Collaborative filtering by graph convolution network in location-based recommendation system

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Collaborative filtering by graph convolution network in location-based recommendation system

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Collaborative filtering by graph convolution network in location-based recommendation system

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

KSII Transactions on Internet and Information Systems

ISSN

1976-7277

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

KR - Korejská republika

Počet stran výsledku

20

Strana od-do

1868-1887

Kód UT WoS článku

001282381200006

EID výsledku v databázi Scopus

2-s2.0-85200265846