In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector.
The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes.
In this work, we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option focal record is among the top-k score-wise. In effect, these regions capture all possible user profiles for which the focal record is highly preferable, and are therefore essential in market impact analysis, potential customer identification, profile-based marketing, targeted advertising, etc. We refer to our problem as k-Shortlist Preference Region identification, and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem.
Man Lung Yiu received the bachelor’s degree in computer engineering and the PhD degree in computer science from the University of Hong Kong in 2002 and 2006, respectively. Prior to his current post, he worked at Aalborg University for three years starting in the Fall of 2006. He is now an associate professor in the Department of Computing, Hong Kong Polytechnic University. His research focuses on the management of complex data, in particular query processing topics on spatiotemporal data and multidimensional data.