ATNN: Adversarial Two-Tower Neural Network for New Item’s Popularity Prediction in E-commerce

The e-commerce era is witnessing rising new arrivals of items on e-commerce platforms every day. Identifying potential popular items accurately is of great importance in creating commercial value. Click-Through Rate (CTR) is a general indicator to evaluate item popularity. However, existing methods fail in new arrivals prediction because of sparse item features, missing item statistics and high time complexity of computing for all pairs of users and items. To tackle these challenges, we propose a novel Adversarial Two-tower Neural Network (ATNN) model for new arrivals CTR predictions by introducing an adversarial network to a two-tower network. We design a generator and a discriminator to better learn an item vector based on item profiles without item statistics. We also develop a strategy with an O(1) time complexity for a new item's popularity prediction by constructing a user group and utilizing its mean user vector in a time-efficient manner. We implement ATNN on a largescale real-world dataset from one of the world's largest ecommerce platforms, “Tmall.com”. Empirical results show that ATNN is strongly capable of learning item vectors from item profiles for e-commerce. Furthermore, by introducing multi-task learning technology, we extend ATNN to food delivery service. Experimental results on one popular food delivery platform, “Ele.me”, demonstrate that ATNN can recognize attractive and welcoming new restaurants that have higher Value per Page View (VpPV) and generate more Gross Merchandise Volume (GMV).