In many situations, we are able to build different collaborative and content-based filtering models. What if we take account of all of them at the same time? In machine learning, the approach of combining different models usually leads to better results.

A simple example is collaborative filtering combined with information about users and/or items. In the case of IBCF, the distance between items can take account of user preferences and item descriptions at the same time. Even in UBCF, the distance between users can take account of their preferences and personal data.

In the case of recommendation, these models are called hybrids. There are different ways to combine filtering models.

Parallelized hybrid systems run the recommenders separately and combine their results. There are a few options such as the following ones: