A review of large language models and the recommendation task

Abstract Recommender systems are now ubiquitous across the internet, from streaming services to online shopping to social media. Traditional systems operate behind the scenes, often invisible to the end user. While these systems have enjoyed prolific success, they have limitations—namely, their mechanical interactions lack contextual awareness. A promising area of research is the combination of large language models (LLMs) with traditional recommendation methods to increase flexibility and performance. We discuss prominent examples, including conversational recommender systems, LLMs as end-to-end recommenders, and LLMs as encoders for recommendation. Of particular importance is the transformer neural network architecture, which underpins these LLMs and has shown itself to be incredibly powerful in natural language processing, and has now been adapted to serve recommendation tasks. This review offers a unique perspective on the evolving role of data in recommender systems, tracing data requirements from traditional matrix-based data and knowledge-based data, to the adoption of transformers with web-scale data. We detail how these shifting data paradigms have shaped the field and integration of transformer architecture, large language models, and chatbots in modern recommender systems. This paper is intended for readers interested in the intersection of recommender systems and transformers (and LLMs), those tracking the evolution of data used in such systems, and newcomers seeking an introduction to these topics.