Effective Optimization of Billboard Ads Based on CDR Data Leverage
Main Article Content
Keywords
Call Detail Records, Rating scores, Outdoor advertising, Billboards
Abstract
Call Detail Records (CDRs) provide metadata about phone calls and text message usage. Many studies have shown these CDR data to provide gainful information on people's mobility patterns and relationships with fine-grained aspects, both temporal and spatial elements. This information allows tracking population levels in each country region, individual movements, seasonal locations, population changes, and migration. This paper introduces a method for analyzing and exploiting CDR data to recommend billboard ads. We usher by clustering the locations based on the recorded activities' pattern regarding users' mobility. The key idea is to rate sites by performing a thorough cluster analysis over the achieved data, having no prior ground-truth information, to assess and optimize the ads' placements and timing for more efficiency at the billboards.
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References
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Bianchi, F. M., Rizzi, A., Sadeghian, A., & Moiso, C. (2016). Identifying user habits through data mining on call data records. Engineering Applications of Artificial Intelligence, 54, 49–61. https://doi.org/10.1016/j.engappai.2016.05.007
Bianchi, F. M., Scardapane, S., Uncini, A., Rizzi, A., & Sadeghian, A. (2015). Prediction of telephone calls load using Echo State Network with exogenous variables. Neural Networks, 71, 204–213. https://doi.org/https://doi.org/10.1016/j.neunet.2015.08.010
CTA [Consumer Technology Association]. (2017, July). How mobile phones are changing the developing world? Retrieved from https://www.cta.tech/News/Blog/Articles/2015/July/How-Mobile-Phones-Are-Changing-the-Developing-Worl.aspx
Cuzzocrea, A., Ferri, F., & Grifoni, P. (2018). Intelligent Sensor Data Fusion for Supporting Advanced Smart Health Processes. In L. Barolli & O. Terzo (Eds), Complex, Intelligent, and Software Intensive Systems, 611, 361–370. https://doi.org/10.1007/978-3-319-61566-0_33
de Montjoye, Y.-A., Zbigniew, S., Romain, T., Cezary, Z., & D. Blondel, V. (2014). D4D-Senegal: The Second Mobile Phone Data for Development Challenge. CoRR, 1–9. https://doi.org/10.48550/arXiv.1407.4885
DeAlmeida, J. M., Pontes, C. F. T., DaSilva, L. A., Both, C. B., Gondim, J. J. C., Ralha, C. G., & Marotta, M. A. (2021). Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Networks. IEEE Transactions on Network and Service Management, 18(4), 4213–4224. https://doi.org/10.1109/TNSM.2021.3125019
Ficek, M., & Kencl, L. (2012). Inter-call mobility model: A Spatio-temporal refinement of call data records using a Gaussian mixture model. 2012 Proceedings IEEE INFOCOM, 469–477. https://doi.org/10.1109/INFCOM.2012.6195786
Gore, R., Wozny, P., Dignum, F. P. M., Shults, F. L., van Burken, C. B., & Royakkers, L. (2019). A Value Sensitive ABM of the Refugee Crisis in the Netherlands. Proceeding 2019 Spring Simulation Conference (SpringSim), 1–12. https://doi.org/10.23919/SpringSim.2019.8732867
Gross Rating Point (GRP). (2020). Retrieved from https://marketing-dictionary.org/g/gross-rating-point/
Hiir, H., Sharma, R., Aasa, A., & Saluveer, E. (2019). Impact of Natural and Social Events on Mobile Call Data Records--An Estonian Case Study. Proceeding International Conference on Complex Networks and Their Applications, 415–426. https://doi.org/10.1007/978-3-030-36683-4_34
Jin, X., & Han, J. (2010). K-Means Clustering. In Encyclopedia of Machine Learning (pp. 563–564). https://doi.org/10.1007/978-0-387-30164-8_425
KMeans Silhouette Score Explained With Python Example. (2020). Retrieved from https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam
Leng, Y., Zhao, J., & Koutsopoulos, H. (2021). Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data. ACM Transactions on Management Information Systems, 12(3). https://doi.org/10.1145/3449042
Letouzé, E., & Vinck, P. (2015). The law, politics and ethics or cell phone data analytics. Retrieved from http://datapopalliance.org/wp-content/uploads/2015/04/WPS_LawPoliticsEthicsCellPhoneDataAnalytics.pdf
Louail, T., Lenormand, M., Cantu Ros, O. G., Picornell, M., Herranz, R., Frias-Martinez, E., Ramasco, J. J., & Barthelemy, M. (2014). From mobile phone data to the spatial structure of cities. Scientific Reports, 4, 5276. https:/doi.org/10.1038/srep05276
Mamei, M., Colonna, M., & Galassi, M. (2016). Automatic identification of relevant places from cellular network data. Pervasive and Mobile Computing, 31, 147–158. https://doi.org/10.1016/j.pmcj.2016.01.009
Mobile policy handbook: an insider’s guide to the issues. (2017). Retrieved from https://www.gsma.com/mena/wp-content/uploads/2018/10/Mobile_Policy_Handbook_2017_EN.pdf
Murphy, K. P. (2013). Machine learning: a probabilistic perspective. MIT Press.
Nair, S. C., Elayidom, M. S., & Gopalan, S. (2020). Call detail record-based traffic density analysis using global K-means clustering. International Journal of Intelligent Enterprise, 7(1/2/3), 176–187. https://dx.doi.org/10.1504/IJIE.2020.104654
Quercia, D., Di Lorenzo, G., Calabrese, F., & Ratti, C. (2011). Mobile Phones and Outdoor Advertising: Measurable Advertising. IEEE Pervasive Computing, 10(2), 28–36.
Scharff, C., Ndiaye, K., Jordan, M., Diene, A. N., & Drame, F. M. (2015). Human mobility during religious festivals and its implications on public health in Senegal: A mobile dataset analysis. Proceedings of 2015 IEEE Global Humanitarian Technology Conference (GHTC), 108–113.
Steenbruggen, J., Tranos, E., & Nijkamp, P. (2015). Data from mobile phone operators: A tool for smarter cities? Telecommunications Policy, 39(3), 335–346. https://doi.org/10.1016/j.telpol.2014.04.001
Sultan, K., Ali, H., Ahmad, A., & Zhang, Z. (2019). Call Details Record Analysis: A Spatio-temporal Exploration toward Mobile Traffic Classification and Optimization. Information, 10(6), 192. https://doi.org/10.3390/info10060192
Sumathi, V. P., Kousalya, K., Vanitha, V., & Cynthia, J. (2018). Crowd estimation at a social event using call data records. International Journal of Business Information Systems, 28(2), 246–261. https://doi.org/10.1504/IJBIS.2018.10012931
Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., & Peng, Z. (2018). Trajectory-Driven Influential Billboard Placement. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2748–2757. https://doi.org/10.1145/3219819.3219946
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Published
Jun 10, 2022
Article Details
How to Cite
Semassel, I. E., & Ben Yahia, S. (2022). Effective Optimization of Billboard Ads Based on CDR Data Leverage. Journal of Telecommunications and the Digital Economy, 10(2), 76–95. https://doi.org/10.18080/jtde.v10n2.527
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Special Issue: Digital Technologies and Innovation
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