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Opinionated Explanations for Recommendation Systems

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Research and Development in Intelligent Systems XXXII (SGAI 2015)

Abstract

This paper describes a novel approach for generating explanations for recommender systems based on opinions in user-generated reviews. We show how these opinions can be used to construct helpful and compelling explanations at recommendation time. The explanation highlights how the pros and cons of a recommended item compares to alternative items. We propose a way to score these explanations based on their content. The scores help to identify compelling explanations, providing a strong reason why the item being explained is better or worse than the alternatives. We describe the results of offline experiments and a live-user study based on TripAdvisor data to demonstrate the usefulness of this approach.

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Acknowledgments

This work is supported by Science Foundation Ireland through through the Insight Centre for Data Analytics under grant number SFI/12/RC/2289.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland

    Aonghus Lawlor, Khalil Muhammad, Rachael Rafter & Barry Smyth

Authors
  1. Aonghus Lawlor
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  2. Khalil Muhammad
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  3. Rachael Rafter
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  4. Barry Smyth
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Corresponding author

Correspondence to Aonghus Lawlor .

Editor information

Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, United Kingdom

    Miltos Petridis

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© 2015 Springer International Publishing Switzerland

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Lawlor, A., Muhammad, K., Rafter, R., Smyth, B. (2015). Opinionated Explanations for Recommendation Systems. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_25

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  • DOI: https://doi.org/10.1007/978-3-319-25032-8_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25030-4

  • Online ISBN: 978-3-319-25032-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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