The purpose of sequential recommendation is to utilize the interaction history of a user and predict the next item that the user is most likely to interact with. While data sparsity and cold start are two challenges that most recommender systems are still facing, many efforts are devoted to utilizing data from other domains, called cross-domain methods. However, general cross-domain methods explore the relationship between two domains by designing complex model architecture, making it difficult to scale to multiple domains and utilize more data. Moreover, existing recommendation systems use IDs to represent item, which carry less transferable signals in cross-domain scenarios, and user cross-domain behaviors are also sparse, making it challenging to learn item relationship from different domains. These problems hinder the application of multi-domain methods to sequential recommendation. Recently, large language models (LLMs) exhibit outstanding performance in world knowledge learning from text corpora and general-purpose question answering. Inspired by these successes, we propose a simple but effective framework for domain-agnostic recommendation by exploiting the pre-trained LLMs (namely LLM-Rec). We mix the user's behavior across different domains, and then concatenate the title information of these items into a sentence and model the user's behaviors with a pre-trained language model. We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems. Furthermore, we are curious about whether the latest technical advances in nature language processing (NLP) can transfer to the recommendation scenarios.

翻译：顺序推荐的目标是利用用户的交互历史，预测用户最可能交互的下一个项目。数据稀疏性和冷启动仍是大多数推荐系统面临的两大挑战，为此许多研究致力于利用其他领域的数据，即采用跨领域方法。然而，通用跨领域方法通过设计复杂的模型架构来探索两个领域间的关系，难以扩展至多个领域并利用更多数据。此外，现有推荐系统使用ID表示项目，这些ID在跨领域场景中携带的可迁移信号较少，而用户的跨领域行为本身也较为稀疏，导致难以从不同领域学习项目关系。这些问题阻碍了多领域方法在顺序推荐中的应用。近期，大型语言模型（LLMs）在从文本语料库中学习世界知识和通用问答方面展现出卓越性能。受此启发，我们提出一个简单而有效的框架，通过利用预训练LLMs实现领域无关的推荐（即LLM-Rec）。我们将用户在不同领域的行为混合，将这些项目的标题信息拼接成句子，并使用预训练语言模型对用户行为进行建模。我们期望通过混合用户跨领域行为，能够利用预训练语言模型中编码的通用知识，缓解数据稀疏性和冷启动问题。此外，我们好奇自然语言处理（NLP）领域的最新技术进展是否能够迁移至推荐场景。