Recommender systems are indispensable in the realm of online applications, and sequential recommendation has enjoyed considerable prevalence due to its capacity to encapsulate the dynamic shifts in user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason is the lack of understanding of domain-specific knowledge and item-related textual content by language models. Fortunately, the emergence of powerful language models has unlocked the potential to incorporate extensive world knowledge into recommendation algorithms, enabling them to go beyond simple item attributes and truly understand the world surrounding user preferences. To achieve this, we propose LANCER, which leverages the semantic understanding capabilities of pre-trained language models to generate personalized recommendations. Our approach bridges the gap between language models and recommender systems, resulting in more human-like recommendations. We demonstrate the effectiveness of our approach through a series of experiments conducted on multiple benchmark datasets, showing promising results and providing valuable insights into the influence of our model on sequential recommendation tasks. Furthermore, our experimental codes are publicly available.

翻译：推荐系统在在线应用领域中不可或缺，而序列推荐因其能够捕捉用户兴趣的动态变化而备受关注。然而，以往的序列建模方法在捕获上下文信息方面仍存在局限性。其主要原因在于语言模型缺乏对领域特定知识以及项目相关文本内容的理解能力。幸运的是，强大语言模型的出现解锁了将广泛的世界知识融入推荐算法的潜力，使其能够超越简单的项目属性，真正理解用户偏好背后的世界。为实现这一目标，我们提出了LANCER模型，该模型利用预训练语言模型的语义理解能力生成个性化推荐。我们的方法弥合了语言模型与推荐系统之间的鸿沟，从而产生更接近人类思维的推荐结果。通过在多个基准数据集上开展的一系列实验，我们验证了该方法的有效性，展示了令人鼓舞的结果，并为理解模型对序列推荐任务的影响提供了宝贵见解。此外，我们的实验代码已公开发布。