Organizers: Yashar Deldjoo (Polytechnic University of Bari), Julian McAuley (UC San Diego), Scott Sanner (University of Toronto), Pablo Castells (Universidad Autónoma de Madrid), Shuai Zhang (Amazon Web Services AI), and Enrico Palumbo (Spotify)

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Generative models are changing the way we seek information online. Large language models (LLMs) such as Chat-GPT represent one successful application of generative models, leveraging vast amounts of texts encoded in their billion-scale parameters. Recommendation systems employing generative models go beyond LLMs, encompassing a broader range of models such as deep generative models (DGMs) trained directly on user-item interactions, multi-modal foundation models, and other non-LLM generative models. These models offer new opportunities in the field of recommender systems by enhancing how user preferences are learned, connecting us with vast amounts of information available on the Internet. They are capable of delivering more personalized and contextually relevant content, generating recommendations without reliance on narrowly defined datasets, and addressing the cold-start issue. Furthermore, these models significantly enhance the level of interactivity users have with recommender systems, boosting conversational capabilities.However, there is no “free cake”; new advantages bring new challenges and risks that must be addressed when using LLMs and other categories of DGMs. Some of these challenges are new (e.g., hallucination, out-of-inventory recommendations) and some are newly intensified due to the expanded capabilities of these systems (privacy, fairness and biases, security and robustness, manipulation, opacity, accountability, over-reliance on automation). A critical aspect of utilizing these technologies is to develop robust evaluation systems that can effectively assess the performance, fairness, and security of these Gen-RecSys. Proper evaluation is essential to ensure these systems are reliable and trustworthy, especially when dealing with sensitive user data and making impactful recommendations.This workshop will specifically focus on the Risks, Opportunities, and Evaluation in real-world Recommender System applications, aiming to cover a full spectrum of current challenges and advances. Additionally, the workshop invites discussions on the application of LLMs and Generative Models in specific tasks and areas such as Conversation, Explanation, Bundle Recommendation, among others. The discussions are encouraged as long as the goal pertains to some form of information seeking.