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BoardWatch: a tree-enhanced regression model for billboard popularity prediction with multi-source urban data

Published: 09 September 2019 Publication History

Abstract

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.

References

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Bin Guo et al. 2018. Citytransfer: Transferring inter-and intra-city knowledge for chain store site recommendation based on multi-source urban data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 135.
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Xinran He et al. 2014. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising. ACM, 1--9.
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Dmytro Karamshuk et al. 2013. Geo-spotting: mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 793--801.
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Yuhong Li et al. 2016. Mining the most influential k-location set from massive trajectories. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 51.
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Xiang Wang et al. 2018. Tem: Tree-enhanced embedding model for explainable recommendation. In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1543--1552.
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Ping Zhang et al. 2018. Trajectory-driven influential billboard placement. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2748--2757.

Cited By

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  • (2024)AdvMOB: Interactive visual analytic system of billboard advertising exposure analysis based on urban digital twin techniqueAdvanced Engineering Informatics10.1016/j.aei.2024.10282962(102829)Online publication date: Oct-2024
  • (2022)$O^{2}$-SiteRec: Store Site Recommendation under the O2O Model via Multi-graph Attention Networks2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00044(525-538)Online publication date: May-2022

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  1. BoardWatch: a tree-enhanced regression model for billboard popularity prediction with multi-source urban data

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      cover image ACM Conferences
      UbiComp/ISWC '19 Adjunct: 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
      September 2019
      1234 pages
      ISBN:9781450368698
      DOI:10.1145/3341162
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 09 September 2019

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      Author Tags

      1. billboard
      2. cross-space data
      3. decision trees
      4. popularity prediction

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      • National Key R&D Program of China

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      UbiComp '19

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      Overall Acceptance Rate 764 of 2,912 submissions, 26%

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      View all
      • (2024)AdvMOB: Interactive visual analytic system of billboard advertising exposure analysis based on urban digital twin techniqueAdvanced Engineering Informatics10.1016/j.aei.2024.10282962(102829)Online publication date: Oct-2024
      • (2022)$O^{2}$-SiteRec: Store Site Recommendation under the O2O Model via Multi-graph Attention Networks2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00044(525-538)Online publication date: May-2022

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