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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173988 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | 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. |
---|