Highly accurate recommendation algorithm based on high-order similarities
arXiv:0808.3726 · doi:10.1016/j.physa.2009.10.027
Abstract
In this Letter, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the standard Pearson coefficient, the user-user similarities are obtained by a diffusion process. Furthermore, by considering the second order similarities, we design an effective algorithm that depresses the influence of mainstream preferences. The corresponding algorithmic accuracy, measured by the ranking score, is further improved by 24.9% in the optimal case. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that the algorithm based on second order similarity can outperform the MCF simultaneously in all three criteria.
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- Degree correlation effect of bipartite network on personalized recommendation
- Ultra accurate collaborative information filtering via directed user similarity