TY - JOUR

T1 - LaSER

T2 - Language-specific event recommendation

AU - Abdollahi, Sara

AU - Gottschalk, Simon

AU - Demidova, Elena

N1 - Funding Information: This work was partially funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 812997 (“Cleopatra” ) and by DFG, German Research Foundation under grant agreement no. 424985896 (“WorldKG”).

PY - 2023/1

Y1 - 2023/1

N2 - 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.

AB - 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.

KW - Event recommendation

KW - Knowledge graphs

KW - Language-specific recommendation

UR - http://www.scopus.com/inward/record.url?scp=85139818990&partnerID=8YFLogxK

U2 - 10.1016/j.websem.2022.100759

DO - 10.1016/j.websem.2022.100759

M3 - Article

AN - SCOPUS:85139818990

VL - 75

JO - Journal of Web Semantics

JF - Journal of Web Semantics

SN - 1570-8268

M1 - 100759

ER -