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Collaborative Filtering
Methods based on similarities between users or items to generate recommendations
Content-Based Filtering
Systems recommending items similar to those the user has liked in the past
Matrix Factorization
Algebraic techniques that decompose the user-item matrix to discover latent factors
Hybrid Systems
Approaches combining multiple recommendation methods to improve performance
Deep Learning for Recommendations
Using deep neural networks to capture complex relationships in data
Sequential Recommendation
Models considering the temporal order of interactions to predict next interests
Contextual Bandits
Reinforcement learning algorithms optimizing real-time recommendations
Graph-Based Recommendation
Using graph structures to model relationships between users and items
Explainable Recommendation Systems
Approaches providing understandable justifications for each generated recommendation
Evaluation of Recommendation Systems
Metrics and methodologies for measuring the effectiveness and relevance of recommendations
Multi-criteria Recommendation
Systems considering multiple dimensions of user preference for personalized recommendations
Real-Time Recommendation
Infrastructure and algorithms adapted to generate instant recommendations