The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongoing cold-start problem, where new items lack interaction data and are hard to value. Existing solutions for the cold-start problem, such as content-based recommenders and hybrid methods, leverage item metadata to determine item similarities. The main challenge with these methods is their reliance on structured and informative metadata to capture detailed item similarities, which may not always be available. This paper introduces a novel approach for cold-start item recommendation that utilizes the language model (LM) to estimate item similarities, which are further integrated as a Bayesian prior with classic recommender systems. This approach is generic and able to boost the performance of various recommenders. Specifically, our experiments integrate it with both sequential and collaborative filtering-based recommender and evaluate it on two real-world datasets, demonstrating the enhanced performance of the proposed approach.

翻译：推荐系统（RecSys）的发展受到数字化进程及电子商务、视频流媒体等领域对个性化内容需求的推动。这些系统中的内容通常更新迅速，因此持续面临冷启动问题：新项目因缺乏交互数据而难以评估价值。现有冷启动解决方案（如基于内容的推荐器和混合方法）利用项目元数据来确定项目相似性。这些方法的主要挑战在于依赖结构化且信息丰富的元数据来捕捉详细的项目相似性，而此类元数据并非总能获得。本文提出一种新颖的冷启动项目推荐方法，利用语言模型（LM）估计项目相似性，并将其作为贝叶斯先验与经典推荐系统集成。该方法具有通用性，能够提升各类推荐器的性能。具体而言，我们的实验将其与基于序列和协同过滤的推荐器相结合，并在两个真实世界数据集上进行评估，验证了所提方法的性能提升。