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Predicting Wikipedia Editor#039;s Editing Interest Based on Factor Graph Model

机译:基于因子图模型的维基百科编辑兴趣预测

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Recruiting or recommending appropriate latent editors who can edit a specific entry (or called article) plays an important role in improving the quality of Wikipedia entries. To predict an editor's editing interest for Wikipedia entries, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, categories of an entry and other important features. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for factor graphs. The experiment on a Wikipedia dataset shows that, the average prediction accuracy (F1-Measure) of the IPFG model could be up to 87.5%, which is about 35% higher than that of a collaborative filtering approach. Moreover, the paper analyses how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. What we found would provide a useful insight into effective Wikipedia article tossing, and improve the quality of those entries that belong to specific categories by means of collective collaboration.
机译:招聘或推荐可以编辑特定条目(或称为文章)的潜在编辑者,对提高Wikipedia条目的质量起着重要作用。为了预测编辑者对Wikipedia条目的编辑兴趣,本文提出了一个兴趣预测因子图(IPFG)模型,该模型的特点是编辑者的社会属性,Wikipedia条目之间的超链接,条目的类别以及其他重要特征。此外,本文提出了一种基于梯度下降和Loopy Sum-Product算法的参数学习算法,用于因子图。 Wikipedia数据集上的实验表明,IPFG模型的平均预测准确性(F1-Measure)可以达到87.5%,比协作过滤方法的平均预测准确性高35%。此外,本文分析了不完整的社会属性和编辑突发如何影响IPFG模型的预测准确性。我们发现的内容将为有效的Wikipedia投掷文章提供有用的见解,并通过集体协作提高属于特定类别的那些条目的质量。

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