item-based recommender systems
Item-based recommender systems are algorithms that use similarity metrics to recommend items to users based on their previous preferences and behaviors, allowing them to find and suggest items that are similar to those the user has liked or interacted with in the past.
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Similar Concepts
- attention-based recommendation systems
- context-aware recommender systems
- hybrid recommender systems
- item-based collaborative filtering
- item-based recommendations
- neural network-based recommender systems
- recommendation systems
- recommender system architectures
- recommender system evaluation
- recommender systems using transformers
- scalability of recommender systems
- serendipity in recommendation systems
- serendipity in recommender systems
- trust-based recommender systems
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