WWW 2024: The 2nd Workshop on Recommendation with Generative Models
Overview
The 2nd Workshop on Recommendation with Generative Models was organized in conjunction with WWW 2024. Full details, including the agenda, speakers, and accepted papers, are available on the official workshop website: 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 tend to treat retrieval, ranking, and generation as separate stages, making it difficult to satisfy users' LLM-era expectations for natural language interaction, multi-hop reasoning, and real-time knowledge.
Against this backdrop, the workshop aimed to systematically review and advance the theoretical and practical boundaries of the intersection between generative models and recommendation. Core application scenarios include:
- Open-domain information access
- Enterprise knowledge retrieval
- Conversational search
- Semantic understanding in recommender systems
- End-to-end architectures that couple external knowledge sources with generative models
- A unified perspective for the problem domain, bringing scattered related work into a comparable framework
- A clear decomposition of method components (representation learning, retrievers, rerankers, planners, generators, feedback mechanisms) to ease engineering adoption
- Reproducible benchmarks, datasets, or taxonomies that lower the entry barrier 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 trustworthiness, latency and cost, hallucination and safety, and 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
- Official page: https://generative-rec.github.io/workshop/
Core Topics
Typical Technical Pipeline
Workshop papers generally follow a four-step pattern: problem formalization → model/system design → training or construction → inference pipeline.
1. Input and representation: encode queries, documents, and user context into dense or sparse representations, or construct structured prompts 2. Core modules: retrievers, rerankers, planners, memory modules, tool interfaces, chained or parallelized per task 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), bootstrapped data synthesis 4. Inference strategies: single-round retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control
Key Insights for Search / Rec / Personalization
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but the agentic paradigm is turning "how many retrieval steps and what policy" 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 and cannot be sacrificed for academic benchmarks alone
Limitations and Notes
Possible limitations include experimental scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, unknown cross-lingual generalization due to English-centric data, and safety 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 and fairness constraints for recommender systems.