This is the first year of the TREC Product search track. The focus this year
was the creation of a reusable collection and evaluation of the impact of the
use of metadata and multi-modal data on retrieval accuracy. This year we
leverage the new product search corpus, which includes contextual metadata. Our
analysis shows that in the product search domain, traditional retrieval systems
are highly effective and commonly outperform general-purpose pretrained
embedding models. Our analysis also evaluates the impact of using simplified
and metadata-enhanced collections, finding no clear trend in the impact of the
expanded collection. We also see some surprising outcomes; despite their
widespread adoption and competitive performance on other tasks, we find
single-stage dense retrieval runs can commonly be noncompetitive or generate
low-quality results both in the zero-shot and fine-tuned domain.

今年是 TREC 产品搜索跟踪的第一年。重点在于创建可重复使用集合，并评估元数据和多模态数据对检索准确性的影响。研究结果显示，在产品搜索领域，传统的检索系统效果非常好，通常优于通用的预训练嵌入模型。我们还评估了使用简化和元数据增强集合的影响，发现扩展集合的影响没有明显的趋势。我们还观察到一些令人惊讶的结果，尽管单阶段密集检索运行在其他任务上表现出色且被广泛采用，但在零 - shot 和精细调整领域中，常常不具竞争力或生成低质量结果。