NDLI: Time-Dependent Demographic Prediction Based on Time-Back-Propagation Method

Content Provider	IET Digital Library
Author	Ke, Yuyang Xiong, Yan Hu, Yiqing Liu, Shichen
Abstract	In various applications like personalized search and recommendation, full demographic information is a precondition for many applications' well performance, but such ideal dataset rarely exists in practical scenarios. What's worse, absence of key characteristics (e.g., baby's age in maternal and infant commodity recommendation) makes these applications struggle. We design a novel solution to solve the problem of time-dependent demographic prediction. The key insight behind our approach is, we leverage a Time-back-propagation (TBP) method to take the internal time correlation of historical behaviours into consideration and collect all available data to train a classifier, which is a mapping from user's historical behaviours to the demographic information.We demonstrate the effectiveness of our method through experiments of baby's age prediction. Our algorithm performs more balanced on each age group, and can predict Baby's age (BA) accurately in 78.2% on a real-world dataset with 2,058,909 items of a major E-commerce site.
Starting Page	35
Ending Page	40
Page Count	6
ISSN	10224653
Volume Number	27
e-ISSN	20755597
Issue Number	Issue 1, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/27/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2017.09.002
Journal	Chinese Journal of Electronics
Publisher Date	2018-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Backpropagation Data Handling Technique Demographic Information Demography E-commerce Site Historical Behaviour Internal Time Correlation Neural Computing Technique Pattern Classification Personalized Search Recommendation System Social And Behavioural Sciences Computing Social Sciences Computing TBP Method Time-backpropagation Method Time-dependent Demographic Prediction
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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