NEAR$^2$: A Nested Embedding Approach to Efficient Product Retrieval and Ranking

TL;DR

NEAR$^2$ introduces a nested embedding method that significantly reduces embedding size and computational cost in e-commerce product retrieval, while enhancing accuracy across various models and IR challenges.

Contribution

The paper presents NEAR$^2$, a novel nested embedding approach that improves efficiency and accuracy in product retrieval without additional training costs.

Findings

Up to 12 times smaller embeddings at inference

Improved retrieval accuracy across multiple models

Effective on diverse IR challenges and datasets

Abstract

E-commerce information retrieval (IR) systems struggle to simultaneously achieve high accuracy in interpreting complex user queries and maintain efficient processing of vast product catalogs. The dual challenge lies in precisely matching user intent with relevant products while managing the computational demands of real-time search across massive inventories. In this paper, we propose a Nested Embedding Approach to product Retrieval and Ranking, called NEAR$^2$, which can achieve up to $12$ times efficiency in embedding size at inference time while introducing no extra cost in training and improving performance in accuracy for various encoder-based Transformer models. We validate our approach using different loss functions for the retrieval and ranking task, including multiple negative ranking loss and online contrastive loss, on four different test sets with various IR challenges such as short and implicit queries. Our approach achieves an improved performance over a smaller embedding dimension, compared to any existing models.