user-based collaborative filtering
User-based collaborative filtering is a recommendation technique that analyzes the preferences and behaviors of similar users to provide personalized suggestions or recommendations. It involves comparing the past interactions and choices of a user with those of other users to identify patterns and similarities, enabling the system to recommend items or content that the user might be interested in based on the preferences of like-minded users.
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Related Concepts (1)
Similar Concepts
- collaborative filtering algorithms
- content-based filtering
- context-aware collaborative filtering
- context-aware recommender systems
- hybrid recommender systems
- implicit feedback in collaborative filtering
- implicit feedback-based content recommendation
- implicit feedback-based social recommendation
- item-based collaborative filtering
- item-based recommender systems
- neural network-based recommender systems
- serendipity in recommender systems
- social computing algorithms
- trust-based collaborative filtering
- trust-based recommender systems