World Wide Web

Voltage

Engineering

Biology

Data mining

Computer science

Embedding

Genetics

Sequence (biology)

Artificial intelligence

Transformer

Deep learning

Recommender system

Machine learning

Electrical engineering

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.

Behavior sequence transformer for e-commerce recommendation in Alibaba