Collaborative filtering by graph convolution network in location-based recommendation system
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256808" target="_blank" >RIV/61989100:27240/24:10256808 - isvavai.cz</a>
Result on the web
<a href="https://itiis.org/digital-library/100957" target="_blank" >https://itiis.org/digital-library/100957</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3837/tiis.2024.07.008" target="_blank" >10.3837/tiis.2024.07.008</a>
Alternative languages
Result language
angličtina
Original language name
Collaborative filtering by graph convolution network in location-based recommendation system
Original language description
Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness. Copyright © 2024 KSII.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
KSII Transactions on Internet and Information Systems
ISSN
1976-7277
e-ISSN
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Volume of the periodical
18
Issue of the periodical within the volume
7
Country of publishing house
KR - KOREA, REPUBLIC OF
Number of pages
20
Pages from-to
1868-1887
UT code for WoS article
001282381200006
EID of the result in the Scopus database
2-s2.0-85200265846