Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)

Dildar Ali, Ankit Kumar Bhagat, Suman Banerjee, Yamuna Prasad

AAAI 2023 pp. 16148-16149

doi:10.1609/AAAI.V37I13.26934 /aaai/2023/ali2023aaai-efficient/

Abstract

Now-a-days, billboard advertisement has emerged as an effective outdoor advertisement technique. In this case, a commercial house approaches an influence provider for a specific number of views of their advertisement content on a payment basis. If the influence provider can satisfy this then they will receive the full payment else a partial payment. If the influence provider provides more or less than the demand then certainly this is a loss to them. This is formalized as ‘Regret’ and the goal of the influence provider will be to minimize the ‘Regret’. In this paper, we propose simple and efficient solution methodologies to solve this problem. Efficiency and effectiveness have been demonstrated by experimentation.

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Text

Ali et al. "Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26934

Markdown

[Ali et al. "Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/ali2023aaai-efficient/) doi:10.1609/AAAI.V37I13.26934

BibTeX

@inproceedings{ali2023aaai-efficient,
  title     = {{Efficient Algorithms for Regret Minimization in Billboard Advertisement (Student Abstract)}},
  author    = {Ali, Dildar and Bhagat, Ankit Kumar and Banerjee, Suman and Prasad, Yamuna},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16148-16149},
  doi       = {10.1609/AAAI.V37I13.26934},
  url       = {https://mlanthology.org/aaai/2023/ali2023aaai-efficient/}
}