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From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
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From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search
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We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world. Specifically, we outline the design of a deep learning system that retrieves semantically relevant items to a query within milliseconds, and a pairwise deep re-ranking system, which learns subtle user preferences. Compared to traditional search systems, the proposed approaches are better at semantic retrieval and personalized ranking, achieving significant improvements.
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Cited by 1 Pith paper
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Semantic Retrieval for Product Search in E-Commerce
A dual-encoder LLM is trained via contrastive learning then ROAR to retrieve exact matches and rank substitutes in e-commerce search.
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