  Generative retrieval (GR) has emerged as a transformative paradigm in search
and recommender systems, leveraging numeric-based identifier representations to
enhance efficiency and generalization. Notably, methods like TIGER employing
Residual Quantization-based Semantic Identifiers (RQ-SID), have shown
significant promise in e-commerce scenarios by effectively managing item IDs.
However, a critical issue termed the "\textbf{Hourglass}" phenomenon, occurs in
RQ-SID, where intermediate codebook tokens become overly concentrated,
hindering the full utilization of generative retrieval methods. This paper
analyses and addresses this problem by identifying data sparsity and
long-tailed distribution as the primary causes. Through comprehensive
experiments and detailed ablation studies, we analyze the impact of these
factors on codebook utilization and data distribution. Our findings reveal that
the "Hourglass" phenomenon substantially impacts the performance of RQ-SID in
generative retrieval. We propose effective solutions to mitigate this issue,
thereby significantly enhancing the effectiveness of generative retrieval in
real-world E-commerce applications.
