We will welcome papers that leverage generative models with a goal of recommendation and personalization on several topics including but not limited to:

  • Generative AI for multimodal and sequential modeling in recommender systems
  • Personalized generative retrieval and recommendation models
  • 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

Important dates

  • Paper submission deadline: 28 May 2024 10 June 2024
  • Paper acceptance notification: 28 June 2024
  • Workshop: 26 August 2024

Submission Instructions:

  • Authors should submit papers with up to 5 pages excluding references and supplementary materials via OpenReview .
  • Submissions should be formatted using the official KDD 2024 template which can be found at https://kdd2024.kdd.org/research-track-call-for-papers/.
  • 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  • Dual submission policy: This workshop welcomes ongoing and unpublished work but will also accept papers that are under review or have recently been accepted at other venues.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 KDD’24 proceeding?
    • A: No, so submitting to the workshop conforms to the dual submission policy.
  • 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.

Invited speakers:

  • Ed Chi, Google DeepMind
  • Dietmar Jannach, University of Klagenfurt
  • Xiao-Ming Wu, The Hong Kong Polytechnic University
  • Dawen Liang, Netflix

Organizers:

  • Narges Tabari, AWS AI Labs
  • Aniket Deshmukh, AWS AI Labs
  • Wang-Cheng Kang, Google DeepMind
  • Hamed Zamani, University of Massachusetts Amherst
  • Rashmi Gangadharaiah, AWS AI Labs
  • Julian McAuley, University of California San Diego
  • George Karypis, University of Minnesota