Other approaches to recommendation systems
In this chapter, we concentrated our efforts on building recommendation systems by following the collaborative filtering paradigm. This is a very popular approach for its many advantages. By essentially mimicking word-of-mouth recommendations, it requires virtually no knowledge about the items being recommended nor any background about the users in question.
Moreover, collaborative filtering systems incorporate new ratings as they arise, either through a memory approach, or via regular retraining of a model-based approach. Thus, they naturally become better for their users over time as they learn more information and adapt to changing preferences. On the other hand, they are not without their disadvantages, not least of which is the fact that they will not take into account any information about the items and their content even when it is available.
Content-based recommendation systems try to suggest items to users that are similar to those that users...
