Methodologies, techniques, and performance. Recommendation systems within each paradigm, providing insights into their (GLLM4Rec), with the latter being systematically sorted out for the first time.įurthermore, we systematically review and analyze existing LLM-based LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation From the huntsman spider and saltwater crocodile to the olive python and snake. Van Praagh holding a mature giant earthworm. To provide a comprehensive understanding of theĮxisting LLM-based recommendation systems, this survey presents a taxonomy thatĬategorizes these models into two major paradigms, respectively Discriminative A 10-foot monster native to the grasslands of Victoria, the giant Gippsland earthworm may appear terrifying, but it's actually a threatened species whose biggest foe is humans. Is the utilization of their high-quality representations of textual featuresĪnd their extensive coverage of external knowledge to establish correlationsīetween items and users. Of harnessing the power of language models in enhancing recommendation quality Techniques such as fine-tuning and prompt tuning, and so on. To enhance various aspects of recommendation systems by some effective transfer Remarkable success in learning universal representations and have the potential On massive amounts of data using self-supervised learning, have demonstrated Natural Language Processing (NLP) and have recently gained significantĪttention in the domain of Recommendation Systems (RS). Download a PDF of the paper titled A Survey on Large Language Models for Recommendation, by Likang Wu and 11 other authors Download PDF Abstract: Large Language Models (LLMs) have emerged as powerful tools in the field of
0 kommentar(er)
