Neural IR Meets Graph Embedding: A Ranking Model for Product Search

Abstract

Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.

A preliminary version of the work to appear in TheWebConf'19 (formerly, WWW'19)

References in corpus (8)

• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Convolutional Matrix Completion
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• On Application of Learning to Rank for E-Commerce Search
• MatchZoo: A Toolkit for Deep Text Matching
• Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016
• Semantic Entity Retrieval Toolkit