News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation

TL;DR

This paper introduces NaSE, a domain-adapted multilingual sentence encoder that significantly improves zero-shot cross-lingual news recommendation, especially in cold-start and few-shot scenarios, without requiring extensive fine-tuning.

Contribution

The paper proposes NaSE, a news-specific multilingual sentence encoder that outperforms existing models in cross-lingual news recommendation without the need for supervised fine-tuning.

Findings

NaSE achieves state-of-the-art zero-shot cross-lingual transfer performance.

NaSE performs well in cold-start and few-shot recommendation scenarios.

Simple baseline with frozen NaSE embeddings and late click-behavior fusion is effective.

Abstract

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.