End-to-end Graph-Sequential Representation Learning for Accurate Recommendations

arXiv:2403.00895v1 Announce Type: cross
Abstract: Many recent advancements in recommender systems have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter one aims to capture indirect dependencies across the interactions graph. This paper …

abstract arxiv behavioral data cs.ai cs.ir cs.lg data good graph graph-based modeling next personalized ranking recommendation recommendations recommender systems relationships representation representation learning scalability systems tasks type