Abstract
Nowadays, billboard advertising has emerged as an effective advertising technique due to higher returns on investment. Given a set of selected slots and tags, how to effectively assign the tags to the slots remains an important question. In this paper, we study the problem of assigning tags to the slots such that the number of tags for which influence demand of each zone is satisfied gets maximized. Formally, we call this problem the Multi-Slot Tag Assignment Problem. The input to the problem is a geographical region partitioned into several zones, a set of selected tags and slots, a trajectory, a billboard database, and the influence demand for every tag for each zone. The task here is to find out the assignment of tags to the slots so that the number of satisfied tags is maximized. We show that the problem is NP-hard, and we propose a Cost Effective Greedy algorithm to solve this problem. An approximation guarantee and complexity analysis of the proposed methodology have been provided. The proposed methodology has been implemented with real-life datasets, and several experiments have been carried out to show the effectiveness and efficiency of the proposed approach. The obtained results have been compared with the baseline methods, and we observe that the proposed approach leads to a number of tags whose zonal influence demand is satisfied.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using pruned submodularity graph. In: International Conference on Advanced Data Mining and Applications, pp. 216–230. Springer (2022)
Ali, D., Banerjee, S., Prasad, Y.: Influential billboard slot selection using spatial clustering and pruned submodularity graph. arXiv preprint arXiv:2305.08949 (2023)
Ali, D., Banerjee, S., Prasad, Y.: Minimizing regret in billboard advertisement under zonal influence constraint. arXiv preprint arXiv:2402.01294 (2024)
Ali, D., Banerjee, S., Prasad, Y.: Regret minimization in billboard advertisement under zonal influence constraint. In: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, pp. 329–336 (2024)
Ali, D., Gupta, T., Banerjee, S., Prasad, Y.: Influential slot and tag selection in billboard advertisement. arXiv preprint arXiv:2401.10601 (2024)
Ali, D., Kumar, H., Banerjee, S., Prasad, Y.: An effective tag assignment approach for billboard advertisement. arXiv preprint arXiv:2409.02455 (2024)
Aslay, C., Bonchi, W.L.F., Goyal, A., Lakshmanan, L.V.: Viral marketing meets social advertising: ad allocation with minimum regret. Proc. VLDB Endow. 8(7) (2015)
Aslay, C., Lakshmanan, F.B.L.V., Lu, W.: Revenue maximization in incentivized social advertising. Proc. VLDB Endow. 10(11) (2017)
Aziz, H., Chan, H., Cseh, A., Li, B., Ramezani, F., Wang, C.: Multi-robot task allocation-complexity and approximation. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, p. 133141. AAMAS’21, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2021)
Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Trans. Ind. Inf. 16(2), 1058–1066 (2020)
Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. Syst. Man Cybern. Syst. 45(1), 129–142 (2015)
Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, p. 27482757. KDD ’18, Association for Computing Machinery, New York, NY, USA (2018)
Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Towards an optimal outdoor advertising placement: when a budget constraint meets moving trajectories. ACM Trans. Knowl. Discov. Data (TKDD) 14(5), 1–32 (2020)
Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising (2019). In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, Alaska, pp. 4–8 (2019)
Zhang, Y., Li, Y., Bao, Z., Zheng, B., Jagadish, H.: Minimizing the regret of an influence provider. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2115–2127 (2021)
Acknowledgments
This work is supported by the Start-Up Research Grant provided by the Indian Institute of Technology Jammu, India (Grant No.: SG100047).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ali, D., Banerjee, S., Prasad, Y. (2025). Multi-slot Tag Assignment Problem in Billboard Advertisement. In: Chen, T., Cao, Y., Nguyen, Q.V.H., Nguyen, T.T. (eds) Databases Theory and Applications. ADC 2024. Lecture Notes in Computer Science, vol 15449. Springer, Singapore. https://doi.org/10.1007/978-981-96-1242-0_12
Download citation
DOI: https://doi.org/10.1007/978-981-96-1242-0_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-1241-3
Online ISBN: 978-981-96-1242-0
eBook Packages: Computer ScienceComputer Science (R0)