Behavior Sequence Transformer for E-commerce Recommendation in Alibaba

15 May 2019  ·  Qiwei Chen, Huan Zhao, Wei Li, Pipei Huang, Wenwu Ou ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Recommendation Systems MovieLens 1M BST RMSE 0.8401 # 9