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Personalized recommendation driven by information flow
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AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Market research has shown that consumers exhibit a variety of different purchasing behaviors; specifically, some tend to purchase products earlier than other consumers. Identifying such early buyers can help personalize marketing strategies, potentially improving their effectiveness. In this paper, we present a non-parametric approach to the problem of identifying early buyers from purchase data. Our formulation takes as inputs the detailed purchase information of each consumer, with which we construct a weighted directed graph whose nodes correspond to consumers and whose edges correspond to purchases consumers have in common; the edge weights indicate how frequently consumers purchase products earlier than other consumers.Identifying early buyers corresponds to the problem of finding a subset of nodes in the graph with maximum difference between the weights of the outgoing and incoming edges. This problem is a variation of the maximum cut problem in a directed graph. We provide an approximation algorithm based on semidefinite programming (SDP) relaxations pioneered by Goemans and Williamson, and analyze its performance. We apply the algorithm to real purchase data from Amazon.com, providing new insights into consumer behaviors.