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Recent years have witnessed the wide adoption of large language models (LLM)in different fields, especially natural language processing and computervision. Such a trend can also be observed in recommender systems (RS). However,most of related work treat LLM as a component of the conventionalrecommendation pipeline (e.g., as a feature extractor) which may not be able tofully leverage the generative power of LLM. Instead of separating therecommendation process into multiple stages such as score computation andre-ranking, this process can be simplified to one stage with LLM: directlygenerating recommendations from the complete pool of items. This survey reviewsthe progress, methods and future directions of LLM-based generativerecommendation by examining three questions: 1) What generative recommendationis, 2) Why RS should advance to generative recommendation, and 3) How toimplement LLM-based generative recommendation for various RS tasks. We hopethat the survey can provide the context and guidance needed to explore thisinteresting and emerging topic.

Large Language Models for Generative Recommendation: A Survey and Visionary Discussions