Face Based Advertisement Recommendation with Deep Learning: A Case Study

Published in International Conference on Smart Computing and Communication, 2017

Recommended citation: X Yao, Y Chen, R Liao, S Cai - International Conference on Smart Computing and …, 2017 https://link.springer.com/chapter/10.1007/978-3-319-73830-7_10

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.