NEAR$^2$: A Nested Embedding Approach to Efficient Product Retrieval and Ranking
This addresses the problem of efficient product retrieval and ranking for e-commerce systems, representing an incremental improvement with specific gains in efficiency and performance.
The paper tackles the challenge of achieving high accuracy and efficiency in e-commerce information retrieval by proposing NEAR$^2$, a nested embedding approach that improves accuracy while reducing embedding size by up to 12 times at inference with no extra training cost.
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.