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R Data Analysis Cookbook - Second Edition by Kuntal Ganguly

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How it works...

Hybrid recommender systems combine various recommender systems and replace the disadvantages of one system with the advantages of another system to build a more robust final 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.

In the previous example, we mixed similar user-liked movies (UBCF) with Random recommendation for diversity. The weights attribute of the HybridRecommender() function refers to the priority of various selected models.

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