eCom'22: The SIGIR 2022 Workshop on eCommerce

Ajinkya Kale, Surya Kallumadi, Tracy Holloway King, Shervin Malmasi, Maarten de Rijke, Jacopo Tagliabue

Abstract: eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the world's largest web sites (e.g. Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowe's, Taobao, and Target). SIGIR has for several years seen sponsorship from eCommerce organisations, reflecting the importance of IR research to them. The purpose of this workshop is (1) to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) to determine how to use eCommerce's unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain. Since eCommerce customers often do not know exactly what they want to buy (i.e. navigational and spearfishing queries are rare), recommendations are valuable for inspiration and serendipitous discovery as well as basket building. The theme of this year's eCommerce IR workshop is Bridging IR Metrics and Business Metrics and Multi-objective Optimization. The workshop includes papers on this topic as well as a panel focused on this area (see Section 3). In addition, Farfetch is sponsoring a recommendation challenge focused on outfit completion: as part of the event, Farfetch will release to the research community a novel, large dataset containing multi-modal information and extensive labels curated by fashion experts. The data challenge reflects themes from prior SIGIR workshops in 2017, 2018, 2019, 2020, 2021.