  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. 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 at https://github.com/Gnimixy/lancer.
