The cold-start problem has been commonly recognized in recommendation systems
and studied by following a general idea to leverage the abundant interaction
records of warm users to infer the preference of cold users. However, the
performance of these solutions is limited by the amount of records available
from warm users to use. Thus, building a recommendation system based on few
interaction records from a few users still remains a challenging problem for
unpopular or early-stage recommendation platforms. This paper focuses on
solving the few-shot recommendation problem for news recommendation based on
two observations. First, news at different platforms (even in different
languages) may share similar topics. Second, the user preference over these
topics is transferable across different platforms. Therefore, we propose to
solve the few-shot news recommendation problem by transferring the user-news
preference from a rich source domain to a low-resource target domain. To bridge
two domains in different languages without any overlapping users and news, we
propose a novel unsupervised cross-lingual transfer model as the news encoder
that aligns semantically similar news in two domains. A user encoder is
constructed on top of the aligned news encoding and transfers the user
preference from the source to the target domain. Experimental results on two
real-world news recommendation datasets show the superior performance of our
proposed method on addressing few-shot news recommendation, comparing to the
state-of-the-art baselines.

本文旨在解决新闻推荐中的 few-shot 问题，通过跨语言转移用户 - 新闻偏好来缩小不同语言域之间的差异，采用一种新颖的跨语言转移模型作为新闻编码器，以达到较高的性能。