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arXiv · 1911.12481

Product Knowledge Graph Embedding for E-commerce

Da Xu·Chuanwei Ruan·Evren Korpeoglu·Sushant Kumar·Kannan Achan
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Abstract

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks.

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Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan. 2019-11-28. Product Knowledge Graph Embedding for E-commerce. https://doi.org/10.1145/3336191.3371778

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