FAIR: Focused Attention Is All You Need for Generative Recommendation

TL;DR

FAIR introduces a focused attention mechanism for transformer-based generative recommendation, improving relevance and robustness in user behavior modeling by suppressing noise and emphasizing informative contexts.

Contribution

It proposes a novel focused attention mechanism, a noise-robustness objective, and a mutual information maximization strategy for enhanced generative recommendation.

Findings

Outperforms existing methods on four public benchmarks.

Demonstrates improved attention focus and noise robustness.

Achieves higher accuracy in next-item prediction.

Abstract

Recently, transformer-based generative recommendation has garnered significant attention for user behavior modeling. However, it often requires discretizing items into multi-code representations (e.g., typically four code tokens or more), which sharply increases the length of the original item sequence. This expansion poses challenges to transformer-based models for modeling user behavior sequences with inherent noises, since they tend to overallocate attention to irrelevant or noisy context. To mitigate this issue, we propose FAIR, the first generative recommendation framework with focused attention, which enhances attention scores to relevant context while suppressing those to irrelevant ones. Specifically, we propose (1) a focused attention mechanism integrated into the standard Transformer, which learns two separate sets of Q and K attention weights and computes their difference as the final attention scores to eliminate attention noise while focusing on relevant contexts; (2) a noise-robustness objective, which encourages the model to maintain stable attention patterns under stochastic perturbations, preventing undesirable shifts toward irrelevant context due to noise; and (3) a mutual information maximization objective, which guides the model to identify contexts that are most informative for next-item prediction. We validate the effectiveness of FAIR on four public benchmarks, demonstrating its superior performance compared to existing methods.