HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs.
Chengming ZhangShaden SmithBaixi SunJiannan TianJonathan SoiferXiaodong YuShuaiwen Leon SongYuxiong HeDingwen TaoPublished in: CoRR (2023)
Keyphrases
- highly efficient
- collaborative filtering
- recommender systems
- matrix factorization
- recommendation systems
- user preferences
- personalized recommendation
- data sparsity
- low latency
- recommendation quality
- product recommendation
- cold start problem
- recommendation algorithms
- low complexity
- cold start
- low cost
- user ratings
- transfer learning
- content based filtering
- web page recommendation
- training set
- collaborative filtering recommendation
- hybrid recommendation
- user profiles
- tensor factorization
- latent factor models
- item recommendation