Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning

TL;DR

This paper explores how language modeling applied to user behavior sequences can enhance recommender systems by improving task-specific and task-agnostic representations, leading to better performance and transfer learning capabilities.

Contribution

It demonstrates that language modeling on user histories significantly improves recommendation accuracy and transferability across domains, a relatively underexplored area.

Findings

Language modeling on user histories yields excellent recommendation results.

Incorporating task-agnostic histories provides additional performance gains.

The approach offers promising transfer learning for unseen domains.

Abstract

Recent studies have proposed unified user modeling frameworks that leverage user behavior data from various applications. Many of them benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.