Enhancing personalized ads using interest category classification of SNS users based on deep neural networks

Taekeun Hong, Jin A. Choi, Kiho Lim, Pankoo Kim

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-gener-ated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and in-forms marketers on making (1) interest-based recommendations, (2) ranked-order recommenda-tions, and (3) real-time recommendations.

Original languageEnglish
Article number199
Pages (from-to)1-17
Number of pages17
JournalSensors (Switzerland)
Volume21
Issue number1
DOIs
StatePublished - 1 Jan 2021

Keywords

  • Deep learning
  • Interest classification
  • Neural networks
  • Personalized ads
  • SNS

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