Highly Scalable Parallel Algorithms for Sparse Matrix Factorization.
Anshul GuptaGeorge KarypisVipin KumarPublished in: IEEE Trans. Parallel Distributed Syst. (1997)
Keyphrases
- matrix factorization
- parallel algorithm
- highly scalable
- tensor factorization
- collaborative filtering
- low rank matrix
- sparse non negative
- latent factors
- low rank
- missing data
- recommender systems
- parallel programming
- negative matrix factorization
- nonnegative matrix factorization
- data partitioning
- parallel computation
- shared memory
- stochastic gradient descent
- factorization methods
- data sparsity
- sparsity constraints
- parallel version
- factor analysis
- high dimensional
- cluster of workstations
- probabilistic model
- parallel implementations
- binary matrix
- processor array
- data matrix
- sparse representation
- sparse coding
- factorization method
- item recommendation
- personalized ranking
- probabilistic matrix factorization
- search algorithm