  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.
