论文标题
语言建模在推荐系统中的关键作用：丰富特定任务和任务不合时宜的表示
Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning
论文作者
论文摘要
最近的研究提出了统一的用户建模框架，以利用来自各种应用程序的用户行为数据。他们中的许多人从利用用户的行为序列作为纯文本中受益，代表任何域或系统中的丰富信息而不会失去通用性。因此，出现了一个问题：用户历史记录语料库的语言建模可以帮助改善推荐系统吗？尽管其多功能可用性已在许多域中进行了广泛的研究，但其在推荐系统中的应用仍未得到充满意。我们表明，直接适用于特定于任务的用户历史的语言建模在各种建议任务上取得了出色的成果。此外，利用其他任务不合时宜的用户历史可带来重大的性能优势。我们进一步证明，即使在看不见的域和服务上，我们的方法也可以为广泛的现实推荐系统提供有希望的转移学习能力。
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.
