A hybrid recommender system for an online store using a fuzzy expert system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F23%3AA2402GNH" target="_blank" >RIV/61988987:17310/23:A2402GNH - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417422016293" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417422016293</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2022.118565" target="_blank" >10.1016/j.eswa.2022.118565</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid recommender system for an online store using a fuzzy expert system

Popis výsledku v původním jazyce

Nowadays, various recommender systems are popular and their main aim is to recommend suitable content to the user based on various parameters. This article proposes a hybrid recommender system, Eshop recommender, which combines a recommender module composed of three subsystems (the subsystems use collaborative-filtering and content-based approaches) and a fuzzy expert system. It is an e-shopping recommender system for suggesting suitable products. The system works with different user preferences and their activity on the e-shop, and the resulting list of recommended products is created using the fuzzy expert system. The expert system works with several parameters - similarity level with already rated products, coefficient of purchased product, and an average rating of the product. Due to this, our proposed system achieves promising results based on standard metrics (Precision, Recall, F1-measure). The system achieves results above 90%. The system also achieves better results than traditional approaches. The main contribution is creating a comprehensive hybrid system in the area of product recommendation in an online store, which has been validated on a group of real users and compared with other traditional approaches and the recommendation module of another online store.

Název v anglickém jazyce

A hybrid recommender system for an online store using a fuzzy expert system

Popis výsledku anglicky

Nowadays, various recommender systems are popular and their main aim is to recommend suitable content to the user based on various parameters. This article proposes a hybrid recommender system, Eshop recommender, which combines a recommender module composed of three subsystems (the subsystems use collaborative-filtering and content-based approaches) and a fuzzy expert system. It is an e-shopping recommender system for suggesting suitable products. The system works with different user preferences and their activity on the e-shop, and the resulting list of recommended products is created using the fuzzy expert system. The expert system works with several parameters - similarity level with already rated products, coefficient of purchased product, and an average rating of the product. Due to this, our proposed system achieves promising results based on standard metrics (Precision, Recall, F1-measure). The system achieves results above 90%. The system also achieves better results than traditional approaches. The main contribution is creating a comprehensive hybrid system in the area of product recommendation in an online store, which has been validated on a group of real users and compared with other traditional approaches and the recommendation module of another online store.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

—

Číslo periodika v rámci svazku

February 2023

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

000870936600008

EID výsledku v databázi Scopus

2-s2.0-85137594859