ABSTRACT
Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element of GCNs to perform information propagation and generate informative representations. Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with less-interested items occur in implicit feedback (say, a user views micro-videos accidentally). This means that the neighborhoods involved with such false-positive edges will be influenced negatively and the signal on user preference can be severely contaminated. However, existing GCN-based recommender models leave such challenge under-explored, resulting in suboptimal representations and performance.
In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommender model, Graph-Refined Convolutional Network (GRCN), which adjusts the structure of interaction graph adaptively based on status of model training, instead of remaining the fixed structure. In particular, a graph refining layer is designed to identify the noisy edges with the high confidence of being false-positive interactions, and consequently prune them in a soft manner. We then apply a graph convolutional layer on the refined graph to distill informative signals on user preference. Through extensive experiments on three datasets for micro-video recommendation, we validate the rationality and effectiveness of our GRCN. Further in-depth analysis presents how the refined graph benefits the GCN-based recommender model.
Supplemental Material
- Sanjeev Arora, Yingyu Liang, and Tengyu Ma. 2016. A simple but tough-to-beat baseline for sentence embeddings. In Proceedings of International Conference on Learning Representations. 1--16.Google Scholar
- Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1--9.Google Scholar
- Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang. 2020. A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. In Proceedings of the AAAI Conference on Artificial Intelligence. 1--6.Google ScholarCross Ref
- Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In Proceedings of the International ACM SIGIR conference on Research and Development in Information Retrieval. 335--344.Google ScholarDigital Library
- Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proceedings of the World Wide Web conference. 639--648.Google ScholarDigital Library
- Peng Cui Wenwu Zhu Dingyuan Zhu, Ziwei Zhang. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1399--1407.Google Scholar
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In Proceedings of the International Conference on World Wide Web. 417--426.Google ScholarDigital Library
- Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and statistics. 249--256.Google Scholar
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of International Conference on Neural Information Processing Systems. 1024--1034.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016b. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770--778.Google ScholarCross Ref
- Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence. 144--150.Google ScholarCross Ref
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 639--648.Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min Yen Kan, and Tat Seng Chua. 2016a. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proceedings of the International ACM SIGIR conference on Research and Development in Information Retrieval. 549--558.Google ScholarDigital Library
- Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et almbox. 2017. CNN architectures for large-scale audio classification. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 131--135.Google ScholarCross Ref
- Jun Hu, Shengsheng Qian, Quan Fang, and Changsheng Xu. 2019 a. Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching. In Proceedings of the ACM International Conference on Multimedia. 1157--1165.Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In IEEE International Conference on Data Mining. 263--272.Google Scholar
- Yupeng Hu, Chong Yang, Peng Zhan, Jia Zhao, Yujun Li, and Xueqing Li. 2019 b. Efficient continuous KNN join processing for real-time recommendation. Personal and Ubiquitous Computing (2019), 1--11.Google Scholar
- Ziling Huang, Zheng Wang, Wei Hu, Chia-Wen Lin, and Shin'ichi Satoh. 2019. DoT-GNN: Domain-Transferred Graph Neural Network for Group Re-identification. In Proceedings of the ACM International Conference on Multimedia. 1888--1896.Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of International Conference on Learning Representations. 1--16.Google Scholar
- Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of International Conference on Learning Representations. 1--14.Google Scholar
- Boris Knyazev, Graham W Taylor, and Mohamed Amer. 2019. Understanding Attention and Generalization in Graph Neural Networks. In Advances in Neural Information Processing Systems. 4204--4214.Google Scholar
- Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. 305--314.Google ScholarDigital Library
- Fan Liu, Zhiyong Cheng, Changchang Sun, Yinglong Wang, Liqiang Nie, and Mohan Kankanhalli. 2018a. User Diverse Preference Modeling by Multimodal Attentive Metric Learning. In Proceedings of the ACM International Conference on Multimedia. 1526--1534.Google Scholar
- Jiawei Liu, Zheng-Jun Zha, Richang Hong, Meng Wang, and Yongdong Zhang. 2019. Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search. In Proceedings of the ACM International Conference on Multimedia. 665--673.Google ScholarDigital Library
- Meng Liu, Liqiang Nie, Meng Wang, and Baoquan Chen. 2017. Towards Micro-Video Understanding by Joint Sequential-Sparse Modeling. In Proceedings of the ACM International Conference on Multimedia. 970--978.Google ScholarDigital Library
- Meng Liu, Liqiang Nie, Xiang Wang, Qi Tian, and Baoquan Chen. 2018b. Online data organizer: micro-video categorization by structure-guided multimodal dictionary learning. IEEE Transactions on Image Processing, Vol. 28, 3 (2018), 1235--1247.Google ScholarDigital Library
- Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. In Proceedings of International Conference on Machine Learning. 4212--4221.Google Scholar
- Andrew L Maas, Awni Y Hannun, and Andrew Y Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the international conference on machine learning. 3--9.Google Scholar
- Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proceedings of the international conference on machine learning. 807--814.Google Scholar
- Liqiang Nie, Xiang Wang, Jianglong Zhang, Xiangnan He, Hanwang Zhang, Richang Hong, and Qi Tian. 2017. Enhancing Micro-video Understanding by Harnessing External Sounds. In Proceedings of ACM Multimedia Conference on Multimedia Conference. 1192--1200.Google ScholarDigital Library
- Arantxa Casanova Adriana Romero Pietro Liò Yoshua Bengio Petar, Guillem Cucurull. 2018. Graph Attention Networks. In Proceedings of International Conference on Learning Representations. 1--12.Google Scholar
- Xufeng Qian, Yueting Zhuang, Yimeng Li, Shaoning Xiao, Shiliang Pu, and Jun Xiao. 2019. Video relation detection with spatio-temporal graph. In Proceedings of the ACM International Conference on Multimedia. 84--93.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009.BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the conference on Uncertainty in Artificial Intelligence. 452--461.Google Scholar
- Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Advances in neural information processing systems. 4077--4087.Google Scholar
- Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019 a. KGAT: Knowledge Graph Attention Network for Recommendation. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1--9.Google ScholarDigital Library
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 b. Neural Graph Collaborative Filtering. In Proceedings of the International ACM SIGIR conference on Research and Development in Information Retrieval. 165--174.Google ScholarDigital Library
- Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020 a. Disentangled Graph Collaborative Filtering. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 1001--1010.Google Scholar
- Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat-Seng Chua. 2020 b. Reinforced Negative Sampling over Knowledge Graph for Recommendation. In Proceedings of The Web Conference. 99--109.Google ScholarDigital Library
- Yinwei Wei, Zhiyong Cheng, Xuzheng Yu, Zhou Zhao, Lei Zhu, and Liqiang Nie. 2019 a. Personalized Hashtag Recommendation for Micro-videos. In Proceedings of the ACM International Conference on Multimedia. 1446--1454.Google ScholarDigital Library
- Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019 b. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In Proceedings of ACM Multimedia Conference on Multimedia Conference. 1437--1445.Google ScholarDigital Library
- Jiaxin Wu, Sheng-Hua Zhong, and Yan Liu. 2019. MvsGCN: A Novel Graph Convolutional Network for Multi-video Summarization. In Proceedings of the ACM International Conference on Multimedia. 827--835.Google ScholarDigital Library
- Jheng-Hong Yang, Chih-Ming Chen, Chuan-Ju Wang, and Ming-Feng Tsai. 2018. HOP-rec: high-order proximity for implicit recommendation. In Proceedings of the ACM Conference on Recommender Systems. 140--144.Google ScholarDigital Library
- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2019. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974--983.Google Scholar
- Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, and Yongdong Zhang. 2020. How to Retrain Recommender System? A Sequential Meta-Learning Method. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 1479--1488.Google Scholar
Index Terms
- Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback
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