Report for Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
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DOI: 10.23977/ICCIA2020002
Corresponding Author
Xiaobing Yu
ABSTRACT
The study conducted by Wen et al. (2019) proposes a multi-Level Deep Cascade Trees Model (IdcTree) for conversion rate prediction in the recommendation system. The report introduces the background of recommendation systems used in the e-commerce industry and the proposed IdcTree for conversion rate prediction. The history and the challenges associated with DNN in previous work are carried out in the previous work of this study. The report also summarized in detail the main techniques and results of this study conducted by Wen et al. (2019) which establishes a basis for future research to investigate incorporating more features and other improvements that can improve efficiency in deep learning.
KEYWORDS
IdcTree; DNN; e-commerce; deep learning