A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering

  • Fatih Gurcan
  • Aysenur Akyuz Birturk
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 363)


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.


Recommender systems Hybrid online movie recommendation 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.METU Department of Computer EngineeringAnkaraTurkey

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