Multi-Label Classification with Deep Learning for Retail Recommendation
Selecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different...
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| Format: | Article |
| Language: | English |
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MMU Press
2023-09-01
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| Series: | Journal of Informatics and Web Engineering |
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| Online Access: | https://journals.mmupress.com/index.php/jiwe/article/view/775 |
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| _version_ | 1846137884586803200 |
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| author | Zhi Yuan Poo Choo Yee Ting Yuen Peng Loh Khairil Imran Ghauth |
| author_facet | Zhi Yuan Poo Choo Yee Ting Yuen Peng Loh Khairil Imran Ghauth |
| author_sort | Zhi Yuan Poo |
| collection | DOAJ |
| description | Selecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different classes or categories based on various features or attributes. Existing research in the field of retail recommendation has extensively proposed and evaluated different algorithms, techniques, and approaches for multi-class classification in the context of retail recommendation, however, limited work has been focusing on formulating retail recommendation as a multi-label problem. This is because in retail recommendation, one location can fit multiple retail businesses so that it can provide more options to recommend the most suitable business for the location. Therefore, multi-label classification will be attempted in this study. An analytical dataset will be constructed that provides comprehensive insights into the characteristics of the business area, and subsequently employ deep learning technique for multi-label classification. The analytical dataset is constructed based on the list of sites of interest data from YellowPages, population data from Humanitarian Data Exchange (HDX) and property data sourced from brickz.my. This work will be focusing on implement deep learning technique which is 1D convolutional neural network (CNN) model. The findings showed that the proposed model achieved 61.22% in terms of accuracy. |
| format | Article |
| id | doaj-art-7d572f99dd7e4153a135cdfeeaf18ad8 |
| institution | Kabale University |
| issn | 2821-370X |
| language | English |
| publishDate | 2023-09-01 |
| publisher | MMU Press |
| record_format | Article |
| series | Journal of Informatics and Web Engineering |
| spelling | doaj-art-7d572f99dd7e4153a135cdfeeaf18ad82024-12-08T04:03:27ZengMMU PressJournal of Informatics and Web Engineering2821-370X2023-09-012221823210.33093/jiwe.2023.2.2.16774Multi-Label Classification with Deep Learning for Retail RecommendationZhi Yuan Poo0https://orcid.org/0009-0005-6443-8794Choo Yee Ting1Yuen Peng Loh2Khairil Imran Ghauth3Multimedia University, MalaysiaMultimedia University, MalaysiaMultimedia University, MalaysiaMultimedia University, MalaysiaSelecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different classes or categories based on various features or attributes. Existing research in the field of retail recommendation has extensively proposed and evaluated different algorithms, techniques, and approaches for multi-class classification in the context of retail recommendation, however, limited work has been focusing on formulating retail recommendation as a multi-label problem. This is because in retail recommendation, one location can fit multiple retail businesses so that it can provide more options to recommend the most suitable business for the location. Therefore, multi-label classification will be attempted in this study. An analytical dataset will be constructed that provides comprehensive insights into the characteristics of the business area, and subsequently employ deep learning technique for multi-label classification. The analytical dataset is constructed based on the list of sites of interest data from YellowPages, population data from Humanitarian Data Exchange (HDX) and property data sourced from brickz.my. This work will be focusing on implement deep learning technique which is 1D convolutional neural network (CNN) model. The findings showed that the proposed model achieved 61.22% in terms of accuracy.https://journals.mmupress.com/index.php/jiwe/article/view/775multi-label classificationdeep learning1d convolutional neural network (cnn) retail recommendationyellowpages |
| spellingShingle | Zhi Yuan Poo Choo Yee Ting Yuen Peng Loh Khairil Imran Ghauth Multi-Label Classification with Deep Learning for Retail Recommendation Journal of Informatics and Web Engineering multi-label classification deep learning 1d convolutional neural network (cnn) retail recommendation yellowpages |
| title | Multi-Label Classification with Deep Learning for Retail Recommendation |
| title_full | Multi-Label Classification with Deep Learning for Retail Recommendation |
| title_fullStr | Multi-Label Classification with Deep Learning for Retail Recommendation |
| title_full_unstemmed | Multi-Label Classification with Deep Learning for Retail Recommendation |
| title_short | Multi-Label Classification with Deep Learning for Retail Recommendation |
| title_sort | multi label classification with deep learning for retail recommendation |
| topic | multi-label classification deep learning 1d convolutional neural network (cnn) retail recommendation yellowpages |
| url | https://journals.mmupress.com/index.php/jiwe/article/view/775 |
| work_keys_str_mv | AT zhiyuanpoo multilabelclassificationwithdeeplearningforretailrecommendation AT chooyeeting multilabelclassificationwithdeeplearningforretailrecommendation AT yuenpengloh multilabelclassificationwithdeeplearningforretailrecommendation AT khairilimranghauth multilabelclassificationwithdeeplearningforretailrecommendation |