Hybrid deep modelling with human knowledge in practical e-commerce search
In recent years, recommender systems have become increasingly important in the E-commerce marketplaces (e.g., Alibaba, Amazon). According to public information, recommender systems have contributed 30% gross merchandise value (GMV) for Amazon. Significant effort has been devoted into this researc...
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sg-ntu-dr.10356-1739882024-04-09T03:58:58Z Hybrid deep modelling with human knowledge in practical e-commerce search Zeng, Anxiang Miao Chun Yan Yu Han School of Computer Science and Engineering han.yu@ntu.edu.sg, ASCYMiao@ntu.edu.sg Computer and Information Science E-commerce Deep learning In recent years, recommender systems have become increasingly important in the E-commerce marketplaces (e.g., Alibaba, Amazon). According to public information, recommender systems have contributed 30% gross merchandise value (GMV) for Amazon. Significant effort has been devoted into this research field by the industry and the academic community. Research works making key advances in recommendation systems can be divided into matrix factorization (MF), collaborative filtering (CF) and click-through rate (CTR) prediction. Currently, in industrial e-commerce recommendation systems, the main adopted approach is to use CF methods for performing recall tasks and deep learning-based CTR prediction methods for performing ranking tasks. However, significant challenges remain when these algorithms are to be deployed into practical e-commerce environments. In the system of search, most of the ctr modeling use big data, with a large number of user behavior data. The amount of data is a large number, which is very helpful to build the model. At the same time, these data also contains a lot of noise. This can lead to inaccurate modeling. In many cases, these behaviors are not consistent with business needs, we need to make manual guidance and intervention to the model. Doctor of Philosophy 2024-03-11T08:10:57Z 2024-03-11T08:10:57Z 2023 Thesis-Doctor of Philosophy Zeng, A. (2023). Hybrid deep modelling with human knowledge in practical e-commerce search. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173988 https://hdl.handle.net/10356/173988 10.32657/10356/173988 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
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In recent years, recommender systems have become increasingly important in the
E-commerce marketplaces (e.g., Alibaba, Amazon). According to public information,
recommender systems have contributed 30% gross merchandise value (GMV)
for Amazon. Significant effort has been devoted into this research field by the
industry and the academic community. Research works making key advances in
recommendation systems can be divided into matrix factorization (MF), collaborative
filtering (CF) and click-through rate (CTR) prediction.
Currently, in industrial e-commerce recommendation systems, the main adopted
approach is to use CF methods for performing recall tasks and deep learning-based
CTR prediction methods for performing ranking tasks. However, significant challenges
remain when these algorithms are to be deployed into practical e-commerce
environments.
In the system of search, most of the ctr modeling use big data, with a large number
of user behavior data. The amount of data is a large number, which is very helpful
to build the model. At the same time, these data also contains a lot of noise. This
can lead to inaccurate modeling. In many cases, these behaviors are not consistent
with business needs, we need to make manual guidance and intervention to the
model. |
author2 |
Miao Chun Yan |
author_facet |
Miao Chun Yan Zeng, Anxiang |
format |
Thesis-Doctor of Philosophy |
author |
Zeng, Anxiang |
author_sort |
Zeng, Anxiang |
title |
Hybrid deep modelling with human knowledge in practical e-commerce search |
title_short |
Hybrid deep modelling with human knowledge in practical e-commerce search |
title_full |
Hybrid deep modelling with human knowledge in practical e-commerce search |
title_fullStr |
Hybrid deep modelling with human knowledge in practical e-commerce search |
title_full_unstemmed |
Hybrid deep modelling with human knowledge in practical e-commerce search |
title_sort |
hybrid deep modelling with human knowledge in practical e-commerce search |
publisher |
Nanyang Technological University |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/173988 |
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1800916355250126848 |