Personal Recommendation via Modified Collaborative Filtering
arXiv:0801.1333 · doi:10.1016/j.physa.2008.10.010
Abstract
In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard Person correlation, we take into account the influence of node's degree. Substituting this new definition of similarity for the standard Person correlation, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.
7 pages, 8 figures and 1 table
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Cited by in corpus (8)
- Empirical analysis of web-based user-object bipartite networks
- Ultra accurate personalized recommendation via eliminating redundant correlations
- Collaborative filtering with diffusion-based similarity on tripartite graphs
- Information filtering based on transferring similarity
- A vertex similarity index for better personalized recommendation
- Effect of user tastes on personalized recommendation
- A trust-based recommendation method using network diffusion processes
- Collaborative filtering based on multi-channel diffusion