About: In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.   Goto Sponge  NotDistinct  Permalink

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  • In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.
subject
  • Machine learning
  • Information theory
  • Classification algorithms
  • Statistical classification
  • Computational statistics
  • Decision theory
  • Decision trees
  • Ensemble learning
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