SIGIR 2024 Workshop on eCommerce (ECOM24)

Overview

| Field | Content | |------|------| | Title | SIGIR 2024 Workshop on eCommerce (ECOM24) | | Link | https://dl.acm.org/doi/10.1145/3626772.3657988 | | Type | Conference / Workshop | | Section | Conferences, Workshops |

ECOM24 is the SIGIR 2024 Workshop on eCommerce, addressing information retrieval challenges in large-scale search, recommendation, and personalization systems — including efficiency, scalability, and user intent understanding.

Core Topics

• A unified perspective on retrieval, ranking, and generation for e-commerce, organizing scattered prior work into a comparable framework.
• Decomposition of method components: representation learning, retrievers, re-rankers, planners, generators, and feedback mechanisms.
• Reproducible benchmarks, datasets, and taxonomies that lower the entry cost for future researchers.
• Interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, and paths from research prototypes to industrial systems.
• Open problems: evaluation credibility, latency and cost, hallucination and safety, and cross-lingual / multimodal scaling.

Typical Method Pipeline

1. Input & representation: encode queries, documents, and user context into dense/sparse representations or structured prompts. 2. Core modules: retrievers, re-rankers, planners, memory modules, and tool interfaces arranged in series or parallel. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.

Position within the SIGIR / RecSys Ecosystem

Neural IR has evolved from BM25 through BERT cross-encoders, dual-tower dense retrieval, and late interaction, to generative retrieval and LLM agents. Each generation balances the efficiency–effectiveness–maintainability triangle. On the recommendation side, the shift from matrix factorization and deep CTR models to sequence Transformers and LLM-based generative recommendation (Gen-Rec) must contend with sparse user behavior, huge item catalogs, and multi-objective business trade-offs. 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.

Key Insights for Search / Rec / Personalization

1. Architecture: cascaded retrieve + re-rank + generate remains mainstream, but agentic paradigms make retrieval count and policy themselves learnable. 2. Data: high-quality instruction data and click/session logs are equally critical; synthetic data requires safeguards against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge needs cross-validation with human assessment. 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment — academic benchmarks alone are insufficient.

Engineering Checklist

| Item | Question | Recommendation | |--------|------|------| | Data | Does training/indexing contain PII? How is versioning handled? | Partitioned indexes, anonymization, rollback-capable embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascading + early stop, cache hot queries, async re-ranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, manual audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source whitelisting, adversarial detection, output filtering | | Cost | Per-query token and GPU usage? | Route to smaller models, distillation, hybrid sparse+dense |

Glossary

| Term | Meaning | |------|------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain, a ranking quality metric | | Agentic Search | Modeling search as sequential decision-making and tool invocation | | Gen-IR | Generative Information Retrieval |

Related Entries

• 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

Notes

This entry is indexed in the forum's Awesome List under Conferences/Workshops. Readers can locate complementary literature along the retrieve → rank → generate/agent → evaluate pipeline. Note that the workshop's own abstract text was not available in the source post; details beyond the metadata come from the entry's contextual discussion. Readers are advised to treat conclusions as directional evidence and validate via shadow experiments and A/B testing before full production rollout.