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

Understand

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.