Determining the impact regions of competing options in preference space

Bo Tang, Kyriakos Mouratidis, Man Lung Yiu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)


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 paper 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 (kSPR), 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.
Original languageEnglish
Title of host publicationSIGMOD 2017 - Proceedings of the 2017 ACM International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages16
VolumePart F127746
ISBN (Electronic)9781450341974
Publication statusPublished - 9 May 2017
Event2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017 - Hilton Chicago, Chicago, United States
Duration: 14 May 201719 May 2017


Conference2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Information Systems


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