Predictive Clustering for Performance Stability in Collaborative Filtering Techniques

Abstract

Model-based collaborative filtering improves the fundamental limitations of the collaborative filtering facing the issues of data sparsity and scalability while presenting other constraints of high costs of model building and the tradeoff between performance and scalability. Such tradeoff results in reduced coverage, which is one sort of the sparsity issue. Furthermore, high model building costs lead to unstable performance driven by cumulative changes in the domain environment. To solve these problems, we propose Predictive Clustering-based CF (PCCF) that incorporates the Markov model and fuzzy clustering with Clustering based CF (CBCF). The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage is also improved by expanding the coverage based on transition probabilities. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. In comparison with the existing techniques, the suggested method shows slight performance improvement. Notwithstanding, it is more advanced than the existing techniques in terms of the range that indicates the level of performance fluctuation. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques.