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Issue No. 10 - Oct. (2012 vol. 24)
ISSN: 1041-4347
pp: 1774-1788
Yu Peng , The Hong Kong University of Science and Technology, Hong Kong
Raymond Chi-Wing Wong , The Hong Kong University of Science and Technology, Hong Kong
Qian Wan , University of Wisconsin - Madison, Madison
The importance of dominance and skyline analysis has been well recognized in multicriteria decision-making applications. Most previous studies focus on how to help customers find a set of “best” possible products from a pool of given products. In this paper, we identify an interesting problem, finding top-k preferable products, which has not been studied before. Given a set of products in the existing market, we want to find a set of k “best” possible products such that these new products are not dominated by the products in the existing market. We study two problem instances of finding top-k preferable products. In the first problem instance, we need to set the prices of these products such that the total profit is maximized. We refer such products as top-k profitable products. In the second problem instance, we want to find k products such that these k products can attract the greatest number of customers. We refer these products as top-k products. In both problem instances, a straightforward solution is to enumerate all possible subsets of size k and find the subset which gives the greatest profit (for the first problem instance) or attracts the greatest number of customers (for the second problem instance). However, there are an exponential number of possible subsets. In this paper, we propose solutions to find the top-k profitable products and the top-k popular products efficiently. An extensive performance study using both synthetic and real data sets is reported to verify the effectiveness and efficiency of proposed algorithms.
Vectors, Greedy algorithms, Dynamic programming, Companies, Complexity theory, Correlation, Heuristic algorithms, spatial database., Skyline

R. C. Wong, Y. Peng and Q. Wan, "Finding Top-k Preferable Products," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1774-1788, 2012.
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