  Rapidly growing numbers of multilingual news consumers pose an increasing
challenge to news recommender systems in terms of providing customized
recommendations. First, existing neural news recommenders, even when powered by
multilingual language models (LMs), suffer substantial performance losses in
zero-shot cross-lingual transfer (ZS-XLT). Second, the current paradigm of
fine-tuning the backbone LM of a neural recommender on task-specific data is
computationally expensive and infeasible in few-shot recommendation and
cold-start setups, where data is scarce or completely unavailable. In this
work, we propose a news-adapted sentence encoder (NaSE), domain-specialized
from a pretrained massively multilingual sentence encoder (SE). To this end, we
construct and leverage PolyNews and PolyNewsParallel, two multilingual
news-specific corpora. With the news-adapted multilingual SE in place, we test
the effectiveness of (i.e., question the need for) supervised fine-tuning for
news recommendation, and propose a simple and strong baseline based on (i)
frozen NaSE embeddings and (ii) late click-behavior fusion. We show that NaSE
achieves state-of-the-art performance in ZS-XLT in true cold-start and few-shot
news recommendation.
