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  • Know your customer: computing k-most promising products for targeted marketing

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    Saiful IslamPUB4449.pdf (1.636Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Islam, Md Saiful
    Liu, Chengfei
    Griffith University Author(s)
    Islam, Saiful
    Year published
    2016
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    Abstract
    The advancement of World Wide Web has revolutionized the way the manufacturers can do business. The manufacturers can collect customer preferences for products and product features from their sales and other product-related Web sites to enter and sustain in the global market. For example, the manufactures can make intelligent use of these customer preference data to decide on which products should be selected for targeted marketing. However, the selected products must attract as many customers as possible to increase the possibility of selling more than their respective competitors. This paper addresses this kind of product ...
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    The advancement of World Wide Web has revolutionized the way the manufacturers can do business. The manufacturers can collect customer preferences for products and product features from their sales and other product-related Web sites to enter and sustain in the global market. For example, the manufactures can make intelligent use of these customer preference data to decide on which products should be selected for targeted marketing. However, the selected products must attract as many customers as possible to increase the possibility of selling more than their respective competitors. This paper addresses this kind of product selection problem. That is, given a database of existing products P from the competitors, a set of company’s own products Q, a dataset C of customer preferences and a positive integer k, we want to find k-most promising products (k-MPP) from Q with maximum expected number of total customers for targeted marketing. We model k-MPP query and propose an algorithmic framework for processing such query and its variants. Our framework utilizes grid-based data partitioning scheme and parallel computing techniques to realize k-MPP query. The effectiveness and efficiency of the framework are demonstrated by conducting extensive experiments with real and synthetic datasets.
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    Journal Title
    VLDB Journal
    Volume
    25
    Issue
    4
    DOI
    https://doi.org/10.1007/s00778-016-0428-3
    Copyright Statement
    © 2016 Springer-Verlag Berlin Heidelberg. This is an electronic version of an article published in The VLDB Journal, Volume 25, Issue 4, pp 545–570, 2016. The VLDB Journal is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Data management and data science
    Data structures and algorithms
    Distributed computing and systems software
    Information systems
    Publication URI
    http://hdl.handle.net/10072/368865
    Collection
    • Journal articles

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