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International Conference on Case-Based Reasoning

ICCBR 2016: Case-Based Reasoning Research and Development pp 93–107Cite as

Personalized Opinion-Based Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9969))

Abstract

E-commerce recommender systems seek out matches between customers and items in order to help customers discover more relevant and satisfying products and to increase the conversion rate of browsers to buyers. To do this, a recommender system must learn about the likes and dislikes of customers/users as well as the advantages and disadvantages (pros and cons) of products. Recently, the explosion of user-generated content, especially customer reviews, and other forms of opinionated expression, has provided a new source of user and product insights. The interests of a user can be mined from the reviews that they write and the pros and cons of products can be mined from the reviews written about them. In this paper, we build on recent work in this area to generate user and product profiles from user-generated reviews. We further describe how this information can be used in various recommendation tasks to suggest high-quality and relevant items to users based on either an explicit query or their profile. We evaluate these ideas using a large dataset of TripAdvisor reviews. The results show the benefits of combining sentiment and similarity in both query-based and user-based recommendation scenarios, and also disclose the effect of the number of reviews written by a user on recommendation performance.

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Notes

  1. 1.

    http://glaros.dtc.umn.edu/gkhome/views/cluto.

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Acknowledgments

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

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Authors and Affiliations

  1. School of Computer Science and Informatics, Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Ruihai Dong & Barry Smyth

Authors
  1. Ruihai Dong
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  2. Barry Smyth
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Corresponding author

Correspondence to Ruihai Dong .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Ashok Goel

  2. Universidad Complutense de Madrid , Madrid, Spain

    M Belén Díaz-Agudo

  3. University of West London , London, United Kingdom

    Thomas Roth-Berghofer

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Dong, R., Smyth, B. (2016). Personalized Opinion-Based Recommendation. In: Goel, A., Díaz-Agudo, M., Roth-Berghofer, T. (eds) Case-Based Reasoning Research and Development. ICCBR 2016. Lecture Notes in Computer Science(), vol 9969. Springer, Cham. https://doi.org/10.1007/978-3-319-47096-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-47096-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47095-5

  • Online ISBN: 978-3-319-47096-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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