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Query-dependent cross-domain ranking in heterogeneous network

机译：异构网络中依赖查询的跨域排名

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摘要

Traditional learning-to-rank problem mainly focuses on one single type of objects. However, with the rapid growth of the Web 2.0, ranking over multiple interrelated and heterogeneous objects becomes a common situation, e.g., the heterogeneous academic network. In this scenario, one may have much training data for some type of objects (e.g. conferences) while only very few for the interested types of objects (e.g. authors). Thus, the two important questions are: (1) Given a networked data set, how could one borrow supervision from other types of objects in order to build an accurate ranking model for the interested objects with insufficient supervision? (2) If there are links between different objects, how can we exploit their relationships for improved ranking performance? In this work, we first propose a regularized framework called HCDRank to simultaneously minimize two loss functions related to these two domains. Then, we extend the approach by exploiting the link information between heterogeneous objects. We conduct a theoretical analysis to the proposed approach and derive its generalization bound to demonstrate how the two related domains could help each other in learning ranking functions. Experimental results on three different genres of data sets demonstrate the effectiveness of the proposed approaches.
机译：传统的排名学习问题主要集中于一种类型的对象。但是，随着Web 2.0的快速发展，对多个相互关联且异构的对象进行排名已成为一种常见的情况，例如，异构的学术网络。在这种情况下，对于某些类型的对象（例如会议），可能会有很多训练数据，而对于感兴趣的对象（例如作者）只有很少的训练数据。因此，两个重要的问题是：（1）给定一个网络数据集，一个人如何能从其他类型的对象中借用监管，以便在监管不足的情况下为感兴趣的对象建立准确的排名模型？ （2）如果不同对象之间存在链接，我们如何利用它们之间的关系来改善排名性能？在这项工作中，我们首先提出一个称为HCDRank的正则化框架，以同时最小化与这两个域相关的两个损失函数。然后，我们通过利用异构对象之间的链接信息来扩展该方法。我们对所提出的方法进行了理论分析，并推导了它的推广范围，以证明这两个相关领域如何在学习排名功能方面互相帮助。三种不同类型的数据集的实验结果证明了所提出方法的有效性。

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