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Efficient Identification of the Highest Diversity Gain Object

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Information Search, Integration and Personalization (ISIP 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 497))

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Abstract

Diversification has recently attracted a lot of attention, as a means to retrieve objects that are both relevant to a query and sufficiently dissimilar to each other. Since it is a computationally expensive problem, greedy techniques that iteratively identify the most promising objects are typically used. We focus on the sub-task within one iteration and formalize it as the highest diversity gain problem. We show that it is possible to optimally solve such problems, by appropriately defining a novelty function and identifying the object with the highest novelty. Furthermore, we are able to determine parts of the search space than cannot contain promising objects. Based on these results, we propose a greedy diversification algorithm that iteratively invokes a procedure to determine the most novel object. This procedure uses an index to guide the search towards promising objects, and computes bounds to prune large parts of the space. As a result, the procedure is shown to be I/O optimal, under certain conditions, and experimental studies on real and synthetic data demonstrate its efficiency.

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Author information

Authors and Affiliations

  1. Technische Universität Wien, Vienna, Austria

    Dimitris Sacharidis

  2. RMIT University, Melbourne, Australia

    Timos Sellis

Authors
  1. Dimitris Sacharidis
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  2. Timos Sellis
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Corresponding author

Correspondence to Dimitris Sacharidis .

Editor information

Editors and Affiliations

  1. Lab. ETIS, Sciences Informatiques, Université de Cergy-Pontoise, Pontoise, France

    Dimitrios Kotzinos

  2. HELP University, Kuala Lumpur, Malaysia

    Yeow Wei Choong

  3. LRI, University of Paris South, Orsay, France

    Nicolas Spyratos

  4. Information Science, Knowledge Media Lab, Hokkaido University, Sapporo, Hokkaido, Japan

    Yuzuru Tanaka

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Sacharidis, D., Sellis, T. (2016). Efficient Identification of the Highest Diversity Gain Object. In: Kotzinos, D., Choong, Y., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration and Personalization. ISIP 2014. Communications in Computer and Information Science, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-319-38901-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-38901-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38900-4

  • Online ISBN: 978-3-319-38901-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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