OpenTable data with multi-criteria ratings

2024-11-22

arXiv.org

AI Summary

This work addresses two key challenges in multi-criteria recommendation systems (MCRS): the difficulty of modeling users’ multidimensional preferences and the scarcity of high-quality, real-world benchmark datasets. To this end, we introduce OpenTable-MCR—the first fine-grained, multi-criteria rating dataset designed for authentic restaurant scenarios. It explicitly captures criteria such as food quality, service, and ambiance, comprising tens of thousands of structured, rigorously cleaned and validated user ratings. We further establish a standardized MCRS benchmarking framework and publicly release both the dataset and its full preprocessing pipeline. Unlike existing resources, OpenTable-MCR is the first large-scale, multi-attribute, explicitly annotated, real-world dataset offering fine-grained criterion-level ratings. It fills a critical gap in the MCRS literature by providing a high-fidelity, reproducible, and comparable public benchmark. The dataset has already been adopted for evaluation and training by multiple state-of-the-art MCRS models.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Develop multi-criteria recommender systems for personalized recommendations
• Address diverse user preferences across multiple attributes or criteria
• Provide benchmark data set from OpenTable for multi-criteria recommendations

Innovation

• Multi-criteria ratings for personalized recommendations
• OpenTable dataset as benchmark for MCRS
• Context-aware and group-based recommender systems