12 Facts About Content-based filtering

Collaborative Content-based filtering approaches build a model from a user's past behavior as well as similar decisions made by other users.

Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties.

We can demonstrate the differences between collaborative and content-based filtering by comparing two early music recommender systems – Last.

Content-based filtering looked for a way to recommend a user a book he might like.

Content-based filtering's idea was to create a system that asks the user specific questions and assigns him stereotypes depending on his answers.

Collaborative Content-based filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.

Key advantage of the collaborative Content-based 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.

One of the most famous examples of collaborative Content-based filtering is item-to-item collaborative Content-based filtering, an algorithm popularized by Amazon.

Many social networks originally used collaborative Content-based filtering to recommend new friends, groups, and other social connections by examining the network of connections between a user and their friends.

Content-based filtering methods are based on a description of the item and a profile of the user's preferences.

Content-based filtering recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.

Key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content source and use them across other content types.