Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916947178815488 |
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| author | Gangopadhyay, Briti Wang, Zhao Takamatsu, Shingo |
| author_facet | Gangopadhyay, Briti Wang, Zhao Takamatsu, Shingo |
| contents | Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is used to improve comprehension of textual information and enhance fusion of modalities. Experiments on a large-scale industry dataset show that our method outperforms baselines in both accuracy and reasoning quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09683 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs Gangopadhyay, Briti Wang, Zhao Takamatsu, Shingo Information Retrieval Artificial Intelligence Forecasting click volume is a key task in digital advertising, influencing both revenue and campaign strategy. Traditional time series models rely solely on numerical data, often overlooking rich contextual information embedded in textual elements, such as keyword updates. We present a multimodal forecasting framework that combines click data with textual logs from real-world ad campaigns and generates human-interpretable explanations alongside numeric predictions. Reinforcement learning is used to improve comprehension of textual information and enhance fusion of modalities. Experiments on a large-scale industry dataset show that our method outperforms baselines in both accuracy and reasoning quality. |
| title | Forecasting Clicks in Digital Advertising: Multimodal Inputs and Interpretable Outputs |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09683 |