Multi-word Term Embeddings Improve Lexical Product Retrieval
This work addresses product retrieval for e-commerce platforms by enhancing search precision through multi-word term handling, representing an incremental improvement over existing methods.
The paper tackles product search by developing the H1 embedding model for offline term indexing of product descriptions, which processes multi-word terms as single tokens to improve precision without affecting recall. The hybrid system with H1 achieves mAP@12 = 56.1% and R@1k = 86.6% on the WANDS dataset, outperforming other state-of-the-art models.
Product search is uniquely different from search for documents, Internet resources or vacancies, therefore it requires the development of specialized search systems. The present work describes the H1 embdedding model, designed for an offline term indexing of product descriptions at e-commerce platforms. The model is compared to other state-of-the-art (SoTA) embedding models within a framework of hybrid product search system that incorporates the advantages of lexical methods for product retrieval and semantic embedding-based methods. We propose an approach to building semantically rich term vocabularies for search indexes. Compared to other production semantic models, H1 paired with the proposed approach stands out due to its ability to process multi-word product terms as one token. As an example, for search queries "new balance shoes", "gloria jeans kids wear" brand entity will be represented as one token - "new balance", "gloria jeans". This results in an increased precision of the system without affecting the recall. The hybrid search system with proposed model scores mAP@12 = 56.1% and R@1k = 86.6% on the WANDS public dataset, beating other SoTA analogues.