print

A scalable knowledge graph embedding model for next point-of-interest recommendation in Tallinn city

author
Ounoughi, Chahinez
Mouakher, Amira
Sherzad, Muhammad Ibraheem
Ben Yahia, Sadok
statement of authorship
Chahinez Ounoughi, Amira Mouakher, Muhammad Ibraheem Sherzad & Sadok Ben Yahia
source
Research Challenges in Information Science : 15th International Conference, RCIS 2021, Limassol, Cyprus, May 11–14, 2021 : proceedings
location of publication
Cham
publisher
Springer Nature Switzerland AG
year of publication
2021
pages
p. 435–451
series
Lecture notes in business information processing ; 415
conference name, date
15th International Conference, RCIS 2021, May 11–14, 2021
conference location
Limassol, Cyprus
url
https://doi.org/10.1007/978-3-030-75018-3_29
subject term
sotsiaalsed võrgustikud
huvid
Scopus
Conference Proceedings at Scopus
Article at Scopus
WOS
Article at WOS
kvartiil
Q3
category (general)
Business, management and accounting
Decision sciences
Engineering
Mathematics
Computer science
category (sub)
Business, management and accounting. Business and international management
Business, management and accounting. Management information systems
Decision sciences. Information systems and management
Engineering. Control and systems engineering
Mathematics. Modeling and simulation
Computer science. Information systems
keyword
node embedding learning
knowledge graph
point-of-interest
recommender system
location-based social networks
ISBN
978-3-030-75017-6
978-3-030-75018-3
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
tarkvarateaduse instituut
language
inglise
Uurimisrühm
Data science group
Ounoughi, C., Mouakher, A., Sherzad, M. I., Yahia, S. B. A scalable knowledge graph embedding model for next point-of-interest recommendation in Tallinn city // Research Challenges in Information Science : 15th International Conference, RCIS 2021, Limassol, Cyprus, May 11–14, 2021 : proceedings. Cham : Springer Nature Switzerland AG, 2021. p. 435–451. (Lecture notes in business information processing ; 415). https://doi.org/10.1007/978-3-030-75018-3_29