In recent years, Recommender Systems (RS) have witnessed a transformative shift with the advent of Large Language Models (LLMs) in the field of Natural Language Processing (NLP). Models such as GPT-3.5/4, Llama, have demonstrated unprecedented capabilities in understanding and generating human-like text. The extensive information pre-trained by these LLMs allows for the potential to capture a more profound semantic representation from different contextual information of users and items. While the great potential lies behind the thriving of LLMs, the challenge of leveraging user-item preferences from contextual information and its alignment with the improvement of Recommender Systems needs to be addressed. Believing that a better understanding of the user or item itself can be the key factor in improving recommendation performance, we conduct research on generating informative profiles using state-of-the-art LLMs. To boost the linguistic abilities of LLMs in Recommender Systems, we introduce the Prompting-Based Representation Learning Method for Recommendation (P4R). In our P4R framework, we utilize the LLM prompting strategy to create personalized item profiles. These profiles are then transformed into semantic representation spaces using a pre-trained BERT model for text embedding. Furthermore, we incorporate a Graph Convolution Network (GCN) for collaborative filtering representation. The P4R framework aligns these two embedding spaces in order to address the general recommendation tasks. In our evaluation, we compare P4R with state-of-the-art Recommender models and assess the quality of prompt-based profile generation.

翻译：近年来，随着自然语言处理领域大语言模型的出现，推荐系统经历了变革性发展。GPT-3.5/4、Llama等模型在理解和生成类人文本方面展现出前所未有的能力。这些大语言模型通过预训练获得的海量信息，使其具备从用户与项目的多维度上下文信息中捕捉更深层语义表征的潜力。尽管大语言模型的蓬勃发展蕴含着巨大潜力，但如何从上下文信息中挖掘用户-项目偏好，并使其与推荐系统性能提升相协同，仍是亟待解决的挑战。我们认为，更精准地理解用户或项目本质是提升推荐性能的关键因素，因此本研究采用前沿大语言模型生成信息丰富的用户画像。为增强大语言模型在推荐系统中的语言处理能力，我们提出了基于提示的推荐表征学习方法。在该框架中，我们运用大语言模型提示策略创建个性化项目画像，随后通过预训练的BERT模型将这些画像转化为文本嵌入的语义表征空间。此外，我们引入图卷积网络以获取协同过滤表征。该框架通过对齐这两个嵌入空间来处理通用推荐任务。在评估阶段，我们将该方法与前沿推荐模型进行对比，并对基于提示的画像生成质量进行了系统性评估。