Cross-Graph Convolution Learning for Large-Scale Text-Picture Shopping Guide in E-Commerce Search

In this work, a new e-commerce search service named text-picture shopping guide (TPSG) is investigated and deployed to one of the most popular shopping platforms called Taobao. Different from traditional services that only contain text options, the TPSG provides pairs of text terms and user-friendly pictures for shopping guide, named text-picture options (TPOs). Instead of manually labeling pictures, we aim to automatically recommend personalized pictures in TPOs. To this end, we build a large-scale graph model on a great amount of data about users, pictures, and terms. Accordingly, a cross-graph convolution learning (CGCL) method is proposed to facilitate the accurate and efficient inference on the constructed graph. To separate the cue of personalized preferences of users to commodities, we factorize the entire mixture-relation graph involving attributes/relations of users and commodities into the user graph, the commodity graph, and the cross user-commodity graph which just characterizes the preferences. Further, we introduce powerful graph convolution to learn more effective representation of these graphs. To reduce the computation burden, specifically, we generalize graph convolution and propose a tensor graph convolution method to learn representation on cross graphs. We conduct extensive offline and online experiments on the large-scale datasets. The results show that the proposed CGCL is very effective and the TPOs recommendation method outperforms manual/advanced selection methods.