Hybrid Recommendation

Hybrid recommendation systems aim to improve the accuracy and effectiveness of personalized recommendations by combining collaborative filtering, which leverages user-item interactions, with content-based methods that utilize item features like text descriptions or metadata. Current research emphasizes integrating advanced techniques such as language models (e.g., BERT, mT5) and graph neural networks with collaborative filtering, often within hybrid architectures designed to balance the conflicting goals of recommending popular items and introducing new, less-known ones. This approach addresses limitations of traditional methods, such as the cold-start problem, and has demonstrably improved recommendation performance in various applications, including e-commerce, financial services, and research paper discovery.