一个分散的推荐系统,用于有效的网络可信度评估

Thanasis G. Papaioannou, Jean-Eudes Ranvier, Alexandra Olteanu, K. Aberer
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引用次数: 21

摘要

网上有数量惊人且不断增长的数据。由于网络信息具有巨大的经济潜力和动态性,如瞬态域名、动态内容等,使得网络信息不可信问题更加突出。在本文中,我们通过一个分散的社会推荐系统解决了评估网页可信度的问题。具体来说,我们同时采用i)基于特定网页特征的基于项目的协同过滤(CF), ii)基于朋友评分的基于用户的协同过滤,以及iii)页面在搜索结果中的排名。这些因素被适当地组合到基于自适应权重的单一评估中,这些权重取决于它们对不同主题和不同恶意评级部分的有效性。具有真实网页可信度评估痕迹的模拟实验表明,我们的混合方法优于其组成成分和经典的基于内容的分类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A decentralized recommender system for effective web credibility assessment
An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches.
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