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 system (RecSys) plays important roles in helping users navigate, discover, and consume massive and highly-dynamic information. Today, many RecSys solutions deployed in the real world rely on categorical user profiles and/or pre-calculated recommendation actions that stay static during a user session. However, recent trends suggest that RecSys need to model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. There are three primary drivers for this emerging need of online adaptation. First, in order to meet the increasing demand for a better personalized experience, the personalization dimensions and space will grow larger and larger. It would not be feasible to pre-compute recommended actions for all personalization scenarios beyond a certain scale. Second, in many settings the system does not have user prior history to leverage. Estimating user intent in real time is the only way to personalize. As various consumer privacy laws tighten, it is foreseeable that many businesses will reduce their reliance on static user profiles. Therefore, it makes the modeling of user intent in real time an important research topic. Third, a user’s intent often changes within a session and between sessions, and user behavior could shift significantly during dramatic events. Therefore, it is important to investigate more on online and adaptive recommender systems (OARS) that can adapt in real time to meet user needs and be robust against distribution shifts.

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:

• (multi-modal) LLMs in RecSys (2025 special theme)
  • Conversational and interactive RecSys using LLMs
  • Humanized and adaptive recommendation experiences with LLMs
  • Enhance product understanding and representation using LLMs
  • Improve the generalization and relevancy of RecSys via LLMs
  • LLM backboned RecSys
  • Improve the explanation and reasoning of RecSys via LLMs
  • Evaluation of RecSys using LLMs
  • Scaling LLMs in RecSys
  • Evaluation for embedding, ranking, and other services
• New algorithms and paradigms (foundation models, deep learning, reinforcement learning, online learning etc.)
• New use cases (product, content, fashion/decor, job, healthy lifestyle, interactive/conversational recommendations, etc.)
• New user modeling and representations (real-time user intent/style/taste modeling, combine with long term interest, incorporation of knowledge graph)
• New product understanding and representation methodologies
• New architecture and infrastructure (RAG and similar architectures, novel and scalable deep learning architectures, steaming and event-driven processing, etc.)
• New evaluations and explanations methods (evaluation, comparison, explanation of OARS for a recommendation task, off-policy and counterfactual evaluation, etc.)
• New social and user impact areas (UX, welfare, objectives of OARS, privacy and ethics considerations, etc.)