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利潤最⼤化を伴う機械学習による多様性・公平性考慮型市場セグメンテーション
https://kyoritsu.repo.nii.ac.jp/records/2001061
https://kyoritsu.repo.nii.ac.jp/records/200106195605d55-4a90-4431-84cd-d24bd5625124
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
Kyoritsu business＆economics review5_10Kinjo.pdf (628 KB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-10-27 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | 利潤最⼤化を伴う機械学習による多様性・公平性考慮型市場セグメンテーション | |||||||||||||
| 言語 | ja | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Diversity - and fairness-aware market segmentation using machine learning with profit maximization | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | jpn | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | diversity | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | fairness | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | fairness-aware machine learning | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | clustering | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | profit maximization | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | departmental bulletin paper | |||||||||||||
| 著者 |
金城, 敬太
× 金城, 敬太
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| 内容記述 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | In recent years, considering diversity—such as gender and race—has become an essential issue in society. In various aspects of business management, including marketing, neglecting such considerations may necessitate a revision of strategies and can even lead to a decline in corporate brand image. However, it remains unclear how segmentation and targeting can be conducted while ensuring diversity. Meanwhile, in the field of machine learning, fairness-aware approaches have long been proposed, developed, and studied. This research addresses the above-mentioned challenges by applying fairness-aware machine learning to segmentation, thereby ensuring diversity, evaluating it, and ultimately optimizing marketing strategies. To validate this approach, we applied it to data and confirmed its effectiveness. We expect that this study will provide a novel perspective on incorporating diversity considerations into marketing practices. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Kyoritsu business & economics review 巻 5, p. 1-17, 発行日 2025-10 |
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