Hybrid recommendation systems
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:
Weighted: The CBF and CF predicted ratings are combined in to some weighted mean.
Mixed: CF and CBF predicted movies are found separately and then merged in to a single list.
Switched: Based on certain criteria, the CF predictions or CBF predictions are used.
Feature combination: CF and CBF features are considered together to find the most similar users or items.
Feature augmentation: Similar to feature combination, but the additional features are used to predict some ratings and then the main recommender uses these ratings to produce the recommendation list. For example, Content-Boosted Collaborative Filtering learns the ratings of unrated movies by a content-based model and then a collaborative approach is employed to define the recommendations.
As an example, we implement...
