Face Based Advertisement Recommendation with Deep Learning: A Case Study

  • Xiaozhe Yao
  • Yingying Chen
  • Rongjie Liao
  • Shubin CaiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10699)


Recently, there is a massive growth of the offline advertising industries. To increase the performance of offline advertising, researchers bring out several methodologies.

However, the existing advertisement serving schemes are accustomed to focusing on traditional print media, resulting in the lack of personality and impression. Meanwhile, we find that facial features such as age, gender, can help us classify consumers intuitively and rapidly so that it can raise the accuracy in recommendation in a short time. Motivated by an original idea, we offer a Face Based Advertisement Recommendation System (FBARS). We propose that the FBARS works well in offline scenario and basically it could raise the accuracy 4 times. it performs 4 times better than the classic method using collaborative filtering.


Computer vision Recommendation system Deep learning Face recognition Advertising 



I would like to extend my sincere gratitude to Professor Shiqi Yu, for his instruction on face detection on this paper. I am deeply appreciate for his help.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Xiaozhe Yao
    • 1
  • Yingying Chen
    • 1
  • Rongjie Liao
    • 1
  • Shubin Cai
    • 1
    Email author
  1. 1.Shenzhen UniversityShenzhenChina

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