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arXiv 1012.3793cs.IRphysics.data-an
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一种抗垃圾邮件的鲁棒性排序算法

A robust ranking algorithm to spamming

Yanbo Zhou, Ting Lei, Tao Zhou

摘要:

网络评分系统中的评分问题引起了人们的广泛关注。一个好的排名算法应该对垃圾邮件攻击具有鲁棒性。在此,我们提出了一个基于相关性的信誉算法,以解决这种评级系统,用户投票的一些对象与评级的排名问题。在该算法中,用户的信誉是通过他/她的评级向量和相应的对象的加权平均评级向量之间的相关系数迭代确定的。仿真和真实数据的实验结果表明,与迭代精化算法和平均得分算法相比,该算法对垃圾邮件攻击具有更高的鲁棒性。

英文摘要:

Ranking problem of web-based rating system has attracted many attentions. A good ranking algorithm should be robust against spammer attack. Here we proposed a correlation based reputation algorithm to solve the ranking problem of such rating systems where user votes some objects with ratings. In this algorithm, reputation of user is iteratively determined by the correlation coefficient between his/her rating vector and the corresponding objects' weighted average rating vector. Comparing with iterative refinement (IR) and mean score algorithm, results for both artificial and real data indicate that, the present algorithm shows a higher robustness against spammer attack.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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