Domain driven data mining for unavailability estimation of electrical power grids
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
A hybrid approach for personalized recommendation of news on the Web
Expert Systems with Applications: An International Journal
Using context to improve the effectiveness of segmentation and targeting in e-commerce
Expert Systems with Applications: An International Journal
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system
Proceedings of the sixth ACM conference on Recommender systems
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On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping based approach to computing customer segments that groups customers in terms of optimally combining transactional data of several customers to build a predictive model of customer behavior for each group. We consider customer segmentation as a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups and show that finding an optimal customer partition is NP-hard. We propose several suboptimal direct grouping segmentation methods, empirically compares them against traditional statistics-based hierarchical and affinity propagation based segmentation, and 1-to-1 methods across multiple experimental conditions. We show that the best direct grouping method builds mostly small sized customer segments and significantly dominates the statistics-based and 1-to-1 approaches across most of the experimental conditions, while still being computationally tractable.