Information filtering via preferential diffusion

Details Information filtering via preferential diffusion Information filtering via preferential diffusion

2011-02-27

arxiv.org/abs/1102.5499v1

Physical Review E 83, 066119 (2011)

Physics

Data Analysis, Statistics and Probability

12 pages, 10 figures, 2 tables

Scientific paper

10.1103/PhysRevE.83.066119

Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.

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