Dimension Reduction Based on Effects of Experienced Users in Recommender Systems.
Bo ChenXiaoqian LuJian HePublished in: CSPS (2) (2018)
Keyphrases
- dimension reduction
- recommender systems
- collaborative filtering
- principal component analysis
- high dimensional data
- low dimensional
- user profiles
- information overload
- random projections
- high dimensional
- high dimensional problems
- variable selection
- manifold learning
- feature extraction
- linear discriminant analysis
- user model
- data mining and machine learning
- high dimensionality
- cold start problem
- dimensionality reduction
- active user
- partial least squares
- implicit feedback
- recommendation quality
- recommendation systems
- singular value decomposition
- product recommendation
- matrix factorization
- feature selection
- user ratings
- discriminative information
- cluster analysis
- user preferences
- high dimensional data analysis
- discriminant analysis
- real world
- unsupervised learning
- data points
- computer vision
- data mining