  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 many-shot source domain to a few-shot target domain. To
bridge two domains that are even in different languages and 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 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 baselines.
