Dimensionality Reduction via Self-Organizing Feature Maps for Collaborative Filtering.
Andrew R. PariserWillard L. MirankerPublished in: IJCNN (2007)
Keyphrases
- collaborative filtering
- dimensionality reduction
- recommender systems
- high dimensional
- matrix factorization
- high dimensionality
- principal component analysis
- feature extraction
- high dimensional data
- data sparsity
- low dimensional
- feature space
- dimensionality reduction methods
- data points
- data representation
- recommendation systems
- structure preserving
- linear discriminant analysis
- pattern recognition
- random projections
- manifold learning
- user preferences
- nonlinear dimensionality reduction
- feature selection
- principal components
- user profiles
- deal with information overload
- probabilistic matrix factorization
- cold start problem
- personalized recommendation
- singular value decomposition
- user ratings
- collaborative filtering algorithms
- linear dimensionality reduction
- neighbor selection
- pattern recognition and machine learning
- metric learning
- kernel learning
- kernel pca
- transfer learning
- cold start
- user interests
- computer vision
- netflix prize
- information filtering