Recommendation Systems
Recommendation systems are a subclass of information filtering systems that predict the preferences or ratings that a user would give to a product or service. These systems are widely used in e-commerce, social media, and entertainment industries to provide personalized recommendations to users. Recommendation 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. Recommendation systems are evaluated using metrics such as precision, recall, and F1 score, and their performance can be improved using techniques such as matrix factorization, deep learning, and reinforcement learning.
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