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 .

Digital news platforms use news recommenders as the main instrument to cater to the individual information needs of readers. Despite an increasingly language-diverse online community, in which many Internet users consume news in multiple languages, the majority of news recommendation focuses on major, resource-rich languages. Moreover, nearly all news recommendation efforts assume monolingual news consumption, whereas more and more users tend to consume information in at least two languages. Accordingly, the existing body of work on news recommendation suffers from a lack of publicly available multilingual benchmarks that would catalyze development of news recommenders effective in multilingual settings and for low-resource languages. Aiming to fill this gap, we introduce xMIND, an open, multilingual news recommendation dataset derived from the English MIND dataset using machine translation, covering a set of 14 linguistically and geographically diverse languages, with digital footprints of varying sizes. Using xMIND, we systematically benchmark several content-based neural news recommenders (NNRs) in zero-shot (ZS-XLT) and few-shot (FS-XLT) cross-lingual transfer scenarios, considering both monolingual and bilingual news consumption patterns. Our findings reveal that (i) current NNRs, even when based on a multilingual language model, suffer from substantial performance losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT training has limited benefits, particularly when combined with a bilingual news consumption. Our findings thus warrant a broader research effort in multilingual and cross-lingual news recommendation. We release xMIND at https://github.com/andreeaiana/xMIND.

The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion). These models are predominantly trained with standard point-wise classification objectives. The existing body of work exhibits two main shortcomings: (1) despite general design homogeneity, direct comparisons between models are hindered by varying evaluation datasets and protocols; (2) it leaves alternative model designs and training objectives vastly unexplored. In this work, we present a unified framework for news recommendation, allowing for a systematic and fair comparison of news recommenders across several crucial design dimensions: (i) candidate-awareness in user modeling, (ii) click behavior fusion, and (iii) training objectives. Our findings challenge the status quo in neural news recommendation. We show that replacing sizable user encoders with parameter-efficient dot products between candidate and clicked news embeddings (late fusion) often yields substantial performance gains. Moreover, our results render contrastive training a viable alternative to point-wise classification objectives.