Dual-Channel Multiplex Graph Neural Networks for Recommendation

arXiv:2403.11624v1 Announce Type: cross
Abstract: Efficient recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interaction relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant shortcomings: (1) Insufficient modeling and exploitation of the impact of various behavior patterns …

abstract arxiv cs.ir cs.lg focus graph graph neural networks modeling networks neural networks recommendation recommender systems relations role shift systems type types world