Author

Farhad Farahani

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#machine learning Preprint Aug 2026

SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences

This work presents SAGA, a generative action embedding model that encodes multi-surface user interaction sequences across a Financial Service organization's ecosystems, from checkout, peer-to-peer (P2P) transactions, in-app engagement, email to account actions, into a unified user representation for downstream recommen...

Tsz-Fung Pang, Po‐Jui Chen, Nimish Ronghe et al. · 0 citations

Jul 2026

Cardinality-Decomposed Loss: Matching Training Objectives to Relation Structure in Heterogeneous Recommendation Graphs

A Cardinality-Decomposed Loss (CDL) is proposed that combines both Cross Entropy (CE) and BPR to enable the model to collectively optimize for relations across cardinalities and is found that CDL consistently improves discriminability in attribute embeddings.

Parul Maheshwari, Amulya Paruchuri, Yiqing Zou et al. · arXiv.org · 0 citations