The international workshop on Online and Adaptive Recommender Systems (OARS) will serve as a platform for publication and discussion of OARS. It will bring together practitioners and researchers from academia and industry to discuss the challenges and new approaches to implement OARS algorithms and systems and improve user experiences by better modeling and responding to user intent.

Recommender systems (RecSys) play a pivotal role in helping users navigate, discover, and consume massive and ever-changing information. Yet most deployed systems still rely on static user profiles and pre-computed recommendation actions that fail to adapt within or across sessions. As personalization demands intensify, privacy regulations tighten, and user intent grows more dynamic, the need for online and adaptive recommender systems has never been greater.

We invite submission of papers and posters, representing original research, new position and opinion, preliminary results, proposals for new tools, datasets, and resources. All submitted papers will be single-blind and will be peer reviewed by an international program committee of researchers of high repute. Accepted submissions will be presented at the workshop.

Topics of interest include, but are not limited to:

• Agentic recommender systems, assistant-style interfaces, memory and tool-use (2026 special theme)
• LLMs and foundation models in RecSys: semantic IDs, tokenization, multi-modality, in-context learning
• Online and continual learning, reinforcement learning, bandits, and counterfactual evaluation
• Real-time user intent modeling, session-aware and conversational recommendation
• Cold-start, distribution shift, and robustness under data sparsity
• Predictive analytics and causal inference for recommendation
• New architectures: RAG-based, streaming and event-driven, scalable learning
• Evaluation, explanation, and off-policy methods for OARS
• Privacy, ethics, fairness, and user welfare in OARS
• Industry deployments, infrastructure, and real-world case studies