Overview - Product Search and Recommendation 2025¶
Proceedings | Data | Runs | Participants
The TREC Product Search and Recommendations Track aims to advance research in product search and recommendation systems by creating robust, high-quality datasets that enable the evaluation of end-to-end multimodal retrieval and nuanced product recommendation algorithms.
Track coordinator(s):
- Surya Kallumadi, Coursera
- ChengXiang Zhai, University of Illinois Urbana-Champaign
- Michael Ekstrand, Drexel University
- Rikiya Takehi, Waseda University
- Dean Alvarez, University of Illinois Urbana-Champaign
- Daniel Campos, Snowflake
- Alessandro Magnini, WalmartLabs
Tasks:
trec2025-product-search: Product Searchtrec2025-product-rec: Product Recommendation
Track Web Page: https://trec-product-search.github.io/