Capturing users' precise preferences is a fundamental problem in large-scale
recommender system. Currently, item-based Collaborative Filtering (CF) methods
are common matching approaches in industry. However, they are not effective to
model dynamic and evolving preferences of users. In this paper, we propose a
new sequential deep matching (SDM) model to capture users' dynamic preferences
by combining short-term sessions and long-term behaviors. Compared with
existing sequence-aware recommendation methods, we tackle the following two
inherent problems in real-world applications: (1) there could exist multiple
interest tendencies in one session. (2) long-term preferences may not be
effectively fused with current session interests. Long-term behaviors are
various and complex, hence those highly related to the short-term session
should be kept for fusion. We propose to encode behavior sequences with two
corresponding components: multi-head self-attention module to capture multiple
types of interests and long-short term gated fusion module to incorporate
long-term preferences. Successive items are recommended after matching between
sequential user behavior vector and item embedding vectors. Offline experiments
on real-world datasets show the superior performance of the proposed SDM.
Moreover, SDM has been successfully deployed on online large-scale recommender
system at Taobao and achieves improvements in terms of a range of commercial
metrics.

本文提出了一种新的序列深度匹配模型来捕捉用户的动态兴趣，其中同时结合了短期会话和长期行为，成功地解决了现有序列感知推荐方法在现实世界应用中存在的两个固有问题，并取得了很好的实验效果。