Abstract
Deep learning-based sequential recommender systems have recently attracted increasing attention from both academia and industry. Most of industrial Embedding-Based RetrievalĀ (EBR) systems 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 modelĀ (named XDM) into EBR of recommender system at Taobao, outperforming the previous deployed generation SDM.
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Notes
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Popular E-commerce websites with ten millions of active itemsĀ (www.taobao.com and www.tmall.com).
- 2.
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Lv, F. et al. (2022). XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_31
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