Combining content and ratings in hybrid recommendation engines
Instead of seeing rating-based recommenders as a successor to content-based recommenders, you should consider them as a different recommender after having acquired enough user-item interaction data to provide rating-only recommendations. In most practical cases, a recommendation engine will exist for both approaches—either as two distinct algorithms or as a single hybrid model. In this section, we will look into training such a hybrid model.
Building a state-of-the-art recommender using the Matchbox Recommender
To build a state-of-the-art recommender using the Matchbox Recommender, we open Azure Machine Learning Designer, and add the building blocks for the Matchbox Recommender to the canvas as shown in Figure 11.9. As we can see, the recommender can now take ratings, and user and item features, as inputs to create a hybrid recommendation model:
