文章基本信息

标题：A Dimension Reduction Approach to Multinomial Relation Prediction
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作者：Nozomi Nori ; Danushka Bollegala ; Hisashi Kashima 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2014
卷号：29
期号：1
页码：168-176
DOI：10.1527/tjsai.29.168
出版社：The Japanese Society for Artificial Intelligence
摘要：Many phenomena in the real world can be represented as multinomial relations, which involve multiple and heterogeneous objects. For instance, in social media, users' various actions such as adding annotations to web resources or sharing news with their friends can be represented by multinomial relations which involve multiple and heterogeneous objects such as users, documents, keywords and locations. Predicting multinomial relations would improve many fundamental applications in various domains such as online marketing, social media analyses and drug development. However, the high-dimensional property of such multinomial relations poses one fundamental challenge, that is, predicting multinomial relations with only a limited amount of data. In this paper, we propose a new multinomial relation prediction method, which is robust to data sparsity. We transform each instance of a multinomial relation into a set of binomial relations between the objects and the multinomial relation of the involved objects. We then apply an extension of a low-dimensional embedding technique to these binomial relations, which results in a generalized eigenvalue problem guaranteeing global optimal solutions. We also incorporate attribute information as side information to address the ``cold start"problem in multinomial relation prediction. Experiments with various real-world social web service datasets demonstrate that the proposed method is more robust against data sparseness as compared to several existing methods, which can only find sub-optimal solutions.
关键词：graph ; dimension reduction ; social media ; user modeling ; eigenvalue problem