Recommendation of who-to-follow and what-to-buy
The advancement of technology occurs so fast that it is often difficult to keep up with. This phenomenon affects a lot of things, and one of them is the e-commerce industry. With the rapidly growing user base of the industry, the amount of information also increases significantly. Fortunately, techn...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/10356/75786 |
| _version_ | 1811680512723386368 |
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| author | Prasetya, Steve Alexander |
| author2 | Zhang Jie |
| author_facet | Zhang Jie Prasetya, Steve Alexander |
| author_sort | Prasetya, Steve Alexander |
| collection | NTU |
| description | The advancement of technology occurs so fast that it is often difficult to keep up with. This phenomenon affects a lot of things, and one of them is the e-commerce industry. With the rapidly growing user base of the industry, the amount of information also increases significantly. Fortunately, technology advances allow the industry to be on par in terms of data processing. The increasing popularity of online shopping has posed several problems for both buyers and sellers alike. Sometimes it is not quite straightforward to determine which items to display on the main page specific to each individual user. Should this happen, there would be an increased probability for the user to purchase items that are in tandem with his preferences. One such method to address this is called Collaborative Filtering, which is something used by Recommender Systems. It works by utilizing several other users’ preferences and matching it to one specific user to predict his. This project looks on a recommender system that is the Librec library and implements a user interface such that it is easier for developers in the e-commerce industry to analyze results and select which algorithms are worth using in the business. This is done by creating a Java application using a set of graphics and media packages called JavaFX. |
| first_indexed | 2024-10-01T03:26:14Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/75786 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:26:14Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/757862023-03-03T20:38:12Z Recommendation of who-to-follow and what-to-buy Prasetya, Steve Alexander Zhang Jie School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering The advancement of technology occurs so fast that it is often difficult to keep up with. This phenomenon affects a lot of things, and one of them is the e-commerce industry. With the rapidly growing user base of the industry, the amount of information also increases significantly. Fortunately, technology advances allow the industry to be on par in terms of data processing. The increasing popularity of online shopping has posed several problems for both buyers and sellers alike. Sometimes it is not quite straightforward to determine which items to display on the main page specific to each individual user. Should this happen, there would be an increased probability for the user to purchase items that are in tandem with his preferences. One such method to address this is called Collaborative Filtering, which is something used by Recommender Systems. It works by utilizing several other users’ preferences and matching it to one specific user to predict his. This project looks on a recommender system that is the Librec library and implements a user interface such that it is easier for developers in the e-commerce industry to analyze results and select which algorithms are worth using in the business. This is done by creating a Java application using a set of graphics and media packages called JavaFX. Bachelor of Engineering (Computer Science) 2018-06-14T06:14:46Z 2018-06-14T06:14:46Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75786 en Nanyang Technological University 28 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Prasetya, Steve Alexander Recommendation of who-to-follow and what-to-buy |
| title | Recommendation of who-to-follow and what-to-buy |
| title_full | Recommendation of who-to-follow and what-to-buy |
| title_fullStr | Recommendation of who-to-follow and what-to-buy |
| title_full_unstemmed | Recommendation of who-to-follow and what-to-buy |
| title_short | Recommendation of who-to-follow and what-to-buy |
| title_sort | recommendation of who to follow and what to buy |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/75786 |
| work_keys_str_mv | AT prasetyastevealexander recommendationofwhotofollowandwhattobuy |