Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers

A. Us Saeed, M. Afzal, A. Latif, K. Tochtermann
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引用次数: 4

Abstract

New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.
基于书签计数的引文排名预测——以WWW06论文为例
网络协作和参与性角色的新发展已经出现了新的基于网络的快速通道信息系统,如标签和书签应用程序。同样的作者在其他地方表明,对于同样的论文,与引用相比,标签和书签的出现和增长速度非常快,并且与引用保持良好的相关性。研究基于这些系统的排名预测模型,有利于快速洞察并在很早的时间内定位高生产力和可扩散的知识。这表明,基于书签计数(i-e计数代表有多少研究人员对出版物表现出兴趣),在会议或期刊发行范围内对论文的引用排名进行建模可能会很有趣。我们采用线性回归模型对WWW06会议论文的引文排名进行预测,并比较了书签数和合著者网络数预测的引文排名模型。结果表明,基于书签计数的排名预测模型远优于基于合著者网络的排名预测模型,前者的平均绝对误差在5以内,后者的平均绝对误差在18以上。与此同时,我们还比较了两种书签预测模型,其中一种基于总引用排名作为因变量,另一种基于调整后的引用排名。在总引用数中减去自我和合著者的引用数后调整引文排名。比较发现,调整引文排名后,模型和相关性都有了显著改善。这可以解释为书签机制代表了类似于出版物的全局发现的现象。而在共同作者网络中,论文是亲自交流的,这种交流或选择可能不会在书签系统中被捕获。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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