Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications
Autor: | Jiajun Bu, Xuming Pan, Zhao Li, Chengwei Yao, Yuhang Jiao, Xin Shen, Pengcheng Zou, Xianling Meng |
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Rok vydání: | 2020 |
Předmět: |
Artificial neural network
Computer science Graph neural networks business.industry 02 engineering and technology E-commerce 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Graph 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Collaborative filtering Bipartite graph Artificial intelligence business Cluster analysis computer 0105 earth and related environmental sciences |
Zdroj: | ICDE |
Popis: | The e-commerce appeals to a multitude of online shoppers by providing personalized experiences and becomes indispensable in our daily life. Accurately predicting user preference and making a recommendation of favorable items plays a crucial role in improving several key tasks such as Click Through Rate (CTR) and Conversion Rate (CVR) in order to increase commercial value. Some state-of-the-art collaborative filtering methods exploiting non-linear interactions on a user-item bipartite graph are able to learn better user and item representations with Graph Neural Networks (GNNs), which do not learn hierarchical representations of graphs because they are inherently flat. Hierarchical representation is reportedly favorable in making more personalized item recommendations in terms of behaviorally similar users in the same community and a context of topic-driven taxonomy. However, some advanced approaches, in this regard, are either only considering linear interactions, or adopting single-level community, or computationally expensive. To address these problems, we propose a novel method with Hierarchical bipartite Graph Neural Network (HiGNN) to handle large-scale e-commerce tasks. By stacking multiple GNN modules and using a deterministic clustering algorithm alternately, HiGNN is able to efficiently obtain hierarchical user and item embeddings simultaneously, and effectively predict user preferences on a larger scale. Extensive experiments on some real-world e-commerce datasets demonstrate that HiGNN achieves a significant improvement compared to several popular methods. Moreover, we deploy HiGNN in Taobao, one of the largest e-commerces with hundreds of million users and items, for a series of large-scale prediction tasks of item recommendations. The results also illustrate that HiGNN is arguably promising and scalable in real-world applications. |
Databáze: | OpenAIRE |
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