Recent studies have proposed a unified user modeling framework that leverages user behavior data from various applications. Most 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：最近的研究提出了一个统一的用户模型框架,利用各种应用的用户行为数据。大多数都从将用户行为顺序作为简单的文本而获益,代表任何领域或系统中的丰富信息,不失为一般性。因此,出现一个问题:用户历史档案的语言模型能够帮助改进推荐系统吗?虽然在许多领域广泛调查了其多种用途的可使用性,但其对推荐系统的应用仍未得到充分探讨。我们显示,直接适用于特定任务用户历史的语言模型在各种建议任务上取得了极好的结果。此外,利用额外的任务不可知用户历史也带来显著的业绩效益。我们进一步表明,我们的方法可以为现实世界广泛的推荐系统提供有希望的转移学习能力,即使是在看不见的领域和服务方面也是如此。