Papers by Shingo Takamatsu
OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent (2025.emnlp-main)
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| Challenge: | Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. |
| Approach: | They propose a keyword generation framework that is On-the-fly and Multi-objective to automate keyword generation. |
| Outcome: | Experiments show that OMS outperforms existing methods in keyword generation . relying on large-scale query-keyword data is a major limitation, authors say . |
Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents (2025.emnlp-industry)
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| Challenge: | Recent advances in generative modeling have greatly improved image synthesis quality. |
| Approach: | They propose an agentic refinement framework for automatic ad banner generation that integrates a hierarchical multimodal agent system with a coordination loop. |
| Outcome: | The proposed model outperforms existing models in real-world banner design scenarios. |
BannerAgency: Advertising Banner Design with Multimodal LLM Agents (2025.emnlp-main)
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| Challenge: | Advertising banners are an instrumental medium in digital marketing campaigns. |
| Approach: | They propose a training-free framework for fully automated banner ad design creation that enables frontier multimodal large language models to streamline the production of effective banners with minimal manual effort. |
| Outcome: | The proposed framework is based on a training-free model that can be used to create fully automated banner ad design creations with minimal manual effort across diverse marketing contexts. |
OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising (2025.coling-industry)
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| Challenge: | Conventionally, keyword decision-making in sponsored search advertising relies on deep generation-based methods. |
| Approach: | They propose an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time. |
| Outcome: | The proposed method shows significant improvements across various metrics and emphasizes the importance of each component. |