Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation
复制标题
DOI：
10.1609/aaai.v32i1.12037
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Xiaoyan Cai;Junwei Han;Libin Yang
中科院分区：
文献类型：
--
作者：
Xiaoyan Cai;Junwei Han;Libin Yang
近年来，网络表示技术在引文推荐、多标签分类和链接预测等领域得到了广泛的应用。它学习网络中每个顶点的低维向量表示。现有的网络表示方法只关注顶点信息的不完整方面（即，顶点内容、网络结构或部分集成），而且它们通常被设计用于同构信息网络，其中网络的所有顶点都是相同类型的。本文提出了一种深度网络表示模型，该模型利用生成对抗网络将网络结构和顶点内容信息整合到一个统一的框架中，并将异构网络中不同类型的顶点表示在一个连续的公共向量空间中。基于所提出的模型，我们可以获得异构书目网络表示有效的引文推荐。该模型也使个性化引文推荐成为可能，这是一个新的问题，在过去的几篇论文解决。在AAN和DBLP数据集上进行评估时，所提出的基于异构书目网络的引文推荐方法的性能与其他基于网络表示的引文推荐方法的性能相当。结果还表明，个性化的引文推荐方法比非个性化的引文推荐方法更有效。
Network representation has been recently exploited for many applications, such as citation recommendation, multi-label classification and link prediction. It learns low-dimensional vector representation for each vertex in networks. Existing network representation methods only focus on incomplete aspects of vertex information (i.e., vertex content, network structure or partial integration), moreover they are commonly designed for homogeneous information networks where all the vertices of a network are of the same type. In this paper, we propose a deep network representation model that integrates network structure and the vertex content information into a unified framework by exploiting generative adversarial network, and represents different types of vertices in the heterogeneous network in a continuous and common vector space. Based on the proposed model, we can obtain heterogeneous bibliographic network representation for efficient citation recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When evaluated on the AAN and DBLP datasets, the performance of the proposed heterogeneous bibliographic network based citation recommendation approach is comparable with that of the other network representation based citation recommendation approaches. The results also demonstrate that the personalized citation recommendation approach is more effective than the non-personalized citation recommendation approach.