Centrala begrepp
This paper proposes HAGO, a novel data-centric framework that leverages heterogeneous adaptive graph coordinators and graph prompting to address the challenges of cross-domain recommendation by aligning representations and transferring knowledge across multiple domains.
Statistik
HAGO achieves 1.20% and 1.28% improvements on Recall@10.
HAGO achieves 1.44% and 1.67% improvements on HR@10.
HAGO achieves 1.32% and 1.64% improvements on NDCG@10.
HAGO achieves 0.59% and 2.57% improvements on MRR.
Citat
"Although these methods have achieved notable success, some later theoretical studies [2] have found that the performance ceiling of model-centric methods is strictly limited by the intrinsic discrepancy among various domains, indicating that we might not further improve these methods unless we can find some data-level approaches to narrow down the natural gap among various graph domains."
"Inspired by the recent success of graph prompting in its powerful data operation capability [33, 35, 52], we go beyond the previous model-centric paradigm and hope to bridge gaps between diverse graph datasets in a data-centric perspective."