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Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
[article]
2021
arXiv
pre-print
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation. Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition
arXiv:2107.03813v3
fatcat:tf7b734ymzh47i3o27arpnqmry