Beyond Hard Negatives in Product Search: Semantic Matching Using One-Class Classification (SMOCC)
Arindam Bhattacharya, Ankit Gandhi, Vijay Huddar, M S Ankith, Aayush Moroney, Atul Saroop, Rahul Bhagat · 2023
Semantic matching is an important component of a product search pipeline. Its goal is to capture the semantic intent of the search query as opposed to the syntactic matching performed by a lexical matching system. A semantic matching model captures relationships like synonyms, and also captures common behavioral patterns to retrieve relevant results by generalizing from purchase data. They however suffer from lack of availability of informative negative examples for model training. Various methods have been proposed in the past to address this issue based upon hard-negative mining and contrastive learning.