WWW 2024: The 2nd Workshop on Recommendation with Generative Models
Overview
- Event: The 2nd Workshop on Recommendation with Generative Models at WWW 2024
- Website: https://generative-rec.github.io/workshop/
- Type: Conference Workshop
- Open-domain information access
- Enterprise knowledge retrieval
- Conversational search
- Semantic understanding in recommender systems
- End-to-end architectures that combine external knowledge sources with generative models
- A unified perspective for the field, bringing scattered work into a comparable framework
- Clear decomposition of method components (representation learning, retrievers, rerankers, planners, generators, feedback mechanisms) for engineering practice
- Reproducible benchmarks, datasets, and taxonomies to lower the entry cost for follow-up research
- Interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, and migration paths from research prototypes to industrial systems
- Open problems: evaluation credibility, latency and cost, hallucination and safety, cross-lingual and 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 homepage: 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 approaches often treat retrieval, ranking, and generation as separate stages, making it hard to meet the LLM-era demands for natural language interaction, multi-hop reasoning, and real-time knowledge. This workshop was proposed in this context, aiming to systematically consolidate and advance the theory and practice of generative models in recommendation.
Core topics include:
Key Themes
Typical Technical Pipeline
Work in this area generally follows a four-step pattern: problem formulation → model/system design → training or construction → inference pipeline.
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, tool interfaces, chained or run in parallel 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), self-bootstrapped data synthesis 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping and budget control
Insights for Search / Rec / Personalization
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but the agentic paradigm treats the number and strategy of retrievals itself as a learnable object 2. Data: high-quality instruction data and click/session logs are equally 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 against human evaluation 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment—academic benchmarks alone are not enough
Limitations
Possible limitations include experiment scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, unknown cross-lingual generalization due to English-centric data, and security risks of agentic systems on 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.