  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.
