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Website Recommendation systems is a relatively new area of research in machine learning. There are two main ways that Website recommendation systems produce a list of recommendations for a user – collaborative or content-based filtering. Collaborative filtering uses past behavior (items that a user previously viewed or purchased, in addition to any ratings the user gave those items) and similar decisions made by other users to create a model. This model then predicts items that the user may find interesting. In content-based filtering the model uses a series of discrete characteristics of an item in order to recommend additional items with similar properties. One of the benefits of website recommendation systems over search algorithms is that website recommendation systems help users discover items that they might not have otherwise found. Website recommendation systems is an active research area in data mining and machine learning.
