recommender systems
Recommender systems are algorithms or software tools that analyze a user's preferences and provide personalized recommendations or suggestions for items or content that the user might be interested in.
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Related Concepts (24)
- association rule mining
- cold-start problem
- collaborative filtering
- content-based filtering
- context-aware recommender systems
- convergent instrumental goals
- data mining
- decision-making algorithms
- diversity of recommendations
- hybrid recommender systems
- implicit feedback
- item-based recommender systems
- latent factor models
- matrix factorization techniques
- neural network-based recommender systems
- personalized recommendations
- privacy concerns in recommender systems
- recommender system architectures
- recommender system evaluation
- scalability of recommender systems
- self-attention
- serendipity in recommender systems
- trust-based recommender systems
- user-item interaction data
Similar Concepts
- attention-based recommendation systems
- collaborative filtering algorithms
- ethical issues in recommender systems and personalization algorithms
- expert systems
- feedback systems
- implicit feedback recommendation algorithms
- implicit feedback-based content recommendation
- implicit feedback-based personalized systems
- implicit feedback-based social recommendation
- item-based recommendations
- recommendation systems
- recommender systems using transformers
- rule-based systems
- serendipity in recommendation systems
- user-based collaborative filtering