What are the pros and cons of using a supervised learning approach for building a recommender system compared to traditional approaches? Pretty much all articles I read about recommender systems use Collaborative Filtering, Content Based Filtering and Hybrid approaches. Virtually no one mentioned about supervised learning approaches. We can use ratings/clicks as the target variable and user attributes and item attributes as features to build a classification/regression model and use the predictions from the model to do recommendations. Are there any particular reasons for such an approach being ignored in the recommender system community?

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*First of all, supervised learning is commonly used in recommender systems. One of the most popular examples of recommender system model is matrix factorization, where we predict ratings of items by the users (labels!).

*Basic models like matrix factorization are often extended by including additional features, such models do not differ very much from any other supervised machine learning model.

*Standard machine learning models are sometimes used for this purpose, but they often underperform as compared to the models designed for this purpose.

