They don't adapt to new patterns until trained again, and they need to be carefully tuned by a human. In a sense, they are a restricted type of neural network, with the input data in a regular form. They can predict for thousands of data points simultaneously. And they don't need to be tuned. This makes collaborative filters ideal for realtime personalization applications.
Bayesian networks build a directed graph of conditional probabilities.
As a visitor provides more information about himself or herself, a Bayesian network adjusts the probabilities of each possible end result. This allows a Web system to accelerate the visitor's experience by bringing the most likely things to the visitor's attention as soon as possible. Bayesian networks are most appropriate to help satisfy short-term visitor goals, such as answering customer support questions, diagnosing problems, or selecting an appliance. However, training a Bayesian network is often extremely slow.