We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus. To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora. The learned translator can be used to generate representations for unseen users in the future. We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics. We also show that our translator is effective among existing transfer strategies.

中文翻译：

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TrNews：新闻推荐的异构用户兴趣转移学习

我们研究了将来如何解决针对看不见的用户的跨主体新闻推荐。这是传统的基于内容的推荐技术经常失败的问题。幸运的是，在现实世界中的推荐服务中，某些发布者（例如，每日新闻）可能已经积累了庞大的语料库，其中包含许多可以用于新部署的发布者的消费者（例如，政治新闻）。为了利用现有语料库，我们提出了一种转移学习模型（称为TrNews）来进行新闻推荐，以将知识从源语料库转移到目标语料库。为了解决语料库中不同用户兴趣和不同单词分布的异质性，我们设计了一种基于翻译器的转移学习策略，以学习源语料库和目标语料库之间的表示映射。经验丰富的翻译器可用于将来为看不见的用户生成表示形式。通过对真实数据集的实验，我们发现TrNews在四个指标方面要优于各种基线。我们还表明，在现有的转移策略中，我们的翻译员是有效的。