Recommender systems play a crucial role in helping users discover information that aligns with their interests based on their past behaviors. However, developing personalized recommendation systems becomes challenging when historical records of user-item interactions are unavailable, leading to what is known as the system cold-start recommendation problem. This issue is particularly prominent in start-up businesses or platforms with insufficient user engagement history. Previous studies focus on user or item cold-start scenarios, where systems could make recommendations for new users or items but are still trained with historical user-item interactions in the same domain, which cannot solve our problem. To bridge the gap, our research introduces an innovative and effective approach, capitalizing on the capabilities of pre-trained language models. We transform the recommendation process into sentiment analysis of natural languages containing information of user profiles and item attributes, where the sentiment polarity is predicted with prompt learning. By harnessing the extensive knowledge housed within language models, the prediction can be made without historical user-item interaction records. A benchmark is also introduced to evaluate the proposed method under the cold-start setting, and the results demonstrate the effectiveness of our method. To the best of our knowledge, this is the first study to tackle the system cold-start recommendation problem. The benchmark and implementation of the method are available at https://github.com/JacksonWuxs/PromptRec.

翻译：推荐系统在帮助用户根据其历史行为发现符合兴趣的信息方面发挥着关键作用。然而，当用户-物品交互的历史记录不可用时，开发个性化推荐系统变得极具挑战性，这导致了所谓的系统冷启动推荐问题。该问题在用户交互历史不足的初创企业或平台中尤为突出。先前的研究主要聚焦于用户或物品冷启动场景，这些场景中系统虽能针对新用户或新物品进行推荐，但仍需基于同一领域内的历史用户-物品交互数据进行训练，因此无法解决我们面临的问题。为弥补这一差距，本研究提出了一种创新且有效的方法，充分利用预训练语言模型的能力。我们将推荐过程转化为对包含用户画像与物品属性信息的自然语言的情感分析，并通过提示学习预测情感极性。通过利用语言模型中蕴含的丰富知识，可在无需历史用户-物品交互记录的情况下进行预测。我们同时引入了一个基准测试来评估所提方法在冷启动设定下的表现，结果证明了其有效性。据我们所知，这是首个针对系统冷启动推荐问题的研究。该基准测试及方法实现已公开于 https://github.com/JacksonWuxs/PromptRec。