KDD 2024 Workshop on Generative AI for Recommender Systems and Personalization
This entry indexes the KDD 2024 Workshop on Generative AI for Recommender Systems and Personalization.
- Official site: https://genai-personalization.github.io/GenAIRecP2024
- Type: Conference / Workshop
- Category: Conferences, Workshops
- Motivation: Large-scale search, recommendation, and personalization systems have long faced challenges in efficiency, scalability, and user-intent understanding. Traditional pipelines that treat retrieval, ranking, and generation as separate stages struggle to meet LLM-era demands for natural-language interaction, multi-hop reasoning, and up-to-date knowledge.
- Scope: Open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommendation, and end-to-end architectures that couple external knowledge sources with generative models.
- A unified perspective that organizes scattered related work into a comparable framework.
- Clear decomposition of method components: representation learning, retrievers, rerankers, planners, generators, and feedback mechanisms — easing engineering adoption.
- Reproducible benchmarks, datasets, and taxonomies that lower the entry cost for follow-up research.
- Discussion of interfaces with emerging paradigms: LLM tool calling, reinforcement learning, and multi-agent collaboration, including paths from research prototypes to industrial systems.
- Explicit open problems: evaluation credibility, 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
Key points
Core themes and contributions
Typical technical pipeline
Methods usually follow four steps:
1. Input & 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, connected in series or parallel per task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, RL (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-shot retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Insights for search / recommendation / personalization
1. Architecture: cascaded retrieve–rerank–generate remains mainstream, but the agentic paradigm makes *retrieval count and policy* itself learnable. 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 should be cross-validated with human evaluation. 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment — do not optimize only academic benchmarks.
Engineering checklist
| Check | Question | Suggestion | |-------|----------|------------| | Data | Does training/indexing contain PII? How are versions managed? | Partitioned indexes, anonymization, rollback-capable embedding versions | | Latency | What is the p99 budget? How many retrieval steps? | Cascades + early stopping, hot-query caching, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, human audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source whitelisting, adversarial detection, output filtering | | Cost | Per-query tokens and GPU usage? | Small-model routing, distillation, hybrid sparse+dense |
Related entries
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 calling | | Gen-IR | Generative Information Retrieval |
Limitations
As with many workshop-scale efforts, experiments may be limited by GPU budgets, benchmarks may mismatch real user distributions, English-centric data leaves cross-lingual generalization unknown, and agentic systems carry safety risks on the open web. Readers should treat conclusions as directional evidence and validate via shadow deployments and A/B tests before full production rollout.