A map-based gender prediction model for big E-commerce data

Ling Cen, Dymitr Ruta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Gender differences have been found in costumer behavior and perceptions of the benefits, concerns and important attributes of e-commerce, although related information is in general not available. In this paper, a map based gender predictive model is proposed for predicting the gender types of e-commerce participants from their product viewing records by exploiting the hierarchy of the online products classification structure at several hierarchy levels from top categories down to individual products. In our method, the gender types are estimated by mapping the category transition probability of viewed products based on the assumption that the transition process satisfies first-order Markov property. A comparative adjustment is applied for probability smoothing in order to avoid discontinuity with zero values in probability estimates. An iterative learning scheme with incremental data training by adding 'reliable' testing data instances into training in an iterative way is developed to strengthen the model learning and improve the robustness of model predictions. The proposed method was evaluated by using the data provided in PAKDD'2015 Data Mining Competition. The balanced accuracy we achieved in the 1st iteration is 80.73%, and finally through ten iterations of learning process, is increased to 81.07% scoring the 10th place out of 330 international teams' submissions.

Original languageBritish English
Title of host publicationProceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
EditorsGeyong Min, Xiaolong Jin, Laurence T. Yang, Yulei Wu, Nektarios Georgalas, Ahmed Al-Dubi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1025-1029
Number of pages5
ISBN (Electronic)9781538630655
DOIs
StatePublished - 30 Jan 2018
EventJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 - Exeter, United Kingdom
Duration: 21 Jun 201723 Jun 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
Volume2018-January

Conference

ConferenceJoint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017
Country/TerritoryUnited Kingdom
CityExeter
Period21/06/1723/06/17

Keywords

  • E-commerce
  • Gender prediction
  • Markov property
  • Transition probability

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