AI vs. Wisdom of the Crowds in Selecting Cover Images for Restaurant Reviews

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See all articles by Warut Khern-am-nuai

Warut Khern-am-nuai

McGill University - Desautels Faculty of Management

Hyunji So

College of Business, Korea Advanced Institute of Science and Technology (KAIST)

Yossiri Adulyasak

HEC Montréal

Date Written: October 31, 2020

Abstract

Restaurant review platforms such as Yelp and TripAdvisor have received an increasing amount of photos in the review submissions. These photos provide additional values to the reviews as previous literature has shown that platform users generally perceive them as being useful. As such, the choice of the "cover images" (i.e., the representative images of each restaurant) can potentially influence the level of user engagement in the platform. However, the selection of such images is time consuming and often requires human intervention. Meanwhile, it is particularly challenging to develop a quantitative approach to systematically assess the images on their effectiveness in increasing user engagement. In this study, we collaborate with a large-scale review platform in Asia to investigate this issue. We discuss two different image selection designs, namely the crowd-based and AI-based systems. The AI-based system, which is used to learn complex latent image features, is further enhanced through the feature representation transfer process to overcome lack of high-quality labeled data. In addition to the holdout evaluation method, we conduct a randomized field experiment to objectively evaluate the effectiveness of both designs and show that the AI-based system can outperform the crowd in selecting images that stimulate user interactions. Post-hoc analyses using observational data are conducted to identify underlying mechanisms that drive the superior performance of the AI-based system.

Suggested Citation

Khern-am-nuai, Warut and So, Hyunji and Adulyasak, Yossiri, AI vs. Wisdom of the Crowds in Selecting Cover Images for Restaurant Reviews (October 31, 2020). Available at SSRN: https://ssrn.com/abstract=

Warut Khern-am-nuai (Contact Author)

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Hyunji So

College of Business, Korea Advanced Institute of Science and Technology (KAIST) ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 02455
Korea, Republic of (South Korea)

Yossiri Adulyasak

HEC Montréal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada

HOME PAGE: http://yossiri.info/

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