<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.</p>
</abstract>

Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.

An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Department of Software Engineering, Faculty of Information Technology

Department of Networks and Information Security, Faculty of Information Technology

Department of Computer Science, Faculty of Information Technology

Department of Computer Information Systems

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a...

An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Publié en ligne: 30 mars 2021

Pages: 103 - 118

Reçu: 26 oct. 2020

Accepté: 22 janv. 2021

DOI: https://doi.org/10.2478/cait-2021-0008

Mots clés
e-Commerce, Recommender systems, Collaborative filtering, Multi-criteria, Trust network, Semantic similarity

© 2021 Qusai Y. Shambour et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Publié en ligne: 30 mars 2021

Pages: 103 - 118

Reçu: 26 oct. 2020

Accepté: 22 janv. 2021

DOI: https://doi.org/10.2478/cait-2021-0008

Mots clése-Commerce, Recommender systems, Collaborative filtering, Multi-criteria, Trust network, Semantic similarity

© 2021 Qusai Y. Shambour et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
e-Commerce, Recommender systems, Collaborative filtering, Multi-criteria, Trust network, Semantic similarity