Triplet losses-based matrix factorization for robust recommendations.
Flavio GiobergiaPublished in: CIKM Workshops (2022)
Keyphrases
- matrix factorization
- recommender systems
- collaborative filtering
- cold start problem
- low rank matrix
- item recommendation
- low rank
- data sparsity
- nonnegative matrix factorization
- variational bayesian
- missing data
- stochastic gradient descent
- data matrix
- factorization methods
- user ratings
- latent factor models
- negative matrix factorization
- multiscale
- latent factors
- tensor factorization
- user feedback
- user preferences
- personalized ranking