Abstract
Abstract
Customers demand typical type of products with multiple features. We want to develop a business intelligence system which helps the company to set the blue ocean strategy by discovering k-most promising features (k-MPF) from the customers’ query and a set of existing products of the similar type. In this paper, we have formulated k-MPF to set the blue ocean strategy with compatible features. We have experimented with our proposed algorithms using different synthetic and real datasets, and the results showed the effectiveness of our proposed algorithms.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computational Mechanics
Reference21 articles.
1. Kotler P, Silva GD, Armstrong G, Haque E (2013) Principles of marketing: a south Asian perspective, 13th edn. Pearson, New Delhi
2. Sauter VL (2008) Competitive intelligence systems: handbook on decision support systems 2: variations. Springer, Berlin, pp 195–210. ISBN: 978-3-540-48716-6
3. Hohhof B (1994) Developing information systems for competitive intelligence support. Library Trends 43(2):226–238
4. Islam MS, Liu C (2016) Know your customer: computing k-most promising products for targeted marketing. VLDB J 25(4):545–570
5. Kim WC, Mauborgne R (2005) Blue ocean strategy: how to create uncontested market space and make the competition irrelevant. Harvard Business School Press, Boston ISBN: 978-1591396192
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献