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Department of Justice gather and analyze data from a variety of sources to gain a more complete understanding of school violence.

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

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

This approach has its roots in information retrieval and information filtering research.

To abstract the features of the items in the system, an item presentation algorithm is applied.

One of the most famous examples of collaborative filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by's recommender system.

In a content-based recommender system, keywords are used to describe the items and a user profile is built to indicate the type of item this user likes.A widely used algorithm is the tf–idf representation (also called vector space representation). A history of the user's interaction with the recommender system.To create a user profile, the system mostly focuses on two types of information: 1. Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system.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.Many algorithms have been used in measuring user similarity or item similarity in recommender systems.In the above example, requires a large amount of information about a user to make accurate recommendations.

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