An Enhanced Gated Graph Neural Network for E-commerce Recommendation

Abstract

The recommender system for e-commerce aims to recommend appropriate items to online customers in order to drive more views, clicks or purchases on those items. Most existing models incorporate the users' historical behaviors, their profiles, and the item metadata to achieve good performances. However, since more and more people are surfing the Internet without logging in, it is no longer capable to provide accurate recommendations based on the historical data or profiles. To tackle this issue, we propose MentalNet-a mental model for e-commerce recommendation by enhancing the gated Graph Neural Network (GNN) and capturing user intent in a short session. More precisely, MentalNet is composed of two stages: in the first stage, we enhance the gated GNN to take into account the complex graph-level transitions among items, for an improved item representation; In the second stage, we propose a mental model to simulate user intent using item embedding, and then compute item preferences based on each intent. Finally, we empirically demonstrate the effectiveness of the proposed method on three datasets, including the CIKM CUP data, the RecSys Challenge data and a real-world e-commerce dataset in Alibaba Group.