User Preference Analysis for Most Frequent Peer/Dominator

Abstract

Given a set of objects $O$O (such as hotels), each can be represented as a point in a multi-dimensional feature space where each dimension corresponds to one attribute of the objects (such as price). Given the preference of a customer, the objects in $O$O not dominated by any other object (i.e., beat in all dimensions) are those worthy to be further considered. Such objects are known as skyline objects in database community. Suppose we have an object $o\\in O$o∈O. If $o$o is a skyline point, other skyline objects are called peers of $o$o. If $o$o is not a skyline object, it must be dominated by some skyline objects which are called dominators of $o$o. Given a large number of user preferences, an interesting problem is to identify the most frequent peer/dominator (MFP/MFD) of $o$o. The MFP/MFD search has unique values in competitor analysis of various information systems. However, it is a challenging task because of the complexity to process a large number of user preferences. In this work, we provide robust solutions including exact and approximate methods. While the exact solutions explore the dominance relationship in the feature space, the approximate solutions are based on sampling techniques with theoretical bounds. We did extensive tests on large data sets which are up to 100 million user preferences generated from commercial surveys. The test resutls demonstrate the exact algorithms outperform various baseline algorithms significantly, and the approximate algorithms make further improvement by one order of magnitude with 90-98 percent accuracy.