Improved collaborative filtering algorithm via information transformation

Liu, Jianguo, Wang, Bing-Hong and Guo, Qiang (2009) Improved collaborative filtering algorithm via information transformation. International Journal of Modern Physics C, 20 (2). pp. 285-293.


In this paper, we propose a spreading activation approach for collaborative filtering (SACF). By using the opinion spreading process, the similarity between any users can be
obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-N similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.

Item Type: Article
Keywords: Recommendation systems; Bipartite network; Collaborative filtering
Subject(s): Complexity
Centre: CABDyN Complexity Centre
Date Deposited: 25 Feb 2012 17:20
Last Modified: 23 Oct 2015 14:07

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