Effect of initial configuration on network-based recommendation

TL;DR

This paper introduces a network-based recommendation algorithm sensitive to initial resource distribution, showing improved accuracy and personalization by adjusting initial configurations, especially reducing resources on popular items.

Contribution

It proposes a novel initial resource configuration method with a tunable parameter, enhancing both accuracy and personalization over traditional algorithms.

Findings

Decreasing initial resources on popular objects improves accuracy.

Degree-dependent initial configuration outperforms uniform distribution.

Enhanced personalization achieved alongside higher accuracy.

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 $\beta$ 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.