{"type":"ScholarlyArticle","title":"Inferring Substitutable Products with Deep Network Embedding","authors":[{"name":"Shijie Zhang","slug":"shijie-zhang","given":"Shijie","family":"Zhang","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland , Australia"},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland , Australia"},{"name":"Qinyong Wang","slug":"qinyong-wang","given":"Qinyong","family":"Wang","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland , Australia"},{"name":"Tong Chen","slug":"tong-chen","given":"Tong","family":"Chen","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland , Australia"},{"name":"Hongxu Chen","slug":"hongxu-chen","given":"Hongxu","family":"Chen","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland , Australia"},{"name":"Quoc Viet Hung Nguyen","slug":"quoc-viet-hung-nguyen","given":"Quoc Viet Hung","family":"Nguyen","affiliation":"School of Information and Communication Technology , Griffith University , Australia"}],"abstract":"On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.","doi":"10.24963/ijcai.2019/598","url":"https://ijcai.org/proceedings/2019/598","pdf":"https://ijcai.org/proceedings/2019/0598.pdf","markdown":"https://ijcai.org/proceedings/2019/598.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/598","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":598,"pages":{"first":4306,"last":4312},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2014] Charu C Aggarwal, Mansurul A Bhuiyan, and Mohammad Al Hasan. 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