A supervised active learning framework for recommender systems based on decision trees.
Rasoul KarimiAlexandros NanopoulosLars Schmidt-ThiemePublished in: User Model. User Adapt. Interact. (2015)
Keyphrases
- recommender systems
- decision trees
- active learning framework
- active learning
- learning algorithm
- collaborative filtering
- constructive induction
- supervised learning
- machine learning
- predictive accuracy
- decision tree induction
- naive bayes
- hierarchical text classification
- user modeling
- user model
- matrix factorization
- random forest
- machine learning algorithms
- training data
- feature construction
- semi supervised
- cold start problem
- decision tree algorithm
- user profiling
- implicit feedback
- rule induction
- unsupervised learning
- information overload
- decision tree learning
- classification rules
- decision rules
- data sets
- classification trees
- cold start
- product recommendation
- trust aware
- attribute selection
- weakly supervised
- supervised classification
- user interests
- user preferences
- user profiles
- recommendation quality
- feature selection
- user behavior