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.
Google Cloud Natural Language API: https://cloud.google.com/natural-language/docs/analyzing-entity-sentiment.
- 2.
- 3.
We used the 1.5x interquartile range rule to identify outlier participants.
- 4.
Hokkaido and Tokyo are popular travel destinations in Japan.
- 5.
caloo.com for hospitals and jalan.net for hotels (both websites are in Japanese).
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This work was supported in part by Grants-in-Aid for Scientific Research (18H03244, 21H03554, 21H03775) from MEXT of Japan.
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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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