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One approach to the design of recommender systems that has seen wide use is collaborative filtering.
Collaborative filtering methods are based on collecting and analyzing a large amount of information on users ’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users.
User-based collaborative filtering attempts to model the social process of asking a friend for a recommendation.
A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an " understanding " of the item itself.

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