Problem
Research questions and friction points this paper is trying to address.
product search
recommendation
high-quality dataset
end-to-end retrieval quality
related-product recommendation
Overview of the TREC 2025 Product Search and Recommendation Track
🤖 AI Summary
为解决电商中缺乏高质量数据集评估检索质量的问题，通过2025 TREC产品搜索和推荐任务，采用查询扩展和相关产品推荐方法。
📝 Abstract
In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimaginable for in-person shopping. As product catalogs have grown in diversity and size product search and recommendation have become a cornerstone for e-commerce sites.
Despite the widespread usage of search engines in e-commerce, there is no high-quality dataset designed to evaluate end-to-end retrieval quality. In 2025, we ran a revised and continued version of the Product Search track previously run at TREC 2023 and TREC 2024. The 2025 product search track had two tasks: query expansion and related-product recommendation. The related-product recommendation task is particularly novel, providing an annotated data set of product relationships that distinguishes between complementary and related products. We anticipate the data from this track will enable better recommendation and search applications that reflect user needs, as a building block for conversational product discovery experiences.
Innovation
Methods, ideas, or system contributions that make the work stand out.
query expansion
related-product recommendation
end-to-end retrieval quality
🔎 Similar Papers
2024-09-05arXiv.orgCitations: 0
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