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Université de Fribourg

Improved collaborative filtering algorithm via information transformation

Liu, Jian-Guo ; Wang, Bing-Hong ; Guo, Qiang

In: International Journal of Modern Physics C, 2009, vol. 20, no. 2, p. 285-293

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). 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...

Université de Fribourg

Effects of user's tastes on personalized recommendation

Liu, Jian-Guo ; Zhou, Tao ; Wang, Bing-Hong ; Zhang, Yi-Cheng ; Guo, Qiang

In: International Journal of Modern Physics C, 2009, vol. 20, no. 12, p. 1925-1932

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their...

Université de Fribourg

Personal recommendation via modified collaborative filtering

Liu, Run-Ran ; Jia, Chun-Xiao ; Zhou, Tao ; Sun, Duo ; Wang, Bing-Hong

In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 4, p. 462-468

In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard cosine similarity, we take into account the influence of a node’s degree. Substituting this new definition of similarity for the standard cosine similarity, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we...