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In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended.

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

The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of techniques.

Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.

A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item.

Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

Public health professionals have been studying recommender systems to personalize health education and preventative strategies.

As previously detailed, Pandora Radio is a popular example of a content-based recommender system that plays music with similar characteristics to that of a song provided by the user as an initial seed.Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise.Of note, recommender systems are often implemented using search engines indexing non-traditional data.When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended.For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc.

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