Understanding the significance of entities in the context of a particular search topic necessitates a special focus on linguistic, cultural, and national aspects. This paper presents an innovative study on obtaining entity importance scores within a cross-national context. While setting up an extensive crowdsourcing task pool, we asked the crowd-workers to rank given entities within a particular general-domain search topic following the best-worst scaling method. Considering two different platforms (Amazon Mechanical Turk and Toloka AI), we focus on crowdworkers from the USA and Russia, speaking English and Russian, respectively. As a result of this, we reveal a strong impact of the national factor of crowdworkers on the entity importance annotations. By highlighting differences and commonalities in the importance scores across nations, our work provides advanced insights and a dataset for future research in the field of cross-national entity ranking. Our findings and insights can be applied beyond the general domain search topics, in particular, when working with recommendations (e.g., for eCommerce items) the consideration of the cross-national factor plays a vital role on the quality.

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the César Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in [email protected] concerning the language-specific relevance of recommended events.