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 MAP@5 concerning the language-specific relevance of recommended
events.

介绍了一种新的任务，即语言特定的事件推荐，旨在根据语言特定的上下文推荐与用户查询相关的事件，使用了语言特定的潜在向量表示和时空事件特征融合的方法 LaSER，在公开的维基百科点击流数据上进行训练并在用户研究中表现优越，为信息检索等活动提供支持。