An Effective e-Commerce Recommender System Based on Trust and Semantic Information
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 p...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2021-03-01
|
| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2021-0008 |
| _version_ | 1818308914873106432 |
|---|---|
| author | Shambour Qusai Y. Turab Nidal M. Adwan Omar Y. |
| author_facet | Shambour Qusai Y. Turab Nidal M. Adwan Omar Y. |
| author_sort | Shambour Qusai Y. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T07:21:51Z |
| format | Article |
| id | doaj.art-eefe0f6df5dd469f87a0b1098a3f08f3 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-12-13T07:21:51Z |
| publishDate | 2021-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-eefe0f6df5dd469f87a0b1098a3f08f32022-12-21T23:55:25ZengSciendoCybernetics and Information Technologies1314-40812021-03-0121110311810.2478/cait-2021-0008An Effective e-Commerce Recommender System Based on Trust and Semantic InformationShambour Qusai Y.0Turab Nidal M.1Adwan Omar Y.2Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, JordanDepartment of Networks and Information Security, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, JordanDepartment of Computer Science, Faculty of Information Technology, Al-Ahliyya Amman University, Amman, JordanElectronic 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.https://doi.org/10.2478/cait-2021-0008e-commercerecommender systemscollaborative filteringmulti-criteriatrust networksemantic similarity |
| spellingShingle | Shambour Qusai Y. Turab Nidal M. Adwan Omar Y. An Effective e-Commerce Recommender System Based on Trust and Semantic Information Cybernetics and Information Technologies e-commerce recommender systems collaborative filtering multi-criteria trust network semantic similarity |
| title | An Effective e-Commerce Recommender System Based on Trust and Semantic Information |
| title_full | An Effective e-Commerce Recommender System Based on Trust and Semantic Information |
| title_fullStr | An Effective e-Commerce Recommender System Based on Trust and Semantic Information |
| title_full_unstemmed | An Effective e-Commerce Recommender System Based on Trust and Semantic Information |
| title_short | An Effective e-Commerce Recommender System Based on Trust and Semantic Information |
| title_sort | effective e commerce recommender system based on trust and semantic information |
| topic | e-commerce recommender systems collaborative filtering multi-criteria trust network semantic similarity |
| url | https://doi.org/10.2478/cait-2021-0008 |
| work_keys_str_mv | AT shambourqusaiy aneffectiveecommercerecommendersystembasedontrustandsemanticinformation AT turabnidalm aneffectiveecommercerecommendersystembasedontrustandsemanticinformation AT adwanomary aneffectiveecommercerecommendersystembasedontrustandsemanticinformation AT shambourqusaiy effectiveecommercerecommendersystembasedontrustandsemanticinformation AT turabnidalm effectiveecommercerecommendersystembasedontrustandsemanticinformation AT adwanomary effectiveecommercerecommendersystembasedontrustandsemanticinformation |