to ensure that the system remains effective and scalable.