SIGIR 2024 Workshop on eCommerce (ECOM24)
Overview
The SIGIR 2024 Workshop on eCommerce (ECOM24) is a workshop co-located with the SIGIR 2024 conference. It focuses on information retrieval research applied to e-commerce, where large-scale search, recommendation, and personalization systems face persistent challenges around efficiency, scalability, and user intent understanding.
| Field | Content | |-------|---------| | Title | SIGIR 2024 Workshop on eCommerce (ECOM24) | | Type | Conference / Workshop | | Link | https://dl.acm.org/doi/10.1145/3626772.3657988 | | Section | Conferences, Workshops |
Topic Scope
E-commerce search and recommendation systems traditionally split retrieval, ranking, and generation into separate pipeline stages. In the LLM era, users increasingly expect natural-language interaction, multi-hop reasoning, and up-to-date knowledge, which stresses these pipelines. Workshops in this space typically address:
- Open-domain information access and enterprise/product knowledge retrieval
- Conversational search and semantic understanding in recommendation
- End-to-end architectures combining external knowledge sources with generative models
- Interfaces with LLM tool calling, reinforcement learning, and multi-agent collaboration
- Open problems such as evaluation credibility, latency and cost, hallucination and safety, and cross-lingual / multimodal extension
- 2025 SIGIR Workshop on eCommerce
- CIKM 2024 1st Workshop on Multimodal Search and Recommendations
- EACL 2024 Workshop on Personalization of Generative AI Systems
- ICDM MMSR 2025
- Official record: ACM DL — https://dl.acm.org/doi/10.1145/3626772.3657988
- For organizers, accepted papers, and schedule, consult the workshop's official website and the linked ACM record.
*Note: the source page is an index entry and does not reproduce the full call for papers or accepted-paper list; the specifics above reflect the typical scope of this workshop series.*
Context in the SIGIR / RecSys Ecosystem
Neural information retrieval has evolved from BM25 through BERT cross-encoders, dense bi-encoder retrieval, and late interaction, toward generative retrieval and LLM-based agents. Each generation trades off efficiency, effectiveness, and maintainability. On the recommendation side, the field has moved from matrix factorization and deep CTR models to sequence Transformers and LLM-driven generative recommendation (Gen-Rec), contending with sparse user behavior, massive item catalogs, and multi-objective business goals.
RAG and agentic search extend external knowledge access from one-shot retrieval to iterative, verifiable, plannable processes — shifting evaluation from static nDCG toward task success rate, citation accuracy, and multi-hop reasoning-chain completeness.
Practical Takeaways for Practitioners
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but agentic paradigms treat retrieval count and strategy itself as learnable. 2. Data: high-quality instruction data and click/session logs are both critical; synthetic data requires safeguards against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction keeps widening; LLM-as-judge should be cross-validated with human evaluation. 4. Deployment: latency, cost, explainability, and safety are hard production constraints — do not optimize academic benchmarks alone.
Engineering Checklist
| Check | Question | Suggestion | |-------|----------|------------| | Data | Does training/index data contain PII? How are versions managed? | Partitioned indexes, anonymization, rollback-able embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascades with early stopping, caching hot queries, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source allowlists, adversarial detection, output filtering | | Cost | Per-query token and GPU cost? | Small-model routing, distillation, hybrid sparse+dense retrieval |