CONTENT-BOOSTED COLLABORATIVE FILTERING FOR RATING PREDICTION USING ARTIFICIAL NEURAL NETWORK - Rizki Hasanah, Nurlaili
Recommender system is a popular technology that answers the need of finding relevant information immediately. Collaborative filtering as one of the methods in recommender system has been widely used because of its various advantages. Still, this method has main problem related to data sparsity which causes lack of accuracy in the recommendation results. Content-boosted Collaborative Filtering (CBCF) which incorporates content-based prediction in the recommendation process is one hybrid approach that has been developed previously. Modifying this approach by utilizing Artificial Neural Network can be an effective alternative because of its ability to properly model relationships between features.