INTRO
Recommender systems have become one of the most impactful AI applications, significantly influencing various domains such as e-commerce, media streaming, and social platforms. As these systems continue to evolve, integrating more advanced methodologies has become crucial. In recent years, Large Language Models (LLMs) have shown immense potential to enhance recommender systems by capturing deeper contextual relationships and understanding user preferences in a more nuanced manner.
In this tutorial, we aim to provide a comprehensive review and discussion of the intersection between LLMs and recommender systems. We will examine how LLMs contribute to key recommendation tasks through both discriminative and generative modeling approaches. The tutorial will introduce various paradigms, including fine-tuning, prompt tuning, and in-context learning, along with a detailed taxonomy of LLM-based recommender models. In addition, we will explore the challenges and opportunities presented by these models, including issues related to bias, recommendation prompt design, and evaluation techniques.
By offering a clear overview of recent advances and research directions, this tutorial aims to equip both academic researchers and industry practitioners with the necessary knowledge to leverage LLMs in creating more effective and trustworthy recommender systems.
Time: TBD
Our Survey Paper: A Survey on Large Language Models for Recommendation
The topics of this tutorial include (but are not limited to) the following:
- Introduction to Large Language Models for Recommendation (LLM4Rec)
- Modeling Paradigms and Taxonomy
- Discriminative LLMs for Recommendation
- Generative LLMs for Recommendation Based-on the Non-tuning Paradigm
- Generative LLMs for Recommendation Based-on the Tuning Paradigm
- Discussions and Future Directions
Three Modeling Paradigms of Large Language Models for Recommendation (LLM4Rec)
