User preference mining techniques for personalized applications

Abstract

Executive SummaryPreference mining is suitable for identifying customer preferences from previous transactions. Using these preferences the functionality of personalized applications can be tailored to the needs of the individual customer, thereby improving the satisfaction and the loyalty of the customers. & Preference mining encompasses techniques that identify customer preferences from log data. & The implementation of these techniques is very efficient even for large log relations. & The resulting preferences are stored in an XML format and can therefore be used in different application contexts, e.g. for the development of customer oriented consulting systems in e-commerce, for the improvement of search technologies or for personalized marketing.

The Poset Cover Problem

“…Finite partial orders or posets have numerous applications, including scheduling [1][2][3][4][5][6][7][8], molecular evolution [9][10][11][12], data mining [13][14][15][16][17], graph theory [18][19][20][21][22][23], and algebra [24][25][26][27]. Many applications implicitly or explicitly involve linear extensions of posets.…”