Effect of initial configuration on network-based recommendation
Effect of initial configuration on network-based recommendation
复制标题
初始配置对基于网络的推荐的影响
DOI：
10.1209/0295-5075/81/58004
复制
发表时间：
2008-03-01
期刊：
影响因子：
1.8
通讯作者：
Zhang, Y. -C.
中科院分区：
文献类型：
--
作者：
Zhou, T.;Jiang, L. -L.;Zhang, Y. -C.
本文基于加权对象网络，提出了一种对初始资源分布配置敏感的推荐算法。即使在最简单的二进制资源情况下，该算法也比广泛应用的全局排序方法和协同过滤方法具有更高的准确性。此外，我们还引入了一个自由参数β来调节资源的初始配置。数值结果表明，减少流行对象上的初始资源可以进一步提高算法的准确性。更重要的是，我们认为一个更好的算法应该同时具有更高的准确性和更个性化。根据一个新提出的措施的程度的个性化，我们证明了度依赖的初始配置可以优于均匀的情况下，准确性和个性化的强度。
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.