What is it about?
Recommendation systems are used to assist the users in selecting an item of interest. Item can be a movie to watch, music to listen to, product to purchase, or news article to read. Content information plays the main role in identifying the interest of the users. So content information along with ratings are used to improve the performance of the recommendation system.
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Why is it important?
Content information is used to boost the user profile to give a better-personalized recommendation. Pessimistic users are also benefitted from good recommendations.
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This page is a summary of: Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems, Intelligent Data Analysis, December 2020, IOS Press,
DOI: 10.3233/ida-205244.
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