item-based collaborative filtering
Item-based collaborative filtering is a recommendation technique that analyzes the similarities between items based on user behavior to provide personalized recommendations. It suggests new items to users by identifying items that are similar to the ones they have already shown interest in, rather than analyzing similarities between users.
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Similar Concepts
- collaborative filtering algorithms
- content-based filtering
- context-aware collaborative filtering
- context-aware recommender systems
- data sparsity in collaborative filtering
- hybrid recommender systems
- implicit feedback in collaborative filtering
- implicit feedback-based content recommendation
- implicit feedback-based social recommendation
- item-based recommendations
- item-based recommender systems
- neural network-based recommender systems
- trust-based collaborative filtering
- trust-based recommender systems
- user-based collaborative filtering