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Recent developments in Large Language Models (LLMs) have brought a significant paradigm shift in Natural Language Processing (NLP) domain. These pretrained language models encode an extensive amount of world knowledge, and they can be applied to a multitude of downstream NLP applications with zero or just a handful of demonstrations.
While existing recommender systems mainly focus on behavior data, large language models encode extensive world knowledge mined from large-scale web corpora. Hence these LLMs store knowledge that can complement the behavior data. For example, an LLM-based system, like ChatGPT, can easily recommend buying turkey on Thanksgiving day, in a zero-shot manner, even without having click behavior data related to turkeys or Thanksgiving.
Many researchers have recently proposed different approaches to building recommender systems using LLMs. These methods convert different recommendation tasks into either language understanding or language generation templates. This article highlights the prominent work done on this theme.