发布OpenTable多维度评分数据集，助力推荐系统研究
OpenTable data with multi-criteria ratings
- 构建包含多属性评分的开放数据集
- 支持多维度偏好建模与个性化推荐
- 适合研究多标准推荐系统的学者使用
随着推荐系统的发展，出现了上下文感知推荐系统、多标准推荐系统和群体推荐系统等有前景的系统。多标准推荐系统（MCRS）通过同时考虑用户在多个属性或标准上的偏好，提供个性化推荐。与传统仅关注单一评分的推荐系统不同，这类系统能帮助用户基于多样化需求做出更明智决策。本文发布了从OpenTable.com爬取的OpenTable数据集，该数据集可作为多标准推荐研究的基准数据集。
原文摘要 · Abstract (English)
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.
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