A User Recommendation System Based on Graph Neural Network and Contextual Behavior

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F23%3A43906985" target="_blank" >RIV/60076658:12510/23:43906985 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2023.vse.cz/mme_2023_proceedings.pdf" target="_blank" >https://mme2023.vse.cz/mme_2023_proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A User Recommendation System Based on Graph Neural Network and Contextual Behavior

Popis výsledku v původním jazyce

Today, recommendation systems are an integral part of e-commerce services on the Internet. In connection with their development, neural networks have become the most used approach to recommender systems. In our post, we will demonstrate the use of graph neural networks to create a recommender system. E-commerce systems can be modeled using a bipartite interaction graph. There are two essential parts to this chart, users and items. In our model, context is added to them and integrated into the mentioned parts of the bipartite graph using the theory of hypothetical functions. Different elements of a bipartite graph can interact using edges. Therefore, modeling the interaction of elements can be transformed into modeling the interaction of nodes on the corresponding graph. We implemented a recommender system model in Python and used relevant libraries, which we tested on standard datasets. These experiments showed the good ability of our model for recommendations. We used the root mean square error (RMSE) and mean absolute error (MAE) indicators.

Název v anglickém jazyce

A User Recommendation System Based on Graph Neural Network and Contextual Behavior

Popis výsledku anglicky

Today, recommendation systems are an integral part of e-commerce services on the Internet. In connection with their development, neural networks have become the most used approach to recommender systems. In our post, we will demonstrate the use of graph neural networks to create a recommender system. E-commerce systems can be modeled using a bipartite interaction graph. There are two essential parts to this chart, users and items. In our model, context is added to them and integrated into the mentioned parts of the bipartite graph using the theory of hypothetical functions. Different elements of a bipartite graph can interact using edges. Therefore, modeling the interaction of elements can be transformed into modeling the interaction of nodes on the corresponding graph. We implemented a recommender system model in Python and used relevant libraries, which we tested on standard datasets. These experiments showed the good ability of our model for recommendations. We used the root mean square error (RMSE) and mean absolute error (MAE) indicators.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 41st International Conference on Mathematical Methods in Economics

ISBN

978-80-11-04132-8

ISSN

2788-3965

e-ISSN

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Počet stran výsledku

6

Strana od-do

111-116

Název nakladatele

Czech Society for Operations Research

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

13. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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