Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
This work addresses the challenge of enhancing recommendation accuracy and transferability for real-world systems, though it appears incremental by extending language modeling techniques to an underexplored application area.
The paper tackles the problem of improving recommender systems by applying language modeling to user behavior sequences, showing that it achieves excellent results on diverse recommendation tasks and provides significant performance benefits when leveraging additional task-agnostic histories.
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.