  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.
