OpenTable data with multi-criteria ratings

TL;DR

This paper introduces the OpenTable dataset, a benchmark resource for multi-criteria recommender systems, enabling more personalized and informed restaurant recommendations by considering multiple user preferences.

Contribution

The paper releases a new dataset specifically designed for multi-criteria recommendation research, filling a gap in available benchmark data for this domain.

Findings

Provides a comprehensive dataset for multi-criteria recommendation research

Facilitates development of more personalized recommender systems

Supports evaluation of multi-criteria recommendation algorithms

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.