Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Event Analytics across Languages and Communities |
| Herausgeber (Verlag) | Springer Nature |
| Seiten | 149-168 |
| Seitenumfang | 20 |
| ISBN (elektronisch) | 9783031644511 |
| ISBN (Print) | 9783031644504 |
| Publikationsstatus | Veröffentlicht - 2025 |
Abstract
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 intro-duced LaSER-a language-specific event recommendation model that considers the user's linguistic and cultural preferences. To improve recommendations, LaSER in-corporates 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
- Sozialwissenschaften (insg.)
- Allgemeine Sozialwissenschaften
Ziele für nachhaltige Entwicklung
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Event Analytics across Languages and Communities. Springer Nature, 2025. S. 149-168.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - Event Recommendation Through Language-Specific User Behaviour in Clickstreams
AU - Abdollahi, Sara
AU - Demidova, Elena
AU - Gottschalk, Simon
N1 - Publisher Copyright: © The Author(s) 2025. All rights reserved.
PY - 2025
Y1 - 2025
N2 - 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 intro-duced LaSER-a language-specific event recommendation model that considers the user's linguistic and cultural preferences. To improve recommendations, LaSER in-corporates 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.
AB - 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 intro-duced LaSER-a language-specific event recommendation model that considers the user's linguistic and cultural preferences. To improve recommendations, LaSER in-corporates 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.
UR - http://www.scopus.com/inward/record.url?scp=105002623900&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-64451-1_8
DO - 10.1007/978-3-031-64451-1_8
M3 - Contribution to book/anthology
AN - SCOPUS:105002623900
SN - 9783031644504
SP - 149
EP - 168
BT - Event Analytics across Languages and Communities
PB - Springer Nature
ER -