This is a class of methods that combine both CBF and CF in a single recommender to achieve better results. Several approaches have been tried and can be summarized in the following categories:
As an example, we implement two hybrid feature combination methods merging an item's features CBF method with a user-based CF method. The first method employs a user-based CF to the expanded utility matrix that now also contains the average rating per genre per user. The Python class is as follows:
The constructor generates the expanded utility matrix with the movies' genres average rating features associated to each user, Umatrix_mfeats
. The function CalcRatings
finds the K-NN using the Pearson correlation comparing the expanded feature vectors of the users. The second method applies and SVD factorization to the expanded utility matrix that contains the genre preferences for each user.
As the SVD method, the ratings are subtracted with the user rating's average, and genre preferences are subtracted from the same user rating's average.
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