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Each type of system has its own strengths and weaknesses.

In the above example, requires a large amount of information on a user in order to make accurate recommendations.

This is an example of the cold start problem, and is common in collaborative filtering systems.

While Pandora needs very little information to get started, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed).

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This model is then used to predict items (or ratings for items) that the user may have an interest in.

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These approaches are often combined (see Hybrid Recommender Systems).

The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems – and Pandora Radio.