With content-based filtering approaches, a series of discrete characteristics of an item are utilized to recommend additional items with similar properties. Sometimes, it is based on a description of the item and a profile of the user's preferences. These approaches try to recommend items that are similar to those that a user liked in the past, or that are currently being used.
A key issue with content-based filtering is whether the system is able to learn user preferences from their actions regarding one content source and use them with other content types. When this type of RE is deployed, it can then be used to predict items or ratings for items that the user may have an interest in.