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海量信息预训练，有望从用户和项目的不同上下文信息中捕获更深层次的语义表征。尽管大语言模型的蓬勃发展蕴含着巨大潜力，但如何利用上下文信息中的用户-项目偏好，并使其与推荐系统的改进相协调，仍是亟待解决的挑战。我们认为，更好地理解用户或项目本身可能是提升推荐性能的关键因素，因此我们利用前沿大语言模型开展信息丰富的用户画像生成研究。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，我们采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进行协同过滤表征。P4R框架通过对齐这两个嵌入空间来处理通用推荐任务。在评估中，我们将P4R与前沿推荐模型进行对比，并评估了基于提示的画像生成质量。