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Unclicked User Behaviors Enhanced SequentialRecommendation | | Fuyu Lv
; Mengxue Li
; Tonglei Guo
; Changlong Yu
; Fei Sun
; Taiwei Jin
; Keping Yang
; | | Date: |
24 Oct 2020 | | Abstract: | Deep learning-based sequential recommender systems have recently attracted
increasing attention from both academia and industry. 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., impressed yet
unclicked items. 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. We name our algorithm SRU2B (short for Sequential
Recommendation with Unclicked User Behaviors). The experimental results based
on real-world E-commerce data demonstrate the effectiveness of SRU2B and verify
the importance of unclicked items in sequential recommendation. | | Source: | arXiv, 2010.12837 | | Services: | Forum | Review | PDF | Favorites | |
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