Introduction to the Special Issue on Large Language Models for Recommender Systems

Recommender systems have become pivotal in today’s digital landscape, shaping user experiences across diverse online platforms. Recent advances in Large Language Models (LLMs) such as T5, GPT, LLaMA, and their variants have introduced transformative possibilities for recommender systems. LLMs excel in processing and generating natural language text, offering a unique opportunity to reshape the design and elevate the effectiveness of recommendation algorithms. The main topic of this special issue is to explore the integration of Large Language Models and Recommender Systems, encompassing various facets, including model architectures, recommendation algorithms, evaluation methods, and real-world applications. It provides a dedicated platform for researchers and practitioners to share their insights, innovations, and empirical findings in the realm of LLMs for recommender systems, which helps to promote knowledge exchange, leading to best practices and guidelines for integrating LLMs and recommender systems. Towards this goal, the five articles in this collection span trustworthy issues such as recommendation fairness and diversity with LLMs, as well as classic recommendation problems, including sequential recommendation, click-through rate prediction and bundle recommendation with LLMs. By fostering interdisciplinary collaboration between the natural language processing and recommendation communities, the special issue aspires to advance the state of the art in this evolving field.