The eCommerce search and recommendations space is a unique, dynamic domain within information retrieval (IR), characterized by multimodality and industry-driven challenges. While the basic task of fulfilling a user's information need aligns with web search, the methodologies employed are distinct. On eCommerce platforms, the data available for retrieval and ranking differs significantly, as do the success signals (e.g.\ adding items to a cart, purchasing). The special theme of ECOM26 is User Interaction and Experience: Agentic-driven Trends. Our focus for 2026 is on fostering deeper engagement through interactive discussions, exploring crucial topics such as shifts in user interaction paradigms, and addressing emerging topics such as evaluation metrics for LLMs, multimodality, and the interplay between organic and sponsored search. With our discussion-heavy format and structured facilitation, we aim to spark conversation among all participants, beyond that of the usual interactions between presenters and audience questions.

The eCommerce search and recommendations space is a unique and dynamic domain within information retrieval (IR), characterized by its multimodality and industry-driven challenges.While the basic task of fulfilling a user's information need aligns with web search, the methodologies employed are distinct.On eCommerce platforms (e.g.Alibaba, Amazon, eBay, Etsy, Flipkart, Walmart), the data available for retrieval and ranking differs significantly, as do the success signals (e.g.adding items to a cart, purchasing).Our focus for 2025 is on fostering deeper engagement through interactive discussions, exploring crucial topics such as navigating irreproducibility in research-to-product pipelines, and addressing emerging topics such as evaluation metrics for LLMs, multimodality, and the interplay between organic and sponsored search.With our discussion-heavy format and structured facilitation, we aim to spark conversation among all participants.

With the advent of multimodal LLMs and release of open-source multimodal models, the potential for multimodal search and recommendations has significantly increased. Multimodal systems offer a next-gen customer experience by creating a shared embedding space for text, images, audio, etc. These advancements enable more accurate, personalized recommendations, enhancing user satisfaction and engagement. This workshop on Multimodal Search and Recommendations explores the latest advancements, challenges, and applications of multimodal search and recommendations.

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the largest web sites (e.g. Airbnb, Alibaba, Amazon, eBay, Facebook, Flipkart, Lowes's, Taobao, Target). SIGIR has for several years seen sponsorship from eCommerce organizations, 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 behavior data to improve search relevance, and (3) to examine how to build datasets and evaluate algorithms in this domain.

Search has become an integral part of Adobe products and users rely on it to learn about tool usage, shortcuts, quick links, and ways to add creative effects and to find assets such as backgrounds, templates, and fonts. Within applications such as Photoshop and Illustrator, users express domain-specific search intents via short text queries. In this work, we leverage sentence-BERT models fine-tuned on Adobe's HelpX data to perform multi-lingual semantic search on help and tutorial documents. We used behavioral data (queries, clicks, and impressions) and additional annotated data to train several BERT-based models for scoring query-document pairs for semantic similarity. We benchmarked the keyword-based production system against semantic search. Subsequent AB tests demonstrate that this approach improves engagement for longer queries while reducing null results significantly.

Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either lower accuracy than state-of-the-art solutions or too slow to be used for search use cases where latency is a key requirement. Furthermore, most innovative recent architectures focus on English and are not trained in a multilingual fashion and are trained for spell correction in longer text, which is a different paradigm from spell correction for user queries, where context is sparse (most queries are 1-2 words long). Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users' needs.

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.

Search engines for domain-specific media collections often rely on rich metadata being available for the content items. The annotations may not be complete or rich enough to support an adequate retrieval effectiveness. As a result, some search queries receive only a small result set (low recall) and others might suffer from reduced relevance (low precision). To alleviate this, we present a framework that exploits external knowledge to provide entity-oriented reformulation suggestions for queries that contain entities. We propose that queries be added as surrogate nodes to an external Knowledge Graph (KG) via the use of state-of-the-art entity linking algorithms. Embedding methods are invoked on the augmented graph, which contains additional edges between surrogate nodes and KG entities. We introduce a new evaluation setting to evaluate the quality of these embeddings. Experimental results on seven datasets confirm the effectiveness of the approach.

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.

eCommerce Information Retrieval (IR) is receiving increasing attention in the academic literature and is an essential component of some of the largest web sites (e.g. Amazon, Alibaba, Taobao, eBay, Airbnb, Target, Facebook). eCommerce organisations consistently sponsor SIGIR, reflecting the importance of IR research to them. This workshop (1) brings together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) determines how to use eCommerce's unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) examines how to build data sets and evaluate algorithms in this domain. Since eCommerce customers often do not know exactly what they want to buy, recommendations are valuable for inspiration, serendipitous discovery and basket building. The theme of this year's eCommerce IR workshop is integrating recommendations into search for eCommerce. In addition to the focus on recommender systems in eCommerce search, Rakuten France is sponsoring a data challenge on taxonomy classification using multi-modal (image, text and structured data) input. The data challenge reflects themes from the 2017--2019 SIGIR workshops.