Collaborative Adversarial Learning for Relational Learning on Multiple Bipartite Graphs

Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has been receiving attention because of its popular applications such as recommendations. How to make efficient relation inference with few observed links is the main problem on multiple bipartite graphs. Most existing approaches attempt to solve the sparsity problem via learning shared representations to integrate knowledge from multi-source data for shared entities. However, they merely model the correlations from one aspect (e.g. distribution, representation), and cannot impose sufficient constraints on different relations of the shared entities. One effective way of modeling the multi-domain data is to learn the joint distribution of the shared entities across domains. In this paper, we propose Collaborative Adversarial Learning (CAL) that explicitly models the joint distribution of the shared entities across multiple bipartite graphs. The objective of CAL is formulated from a variational lower bound that maximizes the joint log-likelihoods of the observations. In particular, CAL consists of distribution-level and feature-level alignments for knowledge from multiple bipartite graphs. The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs. Extensive experiments on two real-world datasets have shown that the proposed model outperforms the existing methods.

[1]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[2]  Xing Xie,et al.  CCCFNet: A Content-Boosted Collaborative Filtering Neural Network for Cross Domain Recommender Systems , 2017, WWW.

[3]  Jeff A. Bilmes,et al.  Deep Canonical Correlation Analysis , 2013, ICML.

[4]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[5]  Ming Yang,et al.  A Survey of Multi-View Representation Learning , 2019, IEEE Transactions on Knowledge and Data Engineering.

[6]  Feng Zhu,et al.  A Deep Framework for Cross-Domain and Cross-System Recommendations , 2018, IJCAI.

[7]  Lina Yao,et al.  DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns , 2019, IJCAI.

[8]  Martin Ester,et al.  Collaborative Denoising Auto-Encoders for Top-N Recommender Systems , 2016, WSDM.

[9]  Xiaodong He,et al.  A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems , 2015, WWW.

[10]  Navdeep Jaitly,et al.  Adversarial Autoencoders , 2015, ArXiv.

[11]  Marc'Aurelio Ranzato,et al.  DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.

[12]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[13]  Chen Zhang,et al.  Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching , 2018, KDD.

[14]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[15]  Ruifan Li,et al.  Cross-modal Retrieval with Correspondence Autoencoder , 2014, ACM Multimedia.

[16]  Geoffrey J. Gordon,et al.  Relational learning via collective matrix factorization , 2008, KDD.

[17]  Vaibhav Rajan,et al.  Deep collective matrix factorization for augmented multi-view learning , 2018, Machine Learning.

[18]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Yu Zhang,et al.  CoNet: Collaborative Cross Networks for Cross-Domain Recommendation , 2018, UMCit@KDD.

[20]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[21]  Victor S. Lempitsky,et al.  Unsupervised Domain Adaptation by Backpropagation , 2014, ICML.

[22]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[23]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[24]  Tat-Seng Chua,et al.  Neural Collaborative Filtering , 2017, WWW.

[25]  Yang Xu,et al.  Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems , 2019, AAAI.

[26]  Liang Lin,et al.  Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching , 2019, ICML.

[27]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.