Frontier Recommender Systems

The 1st Frontier Recommender Systems (FRecSys) workshop aims to provide a forum for the systems and recommendation communities to rethink the infrastructure foundations of modern recommender systems. Recommender systems are now core production infrastructure for online services, shaping user experience, content distribution, advertising, and e-commerce at global scale. They operate under strict latency, cost, reliability, and freshness constraints, while continuously adapting to changing users, content, models, policies, and business requirements.

For many years, recommender-system infrastructure has been shaped by retrieval-and-ranking pipelines, large embedding tables, feature stores, and DLRM-style serving architectures. These systems remain essential and continue to raise important systems challenges. At the same time, rapid advances in AI are creating new recommendation paradigms, including generative recommendation, foundation-model-based recommendation, multimodal recommendation, agentic recommendation, chain-of-thought recommendation, and interactive user modeling. These emerging workloads change both what recommender systems compute and how they must be served. They introduce new requirements for model execution, user-context management, online adaptation, system observability, resource scheduling, evaluation, and deployment at scale.

FRecSys seeks papers that examine how recommendation infrastructure should evolve for these frontier workloads. We welcome early ideas, position papers, preliminary research results, industry experience reports, benchmark proposals, and lessons from production deployments. The workshop aims to bring together researchers and practitioners from systems, machine learning, recommendation, databases, networking, hardware, and cloud infrastructure to identify open problems and community resources for the next generation of recommendation systems.

A further goal of the workshop is to encourage the development of shared artifacts for rigorous and reproducible research. Today, many important systems questions in recommender systems are difficult to study because public benchmarks, traces, and evaluation methodologies often fail to capture the scale, heterogeneity, dynamism, and operational constraints of production environments. FRecSys therefore particularly encourages submissions that expose realistic production constraints, characterize workloads that are difficult to capture in existing public benchmarks, formalize open systems problems for emerging recommendation platforms, or propose community resources such as representative traces, workload specifications, benchmark suites, and evaluation guidelines.

Main Topics

Topics of interest include, but are not limited to:

• Agentic, interactive, and reasoning-enhanced recommendation systems.
• Infrastructures for generative and agentic recommendation.
• Retrieval, ranking, and generative recommendation systems.
• Approximate nearest neighbor (ANN) search, vector indexing, and retrieval infrastructure.
• Multimodal, graph-based, sequence-based, and foundation-model-based recommenders.
• Training and serving systems for large-scale recommenders.
• Batching, scheduling, GPU acceleration, and tail-latency control.
• Embedding systems, feature stores, user-context management, and caching.
• Online learning, continual training, model refresh, and feature freshness.
• Reliable model deployment, rollout, rollback, and production experimentation.
• Streaming logs, feedback loops, user simulation, and benchmark construction.
• Reliability, observability, debugging, and incident response for recommendation services.
• Co-design across algorithms, accelerators, and serving platforms.
• Workload characterization, production traces, evaluation methodology, and reproducibility.