In this episode, we delve into the transformative impact of Generative Models on modern Recommender Systems (RS), as detailed in the comprehensive survey titled "A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)". This multidisciplinary study explores how traditional RS, which primarily relied on user-item rating histories, are evolving through the integration of advanced generative techniques.

Key Discussion Points:

• Interaction-Driven Generative Models: We examine how models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are utilized to capture complex user-item interactions, enabling the generation of personalized recommendations beyond historical data.
• Large Language Models (LLMs) in Natural Language Recommendations: The episode highlights the role of LLMs, such as ChatGPT and Gemini, in understanding and generating human-like text, facilitating conversational recommendations and enhancing user engagement through natural language interfaces.
• Multimodal Models for Rich Content Integration: We discuss the incorporation of multimodal data—text, images, and videos—into RS, allowing for a more holistic understanding of user preferences and the ability to recommend diverse content types.
• Evaluation Paradigms and Ethical Considerations: The survey emphasizes the importance of developing new evaluation frameworks to assess the performance and societal impact of Gen-RecSys, addressing challenges such as bias, fairness, and user privacy.

Join us as we explore these advancements, shedding light on the future directions of recommender systems in the era of generative AI.