Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem. However, existing generative methods typically focus solely on either behavioral or semantic aspects of item information, neglecting their complementary nature and thus resulting in limited effectiveness. To address this limitation, we introduce EAGER, a novel generative recommendation framework that seamlessly integrates both behavioral and semantic information. Specifically, we identify three key challenges in combining these two types of information: a unified generative architecture capable of handling two feature types, ensuring sufficient and independent learning for each type, and fostering subtle interactions that enhance collaborative information utilization. To achieve these goals, we propose (1) a two-stream generation architecture leveraging a shared encoder and two separate decoders to decode behavior tokens and semantic tokens with a confidence-based ranking strategy; (2) a global contrastive task with summary tokens to achieve discriminative decoding for each type of information; and (3) a semantic-guided transfer task designed to implicitly promote cross-interactions through reconstruction and estimation objectives. We validate the effectiveness of EAGER on four public benchmarks, demonstrating its superior performance compared to existing methods.

翻译：生成式检索最近已成为序列推荐中一种有前景的方法，它将候选物品检索构建为一个自回归序列生成问题。然而，现有的生成式方法通常只关注物品信息的行为方面或语义方面，忽视了二者的互补性，从而导致效果有限。为了克服这一局限，我们提出了EAGER，一个新颖的生成式推荐框架，它能无缝整合行为信息和语义信息。具体而言，我们识别了结合这两类信息所面临的三个关键挑战：需要一个能够处理两种特征类型的统一生成架构，确保每种类型都能得到充分且独立的学习，以及促进能够增强协同信息利用的微妙交互。为实现这些目标，我们提出了（1）一种双流生成架构，它利用共享编码器和两个独立的解码器，通过基于置信度的排序策略来解码行为标记和语义标记；（2）一个带有摘要标记的全局对比任务，以实现对每类信息的判别式解码；以及（3）一个语义引导的迁移任务，旨在通过重构和估计目标来隐式地促进跨模态交互。我们在四个公开基准上验证了EAGER的有效性，证明了其相对于现有方法的优越性能。