Post-fusion Monolithic Hybrid Recommender System for Suggesting Relevant Movies to Users

Introduction: Recommendation systems are essential tools for facilitating user access to information and addressing the challenge of information overload. They operate by analyzing historical behaviors to learn user preferences. This study explores a hybrid recommendation approach that combines collaborative filtering methods: analyzing sequences of watched movies and leveraging movie ratings. The goal is to enhance recommendation accuracy by dynamically adjusting these methods based on specific use cases. Methods: The proposed method uses a weighted post-fusion technique to combine the two collaborative filtering approaches. For instance, potentially higher weights are assigned to the rating matrix when the majority of data includes ratings, while sequential data receives more emphasis when ratings are scarce. objective: to give vaid recommendations to users based on comprehensive datasets. Results: Technique could integrate sequential data with ratings effectively, capturing both short-term and long-term user preferences. The approach addresses limitations of standalone models, such as insufficient long-term preferences or a lack of rating considerations. method: various weights would be set based on use cases. For instance, in cases where we have the rating for the majority of classes, we will assign a higher weight to the rating matrix and in case where the rating is unavailable for the majority of cases, the higher weights might be assigned to the sequential dataset. Discussion: Integrating heterogeneous data sources within a unified recommendation framework leads to richer, more adaptive user modeling. The dynamic fusion approach not only personalizes results but also aligns recommendations with evolving user patterns. Future work may include learning adaptive weights automatically through meta-learning, incorporating metadata like genres or timestamps, and scaling to real-world datasets with millions of users and items. Conclusion: This hybrid methodology highlights the importance of combining multiple data sources to create more personalized and effective recommendations. The study provides a framework for adapting weights to align with varied use cases, paving the way for advancements in hybrid recommendation systems. conclusion: na