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The large amount of information that is currently being collected (the so-called “big data”), have resulted in model-based Collaborative Filtering (CF) methods to encountering limitations, e.g., the sparsity problem and the scalability problem. It is difficult for model-based CF methods to address the scalability-performance trade-off. Therefore, we propose a scalable clustering-based CF method in this paper that can help provide a balance by re-locating elements in the cluster model. The proposed method is evaluated by performing a comparison against existing methods in terms of measurements for the Mean Absolute Error (MAE) and response time to assess the performance and scalability. The experimental results show that the proposed method improves the MAE and the response time by 50.79% and 48.25%, respectively. * This paper is significantly extended from an earlier version presented at the 3 International Conference on Smart Media Applications in December 2014. † These authors contributed equally to this work as the first author. ‡ Corresponding author. O-J. Lee et al. Adaptive Collaborative Filtering Based on Scalable Clustering for Recommender Systems – 180 –

Adaptive Collaborative Filtering Based on Scalable Clustering for Big Recommender Systems