Standing Out in a Crowd: Selecting Attributes for Maximum Visibility

  • Authors:

  • Muhammed Miah;Gautam Das;Vagelis Hristidis;Heikki Mannila

  • Affiliations:

  • Department of Computer Science and Engineering, University of Texas at Arlington, 416 Yates Street, Arlington, TX 76019, USA. mzmiah@uta.edu;Department of Computer Science and Engineering, University of Texas at Arlington, 416 Yates Street, Arlington, TX 76019, USA. gdas@uta.edu;School of Computing and Information Sciences, Florida International University, 11200 S.W. 8th Street, Miami, FL 33199, USA. vagelis@cis.fiu.edu;HIIT, Helsinki University of Technology and University of Helsinki, P.O Box 68, FI 00014, University of Helsinki, Helsinki, Finland. Heikki.Mannila@tkk.fi

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

In recent years, there has been significant interest in development of ranking functions and efficient top-k retrieval algorithms to help users in ad-hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). In this paper we focus on a novel and complementary problem: how to guide a seller in selecting the best attributes of a new tuple (e.g., new product) to highlight such that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several interesting formulations of this problem. Although these problems are NP-complete, we can give several exact algorithms as well as approximation heuristics that work well in practice. Our exact algorithms are based on Integer Programming (IP) formulations of the problems, as well as on adaptations of maximal frequent itemset mining algorithms, while our approximation algorithms are based on greedy heuristics. We conduct a performance study illustrating the benefits of our methods on real as well as synthetic data.