Dual Sequence Transformer for Query-based Interactive Recommendation

Interactive recommendation has drawn widespread attention from both academia and industry due to its effectiveness in real-world mobile applications. Instead of receiving message passively, customers can exploit further with less effort through generated queries. Usually, such systems mainly contain two main components: query generation and item recommendation. In this paper, we propose a novel framework that models both queries and items in shared latent embedding space via a dual sequence transformer structure, which captures customer's potential interest from the prospect of reconciling the historical queries and corresponding customers interactions. We propose a click-through-rate model to generate query candidates, and a session search model for further more precise information. Comprehensive offline and online experiments are conducted, and the results demonstrate that our proposed dual-sequence-transformer based model can better utilize interaction and improve the accuracy of recommendations.