Novelty-driven recommendation by using integrated matrix factorization and temporal-aware clustering optimization.
D. R. Kumar RajaS. PushpaPublished in: Int. J. Commun. Syst. (2020)
Keyphrases
- matrix factorization
- collaborative filtering
- recommender systems
- item recommendation
- data sparsity
- nonnegative matrix factorization
- stochastic gradient descent
- cold start problem
- low rank
- simultaneous clustering
- latent factor models
- implicit feedback
- clustering algorithm
- negative matrix factorization
- clustering method
- tensor factorization
- factor analysis
- missing data
- k means
- variational bayesian
- personalized ranking
- rating prediction
- recommendation systems
- factorization methods
- probabilistic matrix factorization
- data clustering
- data points
- personalized recommendation
- search engine
- data matrix
- user ratings
- document clustering
- unsupervised learning
- least squares
- user profiles
- binary matrix
- high order