collaborative filtering
Collaborative filtering is a technique used in recommendation systems that analyzes user behavior and preferences to provide personalized recommendations, by finding patterns and similarities between different users' preferences and suggesting items they may like.
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Related Concepts (20)
- cold start problem
- collaborative filtering algorithms
- collaborative tagging
- collective intelligence
- content-based filtering
- context-aware collaborative filtering
- data sparsity in collaborative filtering
- hybrid recommender systems
- implicit feedback dataset
- implicit feedback in collaborative filtering
- item-based collaborative filtering
- matrix factorization
- neighborhood-based methods
- networked intelligence
- recommendation systems
- recommender systems
- serendipity in recommendation systems
- trust-based collaborative filtering
- user-based collaborative filtering
- wisdom of crowds
Similar Concepts
- adaptive filtering
- algorithms and filter bubbles
- collaboration
- collaborative consumption
- collaborative learning
- content filtering
- context-aware recommender systems
- implicit feedback-based content recommendation
- implicit feedback-based social recommendation
- item-based recommender systems
- neural network-based recommender systems
- serendipity in recommender systems
- similarity search
- social computing algorithms
- trust-based recommender systems