Recommender Systems
Recommender systems are a subclass of information filtering systems that predict the preferences or ratings that a user would give to a product. These systems are widely used in e-commerce, social media, and entertainment industries to provide personalized recommendations to users. Recommender systems use various techniques such as collaborative filtering, content-based filtering, and hybrid filtering to generate recommendations. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, while content-based filtering recommends items similar to those that a user has liked in the past. Hybrid filtering combines both techniques to provide more accurate recommendations. Recommender systems are evaluated using metrics such as precision, recall, and F1 score.
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