  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 C\'esar 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 MAP@5 concerning the language-specific relevance of recommended
events.
