Consumer Preference Analysis and Rating Prediction Model in the Restaurant Industry Based on Restaurant Information and Consumer Reviews

DOI

10.2991/978-94-6463-246-0_56How to use a DOI?

Keywords

Restaurant; Consumer rating; Machine learning; Random Forest

Abstract

The restaurant industry has increasingly relied on the development of the Internet and mobile apps in recent years. Diner tends to make decisions based on restaurant information and customer reviews on apps, while merchant also focuses on customer reviews to improve the quality of service and attract more customers. It is noteworthy these customer reviews and ratings show that consumers pay more attention to restaurant attributes such as environment, food variety, parking condition and the need for reservation. These obvious tendencies can either serve as positive factors for restaurants, or directly result in consumer dissatisfaction and negative reviews. However, many review apps limit consumer to rating on a scale of 1–5 with a difference of 0.5, which not only restricts customers’ ratings, but also affects the rating’s authenticity. Therefore, this paper will firstly explore the influence of different factors on consumer ratings by using regression analysis to summarize consumer’s preference and selection tendency. Secondly, it will compare the prediction results of consumer ratings by using regression model, decision tree model and random forest model. The result shows that random forest model can effectively predict consumer ratings, reduce rating errors while keeping MSE and R2 within a reasonable range, and reflect consumer’s real attitude towards restaurants with greater accuracy.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Li Qing
PY  - 2023
DA  - 2023/09/26
TI  - Consumer Preference Analysis and Rating Prediction Model in the Restaurant Industry Based on Restaurant Information and Consumer Reviews
BT  - Proceedings of the 3rd International Conference on Economic Development and Business Culture (ICEDBC 2023)
PB  - Atlantis Press
SP  - 464
EP  - 471
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-246-0_56
DO  - 10.2991/978-94-6463-246-0_56
ID  - Qing2023
ER  -