Collaborative Memory Augmentation for Generative Recommendation

2026-08-02

AI Summary

This work addresses a key limitation in existing generative recommendation methods, which predominantly focus on individual user behavior sequences and struggle to explicitly model collaborative signals across users. To overcome this, the paper proposes OMEGA, a novel framework that introduces external collaborative memory into generative recommendation for the first time. OMEGA constructs a global collaborative memory bank to store cross-user behavioral patterns and incorporates a target-aware retrieval mechanism with gated cross-attention to enable context-aware fusion of relevant knowledge. Extensive experiments on multiple real-world datasets demonstrate that OMEGA significantly outperforms state-of-the-art baselines, validating the effectiveness of collaborative memory in enhancing generative recommendation and highlighting its innovation and practicality in capturing global collaborative patterns.

Abstract

Generative Recommendation (GR) has exhibited great potential by modeling item transitions as a sequence-to-sequence task. Despite the success of GR, existing frameworks primarily focus on modeling individual user sequences within a constrained internal parametric space, failing to explicitly leverage cross-user collaborative signals. To address this issue, we propose \textbf{OMEGA}, a cOllaborative MEmory augmentation framework for Generative recommendAtion. OMEGA bridges the gap between implicit parametric knowledge and explicit collaborative signals. We first introduce a latent context compression method that utilizes learnable query tokens to distill sequential user behavior into compact representations, significantly reducing storage overhead. These compressed representations are aggregated into a collaborative memory bank, serving as an explicit repository of global behavioral patterns. To ensure precise knowledge acquisition, we design a lightweight and target-aware retrieval mechanism that identifies pertinent memories by considering both sequence-level and target-level similarities. Furthermore, a context-aware integration module, equipped with a gated cross-attention mechanism, is employed to adaptively fuse the retrieved collaborative memories with the local user context while mitigating the interference of noisy patterns. Empirical evaluations on multiple real-world datasets demonstrate that OMEGA significantly outperforms existing advanced GR models, validating the potential of external memory as a complement to the generative paradigm.

Problem

• Generative Recommendation
• Collaborative Signals
• Memory Augmentation
• User Sequences
• Cross-user Patterns

Innovation

• Collaborative Memory
• Generative Recommendation
• Latent Context Compression
• Target-aware Retrieval
• Gated Cross-Attention