WWW 2024: The 2nd Workshop on Recommendation with Generative Models
Overview
The 2nd Workshop on Recommendation with Generative Models was held in conjunction with WWW 2024 (The ACM Web Conference). It focuses on the emerging intersection of generative models — particularly large language models (LLMs) — and recommender systems.
- Website: https://generative-rec.github.io/workshop/
- Resource type: Conference / Workshop
- Section: Conferences, Workshops
- Open-domain information access and conversational search
- Semantic understanding in recommender systems
- End-to-end architectures combining external knowledge sources with generative models
- Generative recommendation (Gen-Rec) and LLM-based personalization
- A unified perspective that brings scattered related work into a comparable framework
- Clear decomposition of method components: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms
- Reproducible benchmarks, datasets, and taxonomy tables that lower the entry barrier for new 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 trustworthiness, 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
- Workshop official site: https://generative-rec.github.io/workshop/
Background and Scope
In large-scale search, recommendation, and personalization systems, recommendation has long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline-based approaches treat retrieval, ranking, and generation as separate stages, which makes it difficult to meet users' combined needs for natural language interaction, multi-hop reasoning, and real-time knowledge in the LLM era. This workshop was established to systematically advance the theory and practice of this cross-disciplinary area.
Core topics include:
Key Themes
Typical Technical Pipeline
Methods in this area generally follow a four-step pattern:
1. Input and representation: encode queries, documents, and user context into dense/sparse representations or structured prompts 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, chained or run in parallel per task 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis 4. Inference strategies: single-round retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control
Insights for Search / Rec / Personalization
1. Architecture: cascaded retrieve-rerank-generate remains mainstream, but agentic paradigms are turning "when and how to retrieve" itself into a learnable object 2. Data: high-quality instruction data and click/session logs are both critical; synthetic data must guard 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 evaluation 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment — academic benchmarks alone are not enough
Limitations and Notes
Common limitations in this area include experiment scale bounded by GPU budgets, benchmark misalignment with real user distributions, English-centric data with unknown cross-lingual generalization, and security risks when agents operate over the open web. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal / fairness constraints for recommendation.
Related Entries
References
Glossary
| Term | Meaning | |------|---------| | IR | Information Retrieval | | RAG | Retrieval-Augmented Generation | | LTR | Learning to Rank | | nDCG | Normalized Discounted Cumulative Gain | | Agentic Search | Modeling search as sequential decision-making and tool use | | Gen-IR | Generative Information Retrieval |