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.

虽然社会事件经常影响全世界的人们，但很大一部分事件都以当地为焦点，主要影响特定的语言社区。例子包括全国选举、不同国家冠状病毒大流行的发展，以及法国凯撒奖和莫斯科国际电影节等地方电影节在俄国。然而，现有的实体推荐方法不足以解决推荐的语言环境。本文介绍了语言特定事件推荐的新任务，旨在在特定语言的上下文中推荐与用户查询相关的事件。考虑到用户信息需求的语言上下文，此任务可以支持基本信息检索活动，包括 Web 导航和探索性搜索。我们提出了LaSER，这是一种针对特定语言事件推荐的新方法。激光在学习排序模型中融合实体和事件的特定语言潜在表示（嵌入）和时空事件特征。该模型是根据公开可用的 Wikipedia Clickstream 数据进行训练的。我们的用户研究结果表明，在推荐事件的语言特定相关性方面， LaSER在 MAP@5 中优于最先进的推荐基线高达 33 个百分点。