  Deep learning-based sequential recommender systems have recently attracted
increasing attention from both academia and industry. Most of industrial
Embedding-Based Retrieval (EBR) system for recommendation share the similar
ideas with sequential recommenders. Among them, how to comprehensively capture
sequential user interest is a fundamental problem. However, most existing
sequential recommendation models take as input clicked or purchased behavior
sequences from user-item interactions. This leads to incomprehensive user
representation and sub-optimal model performance, since they ignore the
complete user behavior exposure data, i.e., items impressed yet unclicked by
users. In this work, we attempt to incorporate and model those unclicked item
sequences using a new learning approach in order to explore better sequential
recommendation technique. An efficient triplet metric learning algorithm is
proposed to appropriately learn the representation of unclicked items. Our
method can be simply integrated with existing sequential recommendation models
by a confidence fusion network and further gain better user representation. The
offline experimental results based on real-world E-commerce data demonstrate
the effectiveness and verify the importance of unclicked items in sequential
recommendation. Moreover we deploy our new model (named XDM) into EBR of
recommender system at Taobao, outperforming the deployed previous generation
SDM.
