How do hybrid recommender systems work?

Hybrid recommender systems work by combining multiple recommendation techniques or approaches to provide more accurate and diverse recommendations. These systems leverage the strengths of different recommendation methods, such as collaborative filtering, content-based filtering, and knowledge-based filtering, to overcome their individual limitations. The hybrid approach can be achieved through various methods, including weighted combination, switching, and cascade. Weighted combination involves assigning weights to different recommendation techniques and combining their outputs. Switching involves selecting the most appropriate recommendation technique based on certain conditions or user preferences. Cascade involves using one recommendation technique to pre-filter the items and then applying another technique to refine the recommendations. By integrating multiple approaches, hybrid recommender systems aim to enhance recommendation quality, overcome data sparsity, handle cold-start problems, and provide more personalized and accurate recommendations to users.