Over the past few years, consumer behavior has shifted from traditional in-store shopping to online shopping. For example, eCommerce sales have grown from around 5% of total US sales in 2012 to around 15.4% in year 2023. This rapid growth of eCommerce has created new challenges and vital new requirements for intelligent information retrieval systems.

Scalable systems

Since the pandemic hit, eCommerce became an important part of people’s routine and they started using online shopping for smallest grocery items to big electronics as well as cars. With such a large assortment of products and millions of users, achieving higher scalability without losing accuracy is a leading concern for information retrieval systems for eCommerce.

Interactive Systems

The diverse buyers make the relevance of the results highly subjective, because relevance varies for different buyers. The most suitable and intuitive solution to this problem is to make the system interactive and provide correct relevance for different users. Hence, interactive information retrieval systems are becoming necessity in eCommerce.

System improvement

To handle sudden change in buyers’ behavior, industries adopted existing sub-optimal in- formation retrieval techniques for various eCommerce tasks. Parallelly, they also started exploring/researching for better solutions and in dire need of help from research community

The objective of this workshop is to bring a diverse set of practitioners and researchers together and encourage them to share their ideas, challenges & solutions and research. This workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce.

This workshop will provide a forum to discuss and learn the latest trends for interactive and scalable information retrieval approaches for eCommerce. It will provide academic and industrial researchers a platform to present their latest works, share research ideas, present and discuss various challenges, and identify the areas where further research is needed. It will foster the development of a a research community focused on solving eCommerce-related information retrieval problems that provide superior eCommerce experience to all users.

Topics of interest include, but are not limited to:

• LLMs & GenAI in eCommerce
• LLMs for overall query understanding
• Product review summary using LLMs
• LLMs for recommendations
• GenAI for query reformulation
• GenAI for IR evaluations
• Query Understanding
• Type-ahead/auto-completion, spell correction
• Non-text query understanding
• Attribute understanding
• Implicit query intent understanding
• Product Understanding
• Product intent and facets
• Product knowledge graph
• Ontology mining for product graph construction
• Product Retrieval and Ranking
• Product indexing and recall
• Scalable and real-time indexing for frequently changing products (offers, auctions, etc.)
• Recall and Ranking for multi-faceted products and multi-attributed queries
• Ranking for Relevance vs Popularity vs Business trade-offs
• Search Re-Ranking
• Personalization and Recommendation
• Interactive Search for personalization
• Product Question Answering
• Context and/or location based personalization
• User attribute based personalization
• Personalized and Semantic Retrieval
• Cross-domain learning
• Transfer learning for NLP
• Cross-domain conversational search system
• Transfer learning for cross-domain product ranking
• Multi-modal conversational systems for eCommerce
• Other Topics
• Feature learning for eCommerce search
• Search & Recommendations: Fairness and trust for marketplaces
• Balancing sponsorship vs relevance tread off in search results
• Robust training objective and effective experimental strategy for IR models
• End-to-End solution for interactive and scalable search framework