Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer

TL;DR

Experimental results from a large-scale industrial search system and multiple public datasets indicate that SA^2CRQ yields consistent improvements over existing baselines, particularly in cold-start retrieval scenarios.

Abstract

While semantic ID-based generative retrieval enables efficient end-to-end modeling in industrial applications, these methods face a persistent trade-off: head items are susceptible to ID collisions that negatively impact downstream tasks, whereas data-sparse tail items, including cold-start items, exhibit limited generalization. To address this issue, we propose the Anchored Curriculum with Sequential Adaptive Quantization (SA^2CRQ) framework. The framework introduces Sequential Adaptive Residual Quantization (SARQ) to dynamically allocate code lengths based on item path entropy, assigning longer, discriminative IDs to head items and shorter, generalizable IDs to tail items. To mitigate data sparsity, the Anchored Curriculum Residual Quantization (ACRQ) component utilizes a frozen semantic manifold learned from head items to regularize and accelerate the representation learning of tail items. Experimental results from a large-scale industrial search system and multiple public datasets indicate that SA^2CRQ yields consistent improvements over existing baselines, particularly in cold-start retrieval scenarios.

Figures (8)

• Figure 1: The framework of SA$^{2}$CRQ.
• Figure 2: Overall performance on Public Dataset.
• Figure 3: Performance under the sparse setting (w/o aug).
• Figure 4: The percent quantile of item count. RQ represents RQ-Kmeans.
• Figure 5: Total number of SIDs per method. RQ represents RQ-Kmeans.