Behavior Sequence Transformer For E-commerce Recommendation In Alibaba
Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, Wenwu Ou . Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data 2019 – 331 citations
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users’ behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users’ behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.