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.

翻译：近期研究提出了利用来自各种应用的用户行为数据的统一用户建模框架。其中许多研究受益于将用户行为序列作为纯文本使用，从而在无需丧失一般性的情况下，在任何领域或系统中表示丰富的信息。因此，一个问题随之产生：对用户历史语料进行语言建模是否有助于改进推荐系统？尽管其多功能实用性已在许多领域得到广泛研究，但其在推荐系统中的应用仍缺乏深入探索。我们证明，直接应用于任务特定用户历史的语言建模能在多种推荐任务上取得优异结果。此外，利用额外的任务无关用户历史能带来显著的性能提升。我们进一步表明，我们的方法能够为广泛的真实世界推荐系统提供有前景的迁移学习能力，即便在未见领域和服务上也是如此。