In: International Journal of Modern Physics C, 2010, vol. 21, no. 1, p. 137-147
In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is...
|
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...
|
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...
|
