Abstract
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.
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Acknowledgement
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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Yao, X., Chen, Y., Liao, R., Cai, S. (2018). Face Based Advertisement Recommendation with Deep Learning: A Case Study. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2017. Lecture Notes in Computer Science(), vol 10699. Springer, Cham. https://doi.org/10.1007/978-3-319-73830-7_10
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DOI: https://doi.org/10.1007/978-3-319-73830-7_10
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