Large Language Models as Versatile End-to-End Recommenders: Overcoming the Limitations of Conventional Pipelined Systems

Large Language Models (LLMs) can be leveraged to seamlessly integrate multiple recommendation tasks, including recall, ranking, and re-ranking, within a unified end-to-end framework, eliminating the need for specialized models and enabling efficient handling of large-scale item sets.