Effect of initial configuration on network-based recommendation

2007/11/15 by Tao Zhou, T. Zhou, L.-L. Jiang +5 · 4 citations
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Recommender Systems and Techniques #Stochastic Gradient Optimization Techniques #physics.soc-ph

paper · pdf · doi:10.1209/0295-5075/81/58004

published as EPL 81, 58004 (2008) · 4 pages and 3 figures

arxiv created 2007/11/15 · openalex publication_date 2008/02/13 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/04

Abstract

In this paper, based on a weighted object network, we propose a recommendation algorithm, which is sensitive to the configuration of initial resource distribution. Even under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the widely applied global ranking method and collaborative filtering. Furthermore, we introduce a free parameter β to regulate the initial configuration of resource. The numerical results indicate that decreasing the initial resource located on popular objects can further improve the algorithmic accuracy. More significantly, we argue that a better algorithm should simultaneously have higher accuracy and be more personal. According to a newly proposed measure about the degree of personalization, we demonstrate that a degree-dependent initial configuration can outperform the uniform case for both accuracy and personalization strength.

Citations

• Evaluating collaborative filtering recommender systems
• On power-law relationships of the Internet topology

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