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OpenTable data with multi-criteria ratings
- Citation Author(s):
-
YongZheng
Illinois Institute of Technology - Submitted by:
- Yong Zheng
- Last updated:
- Wed, 10/30/2024 - 10:25
- DOI:
- 10.21227/8avw-yk62
- Data Format:
- Research Article Link:
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Abstract
With the development of recommender systems (RSs), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. Multi-criteria recommender systems (MCRSs) are designed to provide personalized recommendations by considering user preferences in multiple attributes or criteria simultaneously. Unlike traditional RSs that typically focus on a single rating, these systems help users make more informed decisions by considering their diverse preferences and needs across various dimensions. In this article, we release the OpenTable data set which was crawled from OpenTable.com. The data set can be considered as a benchmark data set for multi-criteria recommendations.
We utilized Web crawling to acquire restaurant ratings from OpenTable.com, where both the overall rating and multi-criteria ratings are included. The major challenge in the process of data collection is identifying users. There are several anonymous ratings given by users. Namely, we are not able to identify a specific user or UserID from the Webpages. As a result, it is difficult to acquire dense ratings.
The OpenTable data set has been released on Kaggle.com. There are 19,536 ratings given by 1,309 users on 91 restaurants. In addition to the overall ratings, we have users' ratings on the restaurants from 4 criteria, including food quality, satisfaction of service and ambience, and the overall value of the picks. The ratings were given in the scale of 1 to 5.
