Université de Fribourg

Accurate and diverse recommendations via eliminating redundant correlations

Zhou, Tao ; Su, Ri-Qi ; Liu, Run-Ran ; Jiang, Luo-Luo ; Wang, Bing-Hong ; Zhang, Yi-Cheng

In: New Journal of Physics, 2009, vol. 11, p. 123008

In this paper, based on a weighted projection of a bipartite user-object network, we introduce a personalized recommendation algorithm, called network-based inference (NBI), which has higher accuracy than the classical algorithm, namely collaborative filtering. In NBI, the correlation resulting from a specific attribute may be repeatedly counted in the cumulative recommendations from different...

Université de Fribourg

Information filtering based on transferring similarity

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

In: Physical Review E, 2009, vol. 80, no. 1, p. 017101

n this Brief Report, we propose an index of user similarity, namely, the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach...

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...