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：近期研究提出了多种统一用户建模框架，通过利用来自不同应用的用户行为数据实现建模。其中许多框架受益于将用户行为序列作为纯文本处理，从而在不丧失通用性的前提下，表征任意领域或系统中的丰富信息。由此引发一个关键问题：针对用户历史语料库的语言建模能否助力改进推荐系统？尽管语言建模的通用性已在诸多领域得到广泛探索，其在推荐系统中的应用仍处于研究不足的阶段。我们证明：直接应用于任务特定用户历史数据的语言建模能在多样化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性能提升。进一步研究表明，该方法能够为广泛的实际推荐系统（甚至包括未见领域与服务）提供极具潜力的迁移学习能力。