Determining the Impact Regions of Competing Options in Preference Space
Determining the Impact Regions of Competing Options in Preference Space
Instructors/Speakers Prof. Man Lung YIU The Hong Kong Polytechnic University Abstract 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 ...