{"type":"ScholarlyArticle","title":"STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems","authors":[{"name":"Jiani Zhang","slug":"jiani-zhang","given":"Jiani","family":"Zhang","affiliation":"The Chinese University of Hong Kong , Hong Kong , China"},{"name":"Xingjian Shi","slug":"xingjian-shi","given":"Xingjian","family":"Shi","affiliation":"Hong Kong University of Science and Technology , Hong Kong , China"},{"name":"Shenglin Zhao","slug":"shenglin-zhao","given":"Shenglin","family":"Zhao","affiliation":"Youtu Lab , Tencent , Shenzhen , China"},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":"The Chinese University of Hong Kong , Hong Kong , China"}],"abstract":"We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.","doi":"10.24963/ijcai.2019/592","url":"https://ijcai.org/proceedings/2019/592","pdf":"https://ijcai.org/proceedings/2019/0592.pdf","markdown":"https://ijcai.org/proceedings/2019/592.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/592","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":592,"pages":{"first":4264,"last":4270},"keywords":["Machine Learning: Recommender Systems","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Berg et al., 2017] Rianne van den Berg, Thomas N Kipf, and Max Welling. 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