Modern fruits web store with personalized recommender system
E-commerce is emerged in the last few years. With the current demanding trend and existing technology, people can start selling and buying anything online virtually. This project will develop an online fruit and fruit juice web application. Besides, there will be a discussion about the strength and...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Online Access: | http://eprints.utar.edu.my/5791/ http://eprints.utar.edu.my/5791/1/fyp_CS_2023_LJP.pdf |
| _version_ | 1848886505828777984 |
|---|---|
| author | Lim, Jun Peng |
| author_facet | Lim, Jun Peng |
| author_sort | Lim, Jun Peng |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | E-commerce is emerged in the last few years. With the current demanding trend and existing technology, people can start selling and buying anything online virtually. This project will develop an online fruit and fruit juice web application. Besides, there will be a discussion about the strength and weakness of the current existing system. The system architecture, framework is also being reviewed before building the web application which will provide support on understanding the concept, methodology and also the design of full-stack web application. After the literature review, the research tells that some existing systems do not actually implement the recommender systems to provide product recommendations. So, the main goal and objective of this project is to develop a well-developed online fruit and fruit juice web application with the recommender system. The techniques being chosen for the recommender system testing are the non-personalized popularity-based and personalized collaborative-based filtering covering user-based and item-based. ASP.NET Core MVC is the main programming framework to build this e-commerce project and deploy using Azure services. The proposed system is a full stack web ap plication which can involve the communication between the front-end framework and the back-end framework. |
| first_indexed | 2025-11-15T19:39:34Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-5791 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:39:34Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-57912023-09-08T14:29:35Z Modern fruits web store with personalized recommender system Lim, Jun Peng Q Science (General) T Technology (General) E-commerce is emerged in the last few years. With the current demanding trend and existing technology, people can start selling and buying anything online virtually. This project will develop an online fruit and fruit juice web application. Besides, there will be a discussion about the strength and weakness of the current existing system. The system architecture, framework is also being reviewed before building the web application which will provide support on understanding the concept, methodology and also the design of full-stack web application. After the literature review, the research tells that some existing systems do not actually implement the recommender systems to provide product recommendations. So, the main goal and objective of this project is to develop a well-developed online fruit and fruit juice web application with the recommender system. The techniques being chosen for the recommender system testing are the non-personalized popularity-based and personalized collaborative-based filtering covering user-based and item-based. ASP.NET Core MVC is the main programming framework to build this e-commerce project and deploy using Azure services. The proposed system is a full stack web ap plication which can involve the communication between the front-end framework and the back-end framework. 2023-01 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/5791/1/fyp_CS_2023_LJP.pdf Lim, Jun Peng (2023) Modern fruits web store with personalized recommender system. Final Year Project, UTAR. http://eprints.utar.edu.my/5791/ |
| spellingShingle | Q Science (General) T Technology (General) Lim, Jun Peng Modern fruits web store with personalized recommender system |
| title | Modern fruits web store with personalized recommender system |
| title_full | Modern fruits web store with personalized recommender system |
| title_fullStr | Modern fruits web store with personalized recommender system |
| title_full_unstemmed | Modern fruits web store with personalized recommender system |
| title_short | Modern fruits web store with personalized recommender system |
| title_sort | modern fruits web store with personalized recommender system |
| topic | Q Science (General) T Technology (General) |
| url | http://eprints.utar.edu.my/5791/ http://eprints.utar.edu.my/5791/1/fyp_CS_2023_LJP.pdf |