A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

Abstract

We propose an online hybrid recommender strategy named content-boosted collaborative filtering with dynamic fuzzy clustering ($\mathrm{{CBCF}}_\mathrm{{dfc}}$) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCF$_\mathrm{{dfc}}$ combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. $\mathrm{{CBCF}}_\mathrm{{dfc}}$ uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare $\mathrm{{CBCF}}_\mathrm{{dfc}}$ 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 ($\mathrm{{CBCF}}_\mathrm{{onl}})$ according to online recommendation time. Test results showed that $\mathrm{{CBCF}}_\mathrm{{dfc}}$ 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.