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Topics Contents
Collaborative Filtering Algorithms for a user-item recommendation based on user behavior
Content-Based Filtering Techniques using item features and user preferences
Hybrid Recommender Systems Combination of collaborative and content-based approaches
Matrix Factorization Dimensionality reduction methods for improved performance
Feature Engineering Enhancing recommendation models with relevant data features
Evaluation Methods to assess the effectiveness of recommender systems
Bias Dealing with biases in data that can impact recommendations
Cold Start Addressing challenges when new users or items join the system

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