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Mitigating Position Bias in Review Search Results with Aspect Indicator for Loss Aversion

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Human Interface and the Management of Information: Applications in Complex Technological Environments (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13306))

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Abstract

Conventional review websites display a list of item search results with average rating scores (i.e., star ratings). We propose a method of designing snippets that encourage users to search items on review websites more carefully. The proposed snippets include aspect indicators that identify negative aspects if the item has a good star rating and vice versa. We expect the aspect indicators will help mitigate biases due to ranking position and star ratings by making users feel a “loss” if they do not carefully examine items. Our user study showed that the proposed method of including aspect indicators for loss aversion made participants spend more time searching a list of search results and checking items with worse star ratings, especially when searching hospitals. In contrast, showing aspect indicators that conformed to star ratings caused shortsighted review searches.

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Notes

  1. 1.

    Google Cloud Natural Language API: https://cloud.google.com/natural-language/docs/analyzing-entity-sentiment.

  2. 2.

    https://lancers.jp/.

  3. 3.

    We used the 1.5x interquartile range rule to identify outlier participants.

  4. 4.

    Hokkaido and Tokyo are popular travel destinations in Japan.

  5. 5.

    caloo.com for hospitals and jalan.net for hotels (both websites are in Japanese).

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Acknowledgements

This work was supported in part by Grants-in-Aid for Scientific Research (18H03244, 21H03554, 21H03775) from MEXT of Japan.

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Authors and Affiliations

  1. Shizuoka University, Hamamatsu, Japan

    Hiroki Ihoriya, Masaki Suzuki & Yusuke Yamamoto

Authors
  1. Hiroki Ihoriya
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  2. Masaki Suzuki
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  3. Yusuke Yamamoto
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Corresponding authors

Correspondence to Hiroki Ihoriya or Yusuke Yamamoto .

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Editors and Affiliations

  1. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  2. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

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Ihoriya, H., Suzuki, M., Yamamoto, Y. (2022). Mitigating Position Bias in Review Search Results with Aspect Indicator for Loss Aversion. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information: Applications in Complex Technological Environments. HCII 2022. Lecture Notes in Computer Science, vol 13306. Springer, Cham. https://doi.org/10.1007/978-3-031-06509-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-06509-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06508-8

  • Online ISBN: 978-3-031-06509-5

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