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cs.LG2026
SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences
Tsz Fung Pang, Po Jen Chen, Nimish Ronghe +2
Prior embedding models for sequential recommendation typically operate within a homogeneous action space, limiting their ability to capture cross-surface behavioral signals spannin…
cs.LG2026
Cardinality-Decomposed Loss: Matching Training Objectives to Relation Structure in Heterogeneous Recommendation Graphs
Parul Maheshwari, Amulya Paruchuri, Yiqing Zou +3
Graph Neural Networks trained on heterogenous bipartite graphs form a common basis in recommendation systems. These graphs often express relations that vary in cardinality, for exa…