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

Abstract:Prior embedding models for sequential recommendation typically operate within a homogeneous action space, limiting their ability to capture cross-surface behavioral signals spanning distinct behavioral domains. We present 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 recommendation tasks. Central to SAGA is a per-field tokenization schema that decomposes each action event into multiple field-level tokens (e.g. product, interaction, surface), enabling field-level attention and per-field training objectives that fused single-token approaches cannot support. Through an offline ablation study on loss formulation, tokenization granularity and training data scope, we isolate the contribution of each design choice. A downstream model integrated with SAGA-generated user embeddings delivers the strongest overall click and conversion lift across diverse downstream touchpoints, compared to all ablated and alternative architectures.

9 pages, 3 figures. Accepted to ACM RecSys 2026 Context-Aware Recommender Systems (CARS) workshop

Machine Learning (cs.LG); Information Retrieval (cs.IR)

arXiv:2608.15429 [cs.LG]

(or arXiv:2608.15429v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.15429 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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