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基于生成模型的在线评级数据信任网络推理
Trust Network Inference for Online Rating Data Using Generative Models |
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| 课程网址: |
http://videolectures.net/kdd2010_chong_tat_tni/
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| 主讲教师: |
Freddy Chong Tat Chua
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| 开课单位: |
新加坡管理大学
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| 开课时间: |
2010-10-01
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| 课程语种: |
英语
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| 中文简介: |
在在线评级系统中，评估者为其他用户贡献的对象分配评级。此外，评估者可以根据少数评级和信任相关因素对对象贡献者产生信任和不信任。之前的研究表明，评级和信任链接可以相互影响，但缺乏将这些因素联系在一起的正式模型。因此，在本文中，我们提出了信任先行因子（TAF）模型，这是一种新的概率模型，它根据一些评估者和贡献者的因素产生评级。我们证明了Collapsed Gibbs Sampling可以学习模型的参数。然后，我们应用该模型来预测使用真实数据集的评估者和评论贡献者之间的信任和不信任。我们的实验表明，所提出的模型能够以统一的方式预测信任和不信任。该模型还可以确定从评级和信任数据无法观察到的用户因素。
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| 课程简介: |
In an online rating system, raters assign ratings to objects contributed by other users. In addition, raters can develop trust and distrust on object contributors depending on a few rating and trust related factors. Previous study has shown that ratings and trust links can influence each other but there has been a lack of a formal model to relate these factors together. In this paper, we therefore propose Trust Antecedent Factor (TAF) Model, a novel probabilistic model that generate ratings based on a number of rater's and contributor's factors. We demonstrate that parameters of the model can be learnt by Collapsed Gibbs Sampling. We then apply the model to predict trust and distrust between raters and review contributors using a real data-set. Our experiments have shown that the proposed model is capable of predicting both trust and distrust in a unified way. The model can also determine user factors which otherwise cannot be observed from the rating and trust data.
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| 关 键 词: |
在线评级; 用户贡献; 概率模型
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-05-10：cwx
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| 阅读次数: |
54
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