Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling
This work aims to improve recommender systems for online applications by integrating language models to better understand user interests, though it appears incremental in combining existing methods.
The paper tackled the problem of sequential recommendation by addressing limitations in capturing contextual information and domain-specific knowledge, proposing LANCER to leverage pre-trained language models for personalized recommendations, and demonstrated promising results on multiple benchmark datasets.
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.