New model DSIRM boosts e-commerce search relevance with discrete identifiers

Researchers have developed a new model called DSIRM to improve e-commerce search relevance by learning discrete semantic identifiers. This approach uses query-item interaction supervision to create relevance-aware item partitions and leverages generative LLMs to predict item identifiers from text. When deployed on Tmall's production data, DSIRM significantly improved offline AUC by 1.54% and showed positive online lifts in user click-through and conversion rates. AI

影响 Enhances e-commerce search relevance through learned discrete identifiers, potentially improving user experience and conversion rates.

排序理由 The cluster contains a research paper detailing a new model and its experimental results. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• DSIRM
• Tmall

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