KDD 2024 Workshop on Generative AI for Recommender Systems and Personalization
Overview
| Field | Details | |-------|---------| | Title | KDD 2024 Workshop on Generative AI for Recommender Systems and Personalization | | Link | https://genai-personalization.github.io/GenAIRecP2024 | | Type | Conference / Workshop | | Section | Conferences, Workshops |
Background and Scope
Large-scale search, recommendation, and personalization systems have long faced challenges in efficiency, scalability, and user-intent understanding. Traditional pipeline approaches treat retrieval, ranking, and generation as disconnected stages, which struggles to meet the demands of the LLM era: natural-language interaction, multi-hop reasoning, and up-to-date knowledge. This workshop was organized to systematically map and advance the theory and practice at this intersection.
Core scenarios of interest include open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommenders, and end-to-end architectures that coordinate external knowledge sources with generative models.
Key Topics and Contributions
- A unified perspective that organizes scattered related work into a comparable framework.
- A clear decomposition of method components (representation learning, retrievers, rerankers, planners, generators, feedback mechanisms) for engineering practice.
- Reproducible benchmarks, datasets, and taxonomies that lower the entry barrier for follow-up research.
- Interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration, pointing to paths from research prototypes to industrial systems.
- Explicit open problems: evaluation credibility, latency and cost, hallucination and safety, cross-lingual and multimodal extension.
- 2025 SIGIR Workshop on eCommerce
- Activate
- CIKM 2024 1st Workshop on Multimodal Search and Recommendations
- EACL 2024 Workshop on Personalization of Generative AI Systems
- Haystack
- ICDM MMSR 2025
- Official page: KDD 2024 Workshop on Generative AI for Recommender Systems and Personalization — https://genai-personalization.github.io/GenAIRecP2024
Typical Method Pipeline
1. Input and representation: encode queries, documents, and user context as dense or sparse representations, or build structured prompts. 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, connected in series or in parallel per task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrap data synthesis. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Key Insights for Search / Rec / Personalization
1. Architecture: cascade retrieval + rerank + generation remains mainstream, but the agentic paradigm is making "how many retrievals and what policy" itself 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 with human evaluation. 4. Product: latency, cost, explainability, and safety policies are hard constraints for industrial deployment—academic benchmarks alone are insufficient.
Engineering Checklist
| Item | 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? | Cascade + early stopping, caching hot queries, async reranking | | Quality | Do offline gains translate to online CTR/satisfaction? | Interleaving experiments, manual audits, citation verification | | Safety | Does open retrieval introduce poisoning/bias? | Source whitelists, adversarial detection, output filtering | | Cost | Token and GPU cost per query? | Route to smaller models, distillation, hybrid sparse+dense retrieval |
Limitations
Potential limitations include experiment scale constrained by GPU budgets, benchmarks misaligned with real user distributions, English-centric data leaving cross-lingual generalization unknown, 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/fairness constraints for recommenders.