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Item Preference Parameters from Grouped Ranking Observations

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Advances in Knowledge Discovery and Data Mining (PAKDD 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5476))

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Abstract

Given a set of rating data for a set of items, determining the values of items is a matter of importance. Various probability models have been proposed to solve this task. The Plackett-Luce model is one of such models, which parametrizes the value of each item by a real valued preference parameter. In this paper, the Plackett-Luce model is generalized to cope with the grouped ranking observations such as movies or restaurants ratings. Since the maximization of the likelihood of the proposed model is computationally intractable, the lower bound of the likelihood which is easy to evaluate is derived, and the em algorithm is adopted to the maximization of the lower bound.

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References

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Author information

Authors and Affiliations

  1. School of Science and Engineering, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo, 169-8555, Japan

    Hideitsu Hino, Yu Fujimoto & Noboru Murata

Authors
  1. Hideitsu Hino
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  2. Yu Fujimoto
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  3. Noboru Murata
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Editor information

Editors and Affiliations

  1. Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5 Tiwanont Road, 12000, Bangkadi, Muang, Pathumthani, Thailand

    Thanaruk Theeramunkong

  2. Dept. of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 10330, Bangkok, Thailand

    Boonserm Kijsirikul

  3. Faculty of Science & Engineering, York University, 355 Lumbers Building, 4700 Keele Street, M3J 1P3, Toronto, Ontario, Canada

    Nick Cercone

  4. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, 923-1292, Ishikawa, Japan

    Tu-Bao Ho

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© 2009 Springer-Verlag Berlin Heidelberg

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Hino, H., Fujimoto, Y., Murata, N. (2009). Item Preference Parameters from Grouped Ranking Observations. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_91

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  • DOI: https://doi.org/10.1007/978-3-642-01307-2_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01306-5

  • Online ISBN: 978-3-642-01307-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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