Large-scale personalized marketing modeling for effective use of transaction data
| Project/Area Number |
24683012
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| Research Category |
Grant-in-Aid for Young Scientists (A)
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| Allocation Type | Partial Multi-year Fund |
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| Research Field |
Commerce
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| Research Institution | Tohoku University |
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Principal Investigator |
ISHIGAKI TSUKASA 東北大学, 経済学研究科(研究院), 准教授 (20469597)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥10,270,000 (Direct Cost: ¥7,900,000、Indirect Cost: ¥2,370,000)
Fiscal Year 2014: ¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2013: ¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
Fiscal Year 2012: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | マーケティング / ビッグデータ / 階層ベイズモデル / 次元圧縮 / パーソナライゼーション / トピックモデル / ベイズモデル / マーケティングモデル / データベースマーケティング / サービス科学 / ベイズモデリング / 潜在クラスモデル |
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| Outline of Final Research Achievements |
This research project aims to realize a large-scale personalized marketing model. Large-scale transaction data recorded in supermarkets or convenience stores essentially are sparse with respect to consumers, items, and purchase times. We combine a dimensional reduction model with the hierarchical Bayes binary probit model for overcoming the sparseness of data. For computational feasibility, we employ variational Bayes inference that has computational efficiency compared to the resource-intensive Markov chain Monte Carlo inference in large-scale problem. The result shows that the model is applicable to datasets involving tens of thousands of consumers and hundreds of product items.
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Report
(4 results)
Research Products
(18 results)
