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Automated Interestingness Measure Selection for Exhibition Recommender Systems

  • Kok Keong Bong
  • Matthias Joest
  • Christoph Quix
  • Toni Anwar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8397)

Abstract

Exhibition guide system contain various information pertaining to exhibitors, products and events that are happening during the exhibitions. The system would be more useful if it is augmented with a recommender system. Our recommender system would recommend users a list of interesting exhibitors based on associations that mined from the web server logs. The recommendations are ranked based on various Objective Interestingness Measures (OIMs) that quantify the interestingness of an association. Due to data sparsity, some OIMs cannot provide distinct values for different rules and hamper the ranking process. In mobile applications, the ranking of recommendations is crucial because of the low real estate in mobile device screen sizes. We show that our system is able to select an OIM (from 50 OIMs) that would perform better than the regular Support-Confidence OIM. Our system is tested using data from exhibitions held in Germany.

Keywords

Association Rule Mining Objective Interestingness Measures Clustering 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kok Keong Bong
    • 1
    • 2
  • Matthias Joest
    • 2
  • Christoph Quix
    • 3
    • 4
  • Toni Anwar
    • 1
    • 5
  1. 1.The Sirindhorn International Thai German Graduate School of EngineeringKing Mongkut’s University of Technology North BangkokBangkokThailand
  2. 2.Heidelberg Mobil International GmbHWalldorfGermany
  3. 3.Information SystemsRWTH Aachen UniversityGermany
  4. 4.Fraunhofer FITSt. AugustinGermany
  5. 5.Faculty of ComputingUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia

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