Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language Modeling

TL;DR

This paper introduces LANCER, a novel method that uses pre-trained language models to improve sequential recommendation systems by better capturing user interests and contextual information, leading to more human-like recommendations.

Contribution

The paper proposes LANCER, a new approach that integrates language models into recommender systems to enhance understanding of user preferences and contextual cues.

Findings

LANCER outperforms existing methods on benchmark datasets.

Language models improve contextual understanding in recommendations.

The approach provides more personalized and human-like suggestions.

Abstract

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.