  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.
