Abstract

Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed …

Date

April 30, 2023

Authors

Hamed Bonab, Ashutosh Joshi, Ravi Bhatia, Ankit Gandhi, Vijay Huddar, Juhi Naik, Mutasem Al-Darabsah, Choon Hui Teo, Jonathan May, Tarun Agarwal, Vaclav Petricek

Book

Companion Proceedings of the ACM Web Conference 2023

Pages

869-877