We invite submissions to the Third Workshop on Generative AI for Recommender Systems and Personalization (GenAIRecP 2026), to be held in conjunction with WSDM 2026 in Boise, Idaho, USA.

Our topics of interest include but are not limited to:

  • Generative AI for contextual and sequential modeling in recommender systems
  • Personalized generative retrieval, generative recommendation, search and novel applications
  • Instruction-tuned recommender systems
  • LLM-driven personalized dialogue systems
  • Personalized text and image generation
  • Privacy in personalized generative AI systems
  • Fairness, explainability, and transparency in LLM-driven personalized and recommender systems
  • Efficiency and scalability of LLM-driven personalization and recommendation systems
  • Evaluation of LLM-driven personalization and recommendation systems
  • Agentic systems for recommendation and personalization use-cases
  • Deployment case studies

Important dates

  • Paper submission deadline: November 21, 2025 November 28, 2025
  • Paper acceptance notification: December 18, 2025
  • Workshop: February 26, 2026

Submission Instructions:

  • Authors should submit papers with up to 2-8 pages excluding references and supplementary materials via OpenReview.
  • Submissions should follow WSDM formatting guidelines (link TBD).
  • Submissions should be fully anonymized for double-blind review.
  • Dual submission policy: This workshop welcomes ongoing and unpublished work but will also accept papers that are under review or have recently been accepted at other venues.

FAQ

  • Q: Can I submit work that is currently under review elsewhere?
    • A: Yes.
  • Q: Will the workshop papers be included in the conference proceedings?
    • A: No, the workshop proceedings policy will be clarified later, but authors may submit elsewhere. Organizers will be invited to submit a camera-ready summary to WSDM proceedings (organizer-facing).
  • Q: I’m from industry, how should I maintain anonymity?
    • A: The author’s information should be fully anonymized for double-blind review. However, it’s acceptable to explicitly refer in the paper to the companies or organizations that provided datasets, hosted experiments or deployed solutions.
  • Q: Can previously published papers be submitted to the workshop?
    • A: Yes, though we only encourage very recent and relevant papers.

Organizers

  • Narges Tabari, AWS AI Labs
  • Aniket Deshmukh, Databricks
  • Wang-Cheng Kang, Google DeepMind
  • Neil Shah, Snap
  • Julian McAuley, University of California San Diego
  • James Caverlee, Texas A&M University
  • George Karypis, University of Minnesota