Inferring substitutable products with deep network embedding
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Author(s)
Zhang, S
Yin, H
Wang, Q
Chen, T
Chen, H
Nguyen, QVH
Griffith University Author(s)
Year published
2019
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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 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 ...
View more >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 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.
View less >
View more >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 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.
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Conference Title
IJCAI International Joint Conference on Artificial Intelligence
Volume
2019-August
Copyright Statement
© 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
Subject
Artificial intelligence