| 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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| 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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