Product Knowledge Graph Embedding for E-commerce

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.

References in corpus (3)

• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
• Stochastic gradient descent on Riemannian manifolds
• Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation

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• Cross-platform Product Matching Based on Entity Alignment of Knowledge Graph with RAEA model
• Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?
• Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System
• Towards the D-Optimal Online Experiment Design for Recommender Selection
• GRIT: Graph-based Recall Improvement for Task-oriented E-commerce Queries