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 (The ACM Web Conference). It provides a dedicated venue for research at the intersection of generative models (including large language models) and recommender systems.

• Event: The 2nd Workshop on Recommendation with Generative Models @ WWW 2024
• Website: https://generative-rec.github.io/workshop/
• Category: Conference / Workshop

Background and Scope

Large-scale search, recommendation, and personalization systems have long faced challenges in efficiency, scalability, and user-intent understanding. Traditional pipeline-based approaches often split retrieval, ranking, and generation into disconnected stages, making it difficult to meet modern user expectations around 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 intersection.

Core topics in this problem space include:

• Open-domain information access and enterprise knowledge retrieval
• Conversational search and semantic understanding in recommendation
• End-to-end architectures that combine external knowledge sources with generative models

Key Themes

• Unified perspectives that bring scattered related work into a comparable framework
• Component-level decomposition of methods: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms
• Reproducible protocols — benchmarks, datasets, and taxonomies that lower the entry cost for new researchers
• Interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, including paths from research prototypes to industrial systems
• Open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal extension

Typical Technical Pipeline

Work in this area generally follows 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 composed 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 making retrieval count and strategy themselves learnable 2. Data: high-quality instruction data and click/session logs are as critical as models; 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. Production: latency, cost, interpretability, and safety policies are hard constraints for industrial deployment — academic benchmarks alone are insufficient

Limitations and Notes

Typical limitations in this research area include experimental scale constrained by GPU budgets, benchmarks that mismatch real user distributions, English-centric data with unknown cross-lingual generalization, and safety risks of agentic systems on the open web. Promising future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation.

References

• Workshop website: https://generative-rec.github.io/workshop/
• 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