Towards Regret Free Slot Allocation in Billboard Advertisement
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911767373807616 |
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| author | Ali, Dildar Banerjee, Suman Prasad, Yamuna |
| author_facet | Ali, Dildar Banerjee, Suman Prasad, Yamuna |
| contents | Creating and maximizing influence among the customers is one of the central goals of an advertiser, and hence, remains an active area of research in recent times. In this advertisement technique, the advertisers approach an influence provider for a specific number of views of their content on a payment basis. Now, if the influence provider can provide the required number of views or more, he will receive the full, else a partial payment. In the context of an influence provider, it is a loss for him if he offers more or less views. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to minimize this quantity. In this paper, we solve this problem in the context of billboard advertisement and pose it as a discrete optimization problem. We propose four efficient solution approaches for this problem and analyze them to understand their time and space complexity. We implement all the solution methodologies with real-life datasets and compare the obtained results with the existing solution approaches from the literature. We observe that the proposed solutions lead to less regret while taking less computational time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_16464 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Regret Free Slot Allocation in Billboard Advertisement Ali, Dildar Banerjee, Suman Prasad, Yamuna Information Retrieval Databases Machine Learning Multiagent Systems Creating and maximizing influence among the customers is one of the central goals of an advertiser, and hence, remains an active area of research in recent times. In this advertisement technique, the advertisers approach an influence provider for a specific number of views of their content on a payment basis. Now, if the influence provider can provide the required number of views or more, he will receive the full, else a partial payment. In the context of an influence provider, it is a loss for him if he offers more or less views. This is formalized as 'Regret', and naturally, in the context of the influence provider, the goal will be to minimize this quantity. In this paper, we solve this problem in the context of billboard advertisement and pose it as a discrete optimization problem. We propose four efficient solution approaches for this problem and analyze them to understand their time and space complexity. We implement all the solution methodologies with real-life datasets and compare the obtained results with the existing solution approaches from the literature. We observe that the proposed solutions lead to less regret while taking less computational time. |
| title | Towards Regret Free Slot Allocation in Billboard Advertisement |
| topic | Information Retrieval Databases Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2401.16464 |