TY - GEN
T1 - Boardwatch
T2 - 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019
AU - Jing, Yao
AU - Guo, Bin
AU - Liu, Yan
AU - Zhang, Daqing
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/9/9
Y1 - 2019/9/9
N2 - Predicting the popularity of outdoor billboards is crucial for many applications such as guidance of billboard placement and estimation of advertising cost. Recently, some researchers have worked on leveraging single traffic data to access the performance of billboards, which often leads to coarse-grained performance estimation and undesirable ad placement plans. To solve the problem, we propose a data-driven system, named BoradWatch, for fine-grained billboard popularity prediction. In particular, we extract three types of critical features based on multi-source urban data, including billboard profile, geographic feature and commercial feature. Furthermore, we propose a hybrid model named Tree-Enhanced Regression Model (TERM) based on extracted features for prediction, which takes full advantage of the feature transformation of decision trees model to enhance the prediction performance of the linear model. Experiment results on real-world outdoor billboard data and multi-source urban data demonstrate the effectiveness of our work.
AB - Predicting the popularity of outdoor billboards is crucial for many applications such as guidance of billboard placement and estimation of advertising cost. Recently, some researchers have worked on leveraging single traffic data to access the performance of billboards, which often leads to coarse-grained performance estimation and undesirable ad placement plans. To solve the problem, we propose a data-driven system, named BoradWatch, for fine-grained billboard popularity prediction. In particular, we extract three types of critical features based on multi-source urban data, including billboard profile, geographic feature and commercial feature. Furthermore, we propose a hybrid model named Tree-Enhanced Regression Model (TERM) based on extracted features for prediction, which takes full advantage of the feature transformation of decision trees model to enhance the prediction performance of the linear model. Experiment results on real-world outdoor billboard data and multi-source urban data demonstrate the effectiveness of our work.
KW - Billboard
KW - Cross-space data
KW - Decision trees
KW - Popularity prediction
U2 - 10.1145/3341162.3343826
DO - 10.1145/3341162.3343826
M3 - Conference contribution
AN - SCOPUS:85072882907
T3 - UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
SP - 93
EP - 96
BT - UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
Y2 - 9 September 2019 through 13 September 2019
ER -