LightLT: A Lightweight Representation Quantization Framework for Long-Tail Data

Search tasks require finding items similar to a given query, making it a crucial aspect of various applications. However, storing and computing similarity for millions or billions of item representations can be computationally expensive. To address this, quantization-based hash methods present memory and inference-efficient solutions by converting continuous representations into non-negative integer codes. Despite their advantages, these methods often encounter difficulties in handling long-tail datasets due to imbalanced class distributions. To address this, we propose LightLT, a lightweight representation quantization framework tailored for long-tail datasets. LightLT produces compact codebooks and discrete IDs, enabling efficient inference by computing distances between query and codewords. Our framework includes innovative designs: 1) Quantization Step: We select the most similar codeword for continuous inputs using the differentiable argmax operation. 2) Double Skip Quantization Connection Module: This module promotes codebook diversity and stability during training. 3) Training Loss: Our comprehensive loss includes class-weighted cross-entropy, center loss, and ranking loss. 4) Model Ensemble: We incorporate a model ensemble step to improve generalization. Theoretical analysis confirms LightLT's low space and inference complexity. Experimental results demonstrate superior performance compared to state-of-the-art baselines in terms of search accuracy, efficiency, and memory usage.