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Article summary:

1. Collaborative filtering (CF) is a successful approach to building recommender systems that uses known preferences of a group of users to make recommendations for other users.

2. CF techniques can be categorized into memory-based, model-based, and hybrid CF algorithms, each with their own predictive performance and ability to address challenges such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, and privacy protection.

3. Model-based CF approaches use rating data to estimate or learn a model for making predictions, while memory-based CF techniques calculate similarity values based on common items. Both approaches have limitations and trade-offs in terms of reliability and prediction performance.

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