BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems.
Ruixin GuoFeng ZhangLizhe WangWusheng ZhangXinya LeiRajiv RanjanAlbert Y. ZomayaPublished in: IEEE Trans. Computers (2021)
Keyphrases
- image classification
- matrix factorization
- load balance
- recommender systems
- load balancing
- collaborative filtering
- computing resources
- round robin
- scheduling algorithm
- low rank
- factorization methods
- nonnegative matrix factorization
- negative matrix factorization
- factor analysis
- cold start problem
- data sparsity
- probabilistic matrix factorization
- recommendation systems
- user preferences
- latent factors
- implicit feedback
- missing data
- limited resources
- tensor factorization
- item recommendation
- data sets
- user ratings
- distributed systems
- response time
- latent factor models
- np hard