Recommender system for online shopping
Many websites enable users to express their special interests in new, engaging ways, to offer authentic, high value connectivity with new people they do not already know and help them find the right items to purchase. The objective of this project is to (1) develop new learning- to-rank algorithms...
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| Format: | Final Year Project |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175015 |
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sg-ntu-dr.10356-1750152024-04-19T15:46:08Z Recommender system for online shopping Wang, Jun Jie Zhang Jie School of Computer Science and Engineering ZhangJ@ntu.edu.sg Computer and Information Science Engineering Many websites enable users to express their special interests in new, engaging ways, to offer authentic, high value connectivity with new people they do not already know and help them find the right items to purchase. The objective of this project is to (1) develop new learning- to-rank algorithms for ranking users for Whom-to-Follow, and (2) develop new methods to infer users' preference from their implicit feedback. Bachelor's degree 2024-04-18T07:16:37Z 2024-04-18T07:16:37Z 2024 Final Year Project (FYP) Wang, J. J. (2024). Recommender system for online shopping. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175015 https://hdl.handle.net/10356/175015 en PSCSE22-0046 application/pdf Nanyang Technological University |
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Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
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Singapore Singapore |
| content_provider |
NTU Library |
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DR-NTU |
| language |
English |
| topic |
Computer and Information Science Engineering |
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Computer and Information Science Engineering Wang, Jun Jie Recommender system for online shopping |
| description |
Many websites enable users to express their special interests in new, engaging ways, to offer authentic, high value connectivity with new people they do not already know and help them find the right items to purchase. The objective of this project is to (1) develop new learning-
to-rank algorithms for ranking users for Whom-to-Follow, and (2) develop new methods to infer users'
preference from their implicit feedback. |
| author2 |
Zhang Jie |
| author_facet |
Zhang Jie Wang, Jun Jie |
| format |
Final Year Project |
| author |
Wang, Jun Jie |
| author_sort |
Wang, Jun Jie |
| title |
Recommender system for online shopping |
| title_short |
Recommender system for online shopping |
| title_full |
Recommender system for online shopping |
| title_fullStr |
Recommender system for online shopping |
| title_full_unstemmed |
Recommender system for online shopping |
| title_sort |
recommender system for online shopping |
| publisher |
Nanyang Technological University |
| publishDate |
2024 |
| url |
https://hdl.handle.net/10356/175015 |
| _version_ |
1800916222033788928 |