LaSER: Language-Specific Event Recommendation
This addresses the need for language-aware event recommendation to support information retrieval tasks like web navigation for users in specific linguistic contexts, representing an incremental improvement over existing entity recommendation methods.
The paper tackles the problem of recommending events relevant to specific language communities, which existing methods overlook, and shows that their LaSER approach outperforms state-of-the-art baselines by up to 33 percentage points in MAP@5 for language-specific relevance.
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.