Machine Learning for the Web

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 ...