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：近期研究提出了统一用户建模框架，利用来自各种应用的用户行为数据。其中许多方法通过将用户行为序列作为纯文本处理，在不失一般性的前提下保留了任何领域或系统中的丰富信息。由此引发一个问题：对用户历史语料进行语言建模能否帮助改进推荐系统？尽管语言建模的通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待深入探索。我们表明，直接应用于任务特定用户历史数据的语言建模能在多种推荐任务中取得优异表现。同时，利用额外的任务无关用户历史数据可带来显著的性能提升。我们进一步证明，该方法能为广泛的实际推荐系统提供有前景的迁移学习能力，即使面对未见过的领域和服务也依然有效。