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.

翻译：尽管社会性事件往往影响全球人群，但相当一部分事件具有地域聚焦性，主要影响特定语言社区。例如国家选举、新冠疫情在不同国家的发展态势，以及法国凯撒奖、莫斯科国际电影节等本地电影节。然而，现有实体推荐方法未能充分考虑推荐中的语言语境。本文提出语言特定事件推荐这一新任务，旨在推荐与用户查询在特定语言语境中相关的事件。该任务可支持用户信息需求中考虑语言语境的网页导航、探索式搜索等关键信息检索活动。我们提出LaSER这一面向语言特定事件推荐的新方法。LaSER在排序学习模型中融合了实体与事件的语言特定潜在表征（嵌入）及时空事件特征。该模型基于公开的维基百科点击流数据进行训练。用户研究结果表明，在推荐事件的语言特定相关性方面，LaSER在MAP@5指标上相比现有最优推荐基线方法最高提升33个百分点。