The KDD workshop on online and adaptive recommender systems (OARS) will serve as a platform for publication and discussion of OARS. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems, and improve user experiences by better modeling and responding to user intent.

Many recommender systems deployed in the real world rely on categorical user-profiles and/or pre-calculated recommendation actions that stay static during a user session. Recent trends suggest that recommender systems should model user intent in real time and constantly adapt to meet user needs at the moment or change user behavior in situ. In addition, various techniques have been proposed to help recommender systems adapt to new users, items, or behaviors. Some strategies to build “adaptive” recommenders include:

• Systems for online training, e.g. updating the parameters of a pre-trained model to a new user.
• Feature-based systems that handle cold-start scenarios, and can gracefully adapt to a combination of cold- and warm users/items.
• Systems that avoid modeling users at all (e.g. session-based recommenders that directly learn from item interactions without needing user terms)
• Systems that adapt to new behaviors through RL or other adaptive learning algorithms.

We invite submission of papers and posters of two to ten pages (including references), representing original research, preliminary research results, proposals for new work, position, and opinion papers. All submitted papers and posters 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:

• Novel algorithms and paradigms
  • online and adaptive neural recommender
  • reinforcement learning (on-policy, off-policy, offline RL, and other relevant subfields)
  • online/streaming learning
  • interactive and conversational recommender
  • extreme classification
  • graph recommender
• Applications
  • product recommendations
  • content recommendations (news, music, movie, video, etc.)
  • ads recommendations
  • fashion and decor recommendation
  • job recommendation
  • intervention/behavior change/healthy life-style recommendations
• User modeling and representations
  • implicit and explicit user intent modeling
  • dynamic user intent modeling
  • visual/style/taste modeling
  • combination of in-session intent with long term user interest
  • incorporation of knowledge graph
  • representation learning
• Architecture and infrastructure
  • scalability of neural methods for large scale real-time recommendations
  • steaming and event-driven processing infrastructures
• Evaluation and explanation methodologies
  • evaluation, comparison, explanation of OARS for a recommendation task
  • off-policy and counterfactual evaluation
• Social and user impact
  • UX for OARS
  • welfare and objectives of OARS (CTR, dwell-time, diversity, multi-objectives, long term objectives)
  • privacy and ethics considerations