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.

采用预训练语言模型的语义理解能力，提出了一种新的序列推荐策略 LANCER，以弥补先前序列建模方法对上下文信息的不足，从而在推荐系统中生成更加人性化的个性化推荐。经过在多个基准数据集上的实验证明，我们的方法有效，具有良好的结果，并对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，我们的实验代码是公开可用的。