首页> 外文会议>Computational Aspects of Social Networks, 2009. CASON '09 >Enriching Trust Prediction Model in Social Network with User Rating Similarity
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Enriching Trust Prediction Model in Social Network with User Rating Similarity

机译:用户评价相似度丰富社交网络信任预测模型

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Trust management is an increasingly important issue in large social networks, where the amount of data is too extensive to be analysed by ordinary users. Hence there is an urgent need for research aiming at building automated systems that can support users in making their decisions concerning trust. This work is a preliminary implementation of selected ideas described in our previous research proposal which concerns taking a machine learning approach to the problem of trust prediction in social networks.We report experiments conducted on a publicly available social network dataset epinions.com. The results indicate that i) it is possible to predict trust to some extent, but much room for improvement is present; ii) enriching the model with attributes based on similarity between users can significantly improve trust prediction accuracy for more similar users.
机译:在大型社交网络中,信任管理已成为一个日益重要的问题,在大型社交网络中,数据量过于庞大,普通用户无法对其进行分析。因此,迫切需要进行旨在构建能够支持用户做出有关信任的决策的自动化系统的研究。这项工作是对我们先前的研究建议中描述的某些想法的初步实现,该想法涉及采用机器学习方法解决社交网络中的信任预测问题。我们报告了在可公开获取的社交网络数据集epinions.com上进行的实验。结果表明:i)在某种程度上可以预测信任,但仍有很大的改进空间; ii)利用基于用户之间相似度的属性来丰富模型,可以显着提高更多相似用户的信任预测准确性。

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