An Interpretable Ensemble of Graph and Language Models for Improving Search Relevance in E-Commerce

arXiv:2403.00923v1 Announce Type: cross
Abstract: The problem of search relevance in the E-commerce domain is a challenging one since it involves understanding the intent of a user's short nuanced query and matching it with the appropriate products in the catalog. This problem has traditionally been addressed using language models (LMs) and graph neural networks (GNNs) to capture semantic and inter-product behavior signals, respectively. However, the rapid development of new architectures has created a gap between research and the practical adoption …

abstract arxiv commerce cs.cl cs.ir domain e-commerce ensemble graph language language models products query search type understanding