Recommender systems are essential for online applications, and sequential recommendation has enjoyed significant prevalence due to its expressive ability to capture dynamic user interests. However, previous sequential modeling methods still have limitations in capturing contextual information. The primary reason for this issue is that language models often lack an understanding of domain-specific knowledge and item-related textual content. To address this issue, we adopt a new sequential recommendation paradigm and 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 experiments on several 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：推荐系统对于在线应用至关重要，而序列推荐因其能够捕捉动态用户兴趣的表达能力而广受关注。然而，以往的序列建模方法在捕捉上下文信息方面仍存在局限性。这一问题的主要原因在于语言模型往往缺乏对领域特定知识与项目相关文本内容的理解。为解决此问题，我们采用了一种新的序列推荐范式，并提出LANCER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差距，从而产生更接近人类行为的推荐。通过在多个基准数据集上的实验，我们验证了该方法的有效性，展现了令人满意的结果，并为我们的模型在序列推荐任务中的影响提供了有价值的见解。此外，我们的实验代码已公开提供。