Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems.
Jiufang ChenYe YuanTao RuanJia ChenXin LuoPublished in: Neurocomputing (2021)
Keyphrases
- sparse data
- factor analysis
- recommender systems
- high dimensional
- hyperparameters
- matrix factorization
- latent factors
- model selection
- collaborative filtering
- parameter space
- cross validation
- random sampling
- bayesian framework
- closed form
- bayesian inference
- prior information
- independent component analysis
- sample size
- low dimensional
- support vector
- em algorithm
- discriminant analysis
- latent variables
- maximum likelihood
- statistical tests
- data points
- high dimensionality
- dimensionality reduction
- maximum a posteriori
- similarity search
- cluster analysis
- multi dimensional
- nearest neighbor
- feature space
- incomplete data
- implicit feedback
- databases
- machine learning
- data sets
- missing data
- high dimensional data
- generative model
- prior knowledge
- training set
- computer vision