Multilinguality in MIND: Advancing Cross-lingual News Recommendation with a Multilingual Dataset

Digital news platforms rely on recommendation systems to meet the diverse information needs of readers. However, most research focuses on major, resource-rich languages, overlooking the linguistic diversity of online communities. Moreover, existing work typically assumes monolingual news consumption, neglecting polyglot users, and resulting in a lack of multilingual benchmarks for developing recommenders suited to multilingual and low-resource contexts. To address this gap, we introduce xMIND, an open, multilingual news recommendation dataset created by machine translating the English MIND dataset into 14 linguistically and geographically diverse languages with varying digital footprints. Using xMIND, we systematically evaluate several content-based neural news recommenders (NNRs) in zero-shot ( ZS-XLT ) and few-shot ( FS-XLT ) cross-lingual transfer, examining both monolingual and bilingual consumption patterns. In FS-XLT , we compare random and category-based replacement methods for incorporating target-language data during training. Our results show that (i) current NNRs, grounded in multilingual language models, experience significant performance drops in ZS-XLT , and (ii) injecting target-language data in FS-XLT provides limited improvements, especially for bilingual consumption. Notably, randomly injecting target-language news during training leads to greater performance gains compared to category-based replacements. Our in-depth analysis of representation alignment between source and target languages within the language model shows that FS-XLT improves cross-lingual alignment primarily for high-resource languages, while low-resource languages remain weakly aligned with English. These findings highlight the need for broader research efforts in multilingual and cross-lingual news recommendation. We release xMIND at https://github.com/andreeaiana/xMIND .