A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

Gurcan, Fatih
Birtürk, Ayşe Nur
We propose an online hybrid recommender strategy named content-boosted collaborative filtering with dynamic fuzzy clustering (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with pure content-based filtering (PCB), pure collaborative filtering (PCF) and content-boosted collaborative filtering (CBCF) according to prediction accuracy metrics, and with online CBCF without clustering (CBCFonl) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We also evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The user evaluation results are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.