SIGIR eCom

2025 SIGIR Workshop on eCommerce


Padua, Italy


Previous editions: SIGIReCom'24 | SIGIReCom'23 | SIGIReCom'22 | SIGIReCom'21 | SIGIReCom'20 | SIGIReCom'19 | SIGIReCom'18 | SIGIReCom'17

The special theme of this year's workshop is From Research to Product: Challenges, Lessons, and Opportunities in eCommerce Search and Recommendations

Overview

The SIGIR Workshop on eCommerce will serve as a platform for publication and discussion of Information Retrieval, NLP, and Computer Vision research relative to their applications in the domain of eCommerce. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in eCommerce.

Recently, the rapid evolution of emerging technologies, including large language models (LLMs) and generative AI, has introduced fresh opportunities and challenges for eCommerce search and recommendations. As many organizations race to adopt new technology, the knowledge required to tackle the practical challenges of productionization risks becoming siloed. Avoiding this requires continued dialogue between IR academia and industry, and across various eCommerce organizations. The goal of our workshop is to bridge these gaps and foster collaboration for this crucial exchange of information.

To support this goal, the special theme of ECOM25 is From Research to Product: Challenges, Lessons, and Opportunities in eCommerce Search and Recommendations.

SIGIR eCom is a full day workshop taking place on Thursday, July 17, 2025 in conjunction with SIGIR 2025. SIGIR eCom'25 will be an in-person workshop.

Important Dates

Paper submission deadline April 23, 2025 (11: 59 P.M. AoE)
Notification of acceptance May 21, 2025
SIGIR eCom Full day Workshop July 17, 2025

Call For Papers

We invite quality research contributions, position and opinion papers addressing relevant challenges in the domain of eCommerce. We invite submission of papers and posters representing original research, preliminary research results, proposals for new work, position and opinion papers. All submitted papers and posters will be single-blind and will be peer reviewed by an international program committee of researchers of high repute. Accepted submissions will be presented at the workshop.

Topics of interest include, but are not limited to:

  • From Research to Product: Challenges, Lessons, and Opportunities in eCommerce Search and Recommendations. (2025 special theme)
    • Solutions to practical challenges encountered while translating research into scalable products
    • Lessons from tackling irreproducibility while implementing well-known research papers into industrial eCommerce search and recommendation systems
    • Emerging trends in eCommerce IR, such as multimodality, customer lifetime value considerations, and online and offline usage of LLMs
    • Evaluating production effectiveness, measuring business impact, monitoring the quality of production models
  • Ranking and Whole Page Relevance (WPR)
    • Optimization for IR and business metrics
    • Diversity in product search and recommendations
    • Relevance models for multi-faceted entities
    • Relevance vs. revenue
    • Deterministic sorts (e.g. price low to high)
    • Temporal dynamics and seasonality
  • Query and Document Understanding
    • Query intent, query suggestions, and auto-completion
    • Strategies for resolving low or zero recall queries
    • Cross-modal search (e.g., text, structured data, images)
    • Categorization and facets
    • Reviews and sentiment analysis
  • Recommendation and Personalization
    • Personalization & contextualization
    • Privacy, bias and ethics in eCommerce IR
    • Blending recommendations, sponsored products and search results
    • Representations and Data
    • Semantic representation of products, queries, and customers
    • Construction and use of knowledge graphs for eCommerce
  • IR Fundamentals for eCommerce
    • Unified and universal search and recommendations
    • Cross-lingual search and machine translation
    • Indexing and search in rapidly changing environments (e.g., auction sites)
    • Experimentation techniques including AB testing and multi-armed bandits
  • Other challenges
    • Trust, transparency, and fairness in eCommerce
    • UX for eCommerce
    • Question answering and chatbots for eCommerce

Submission Instructions

All papers will be peer reviewed (single-blind) by the program committee and judged by their relevance to the workshop, especially to the main themes identified above, and their potential to generate discussion. All submissions must be in PDF formatted according to the latest CEUR single column format available at https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-workshop-proceedings-ceur-ws-dot-org/wqyfdgftmcfw. The short (8-page) and long (15-page) limits are extended to account for this.

For instructions and LaTeX/Overleaf/docx templates, see: https://ceur-ws.org/HOWTOSUBMIT.html#CEURART Read up to and including the “License footnote in paper PDFs” section. Please Use Emphasizing Capitalized Style for Paper Titles.

Submissions must describe work that is not previously published, not accepted for publication elsewhere, and not currently under review elsewhere. All submissions must be in English. The workshop follows a single-blind reviewing process. We do not accept anonymized submissions. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper.

Long paper limit: 15 pages. References are not counted in the page limit.
Short paper limit: 8 pages. References are not counted in the page limit.

Submissions to SIGIR eCom should be made through OpenReview at: https://openreview.net/group?id=ACM.org/SIGIR/2025/Workshop/eCom

Make sure to sign up for an OpenReview account ahead of time!

  • New profiles created without an institutional email will go through a moderation process that can take up to two weeks.
  • New profiles created with an institutional email will be activated automatically.