On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets.
Dionisis MargarisCostas VassilakisDimitris SpiliotopoulosPublished in: Inf. (2022)
Keyphrases
- compressive sensing
- collaborative filtering
- user ratings
- recommender systems
- collaborative filtering recommender systems
- sparse representation
- matrix factorization
- high quality
- user preferences
- recommendation algorithms
- data sets
- high accuracy
- data sparsity
- personalized recommendation
- netflix prize
- highly accurate
- recommendation systems
- computationally efficient
- prediction accuracy
- transfer learning
- probabilistic matrix factorization
- user profiles
- tensor factorization
- collaborative filtering algorithms
- latent factor models
- recommendation quality
- rating prediction
- user item rating
- pearson correlation coefficient
- confidence levels
- active user
- accurate classifiers
- cold start
- uci machine learning repository
- missing data