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

作　　者：KE Yuyang XIONG Yan HU Yiqing LIU Shichen 

机构地区：[1]School of Computer Science, University of Science and Technology of China [2]Department of Computer Science, Hefei University

出　　处：《Chinese Journal of Electronics》2018年第1期35-40,共6页电子学报（英文版）

基　　金：supported by the National Natural Science Foundationof China(No.61202404,No.61170233,No.61232018,No.61272472,No.61272317);the Fundamental Research Funds for the Central Universities(No.WK0110000041)

摘　　要：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 ma jor E-commerce site.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 ma jor E-commerce site.

关 键 词：Time-back-propagation(TBP) Time series Time-dependent demographic prediction 

分 类 号：TN[电子电信]