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dc.contributor.authorZhang, S
dc.contributor.authorYin, H
dc.contributor.authorWang, Q
dc.contributor.authorChen, T
dc.contributor.authorChen, H
dc.contributor.authorNguyen, QVH
dc.date.accessioned2020-03-19T04:43:38Z
dc.date.available2020-03-19T04:43:38Z
dc.date.issued2019
dc.identifier.isbn9780999241141
dc.identifier.issn1045-0823
dc.identifier.doi10.24963/ijcai.2019/598
dc.identifier.urihttp://hdl.handle.net/10072/392469
dc.description.abstractOn 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 semi-supervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationship 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 seven real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.
dc.description.peerreviewedYes
dc.publisherInternational Joint Conferences on Artificial Intelligence (IJCAI)
dc.relation.ispartofconferencename28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
dc.relation.ispartofconferencetitleIJCAI International Joint Conference on Artificial Intelligence
dc.relation.ispartofdatefrom2019-08-10
dc.relation.ispartofdateto2019-08-16
dc.relation.ispartoflocationMacao, China
dc.relation.ispartofpagefrom4306
dc.relation.ispartofpageto4312
dc.relation.ispartofvolume2019-August
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleInferring substitutable products with deep network embedding
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhang, S; Yin, H; Wang, Q; Chen, T; Chen, H; Nguyen, QVH, Inferring substitutable products with deep network embedding, IJCAI International Joint Conference on Artificial Intelligence, 2019, 2019-August, pp. 4306-4312
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-03-19T04:39:37Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry


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