Generative AI for Recommender systems
I recently presented on the application of Large Language Models (LLMs) and Generative AI within Recommender Systems, distinguishing it from my earlier presentation on their use in Search. Despite the differences, I maintain that the distinction between Search, Recommender, and Personalization systems is somewhat arbitrary. These systems often share common objectives — serving both customer needs and business goals — and frequently leverage similar underlying technologies.
In this presentation, I explored how LLMs and Generative AI enhance recommender systems through improved sequential modeling, in-session recommendations, and zero-shot ranking, which is particularly advantageous in certain recommendation scenarios. I also addressed the role of conversational recommendations and multimodal approaches, highlighting their effectiveness in helping users discover and engage with relevant content.
It is evident that the recommender systems industry is undergoing a transformation, driven by LLMs and Generative AI. These technologies are not only optimizing existing models to boost conversions but are also creating entirely new recommendation experiences.
