Generative Adversarial Network Based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation

Network representation has been recently exploited for many applications, such as citation recommendation, multilabel classification and link prediction. It learns lowdimensional 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 nonpersonalized citation recommendation approach.

[1]  W. Bruce Croft,et al.  Recommending citations for academic papers , 2007, SIGIR.

[2]  John Glover,et al.  Modeling documents with Generative Adversarial Networks , 2016, ArXiv.

[3]  Shujian Huang,et al.  Academic Paper Recommendation Based on Heterogeneous Graph , 2015, CCL.

[4]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[5]  Wenyi Huang,et al.  RefSeer: A citation recommendation system , 2014, IEEE/ACM Joint Conference on Digital Libraries.

[6]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[7]  Daniel Kifer,et al.  Context-aware citation recommendation , 2010, WWW '10.

[8]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[9]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[10]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[11]  Shenghuo Zhu,et al.  Learning multiple graphs for document recommendations , 2008, WWW.

[12]  Marcos André Gonçalves,et al.  A source independent framework for research paper recommendation , 2011, JCDL '11.

[13]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[14]  Yann LeCun,et al.  Energy-based Generative Adversarial Network , 2016, ICLR.

[15]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[16]  Vasudeva Varma,et al.  Scientific Article Recommendation by using Distributed Representations of Text and Graph , 2017, WWW.

[17]  Myron Wish,et al.  Three-Way Multidimensional Scaling , 1978 .

[18]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[19]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[20]  Dragomir R. Radev,et al.  The ACL anthology network corpus , 2009, Language Resources and Evaluation.

[21]  Xu Sun,et al.  A unified graph model for personalized query-oriented reference paper recommendation , 2013, CIKM.

[22]  Jie Tang,et al.  ArnetMiner: extraction and mining of academic social networks , 2008, KDD.

[23]  Jiawei Han,et al.  ClusCite: effective citation recommendation by information network-based clustering , 2014, KDD.

[24]  Chengqi Zhang,et al.  Tri-Party Deep Network Representation , 2016, IJCAI.

[25]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Fernando Ortega,et al.  A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model , 2016, Knowl. Based Syst..

[28]  Qiaozhu Mei,et al.  PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks , 2015, KDD.

[29]  Peter D. Hoff,et al.  Latent Space Approaches to Social Network Analysis , 2002 .