We build hybrid recommender systems by combining various recommender systems to build a more robust system. By combining various recommender systems, we can eliminate the disadvantages of one system with the advantages of another system and thus build a more robust system. For example, by combining collaborative filtering methods, where the model fails when new items don't have ratings, with content-based systems, where feature information about the items is available, new items can be recommended more accurately and efficiently.
Before building a hybrid model, we consider the following questions:
What techniques should be combined to achieve the business solution?
How should we combine various techniques and their results for better predictions?
