Improving collaborative filtering's rating prediction accuracy by considering users' dynamic rating variability.
Dionisis MargarisCostas VassilakisPublished in: Int. J. Big Data Intell. (2020)
Keyphrases
- user ratings
- prediction accuracy
- collaborative filtering
- recommender systems
- rating prediction
- predictive accuracy
- ensemble methods
- predictive power
- data sparsity
- improve the prediction accuracy
- collaborative filtering algorithms
- cold start
- collaborative filtering recommender systems
- active user
- deal with information overload
- recommendation quality
- recommendation systems
- matrix factorization
- content based filtering
- web page prediction
- decision trees
- netflix prize
- personalized recommendation
- machine learning
- user profiles
- user interface
- feature selection