The Impact of Basic Matrix Factorization Refinements on Recommendation Accuracy.
Parisa LakBora ÇaglayanAyse Basar BenerPublished in: BDC (2014)
Keyphrases
- matrix factorization
- collaborative filtering
- recommender systems
- item recommendation
- data sparsity
- low rank
- latent factor models
- cold start problem
- recommendation algorithms
- factor analysis
- nonnegative matrix factorization
- missing data
- negative matrix factorization
- tensor factorization
- recommendation systems
- personalized ranking
- user profiles
- variational bayesian
- user preferences
- probabilistic matrix factorization
- prediction accuracy
- stochastic gradient descent
- data matrix
- implicit feedback
- personalized recommendation
- data sets
- cold start
- latent variables
- data representation