The relevance and perception of events with global and local impact, such as national elections and terrorist attacks, can vary significantly among different language communities. This chapter discusses recent user access models for event-centric multilingual information, focusing on assisting users, including social scientists and digital humanities researchers, who analyse such events and their impacts. These models aim to facilitate information exploration by emphasising cultural and linguistic differences, a dimension often overlooked by existing entity recommendation methods. Developing recommendation models supporting cross-lingual and cross-cultural analysis of event-related information is particularly challenging due to language barriers and the lack of established datasets. To address these challenges, our prior work involved the creation of the EventKG+Click dataset, which contains event-centric user interaction traces extracted from the EventKG knowledge graph and Wikipedia clickstream data. Additionally, we introduced LaSER—a language-specific event recommendation model that considers the user’s linguistic and cultural preferences. To improve recommendations, LaSER incorporates language-specific click data from EventKG+Click. Furthermore, LaSER integrates language-specific embeddings of entities and events, along with their spatio-temporal features, into a learning-to-rank model. This chapter provides an overview of these methods, datasets and evaluation results.