HotelRec：一个新的超大规模酒店推荐数据集
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
摘要：
今天，推荐系统是每个人日常数字生活中不可避免的一部分，并且存在于大多数互联网平台上。最先进的基于深度学习的模型需要大量数据才能实现最佳性能。许多满足这一标准的数据集已经被提出用于多个领域，例如亚马逊产品，餐馆或啤酒。然而，酒店领域的作品和数据集是有限的：最大的酒店评论数据集低于百万个样本。此外，酒店领域的数据稀疏性高于传统的推荐数据集，因此传统的协作过滤方法无法应用于此类数据。在本文中，我们提出了HotelRec，一个非常大规模的酒店推荐数据集，基于TripAdvisor，包含5000万条评论。据我们所知，HotelRec是酒店领域中最大的公开数据集（50M对0.9M），此外，它也是单个领域中最大的推荐数据集，并具有文本评论（50M对22M）。我们发布HotelRec以供进一步研究：https://github.com/Diego999/HotelRec。
英文摘要：
Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fulfilling this criterion have been proposed for multiple domains, such as Amazon products, restaurants, or beers. However, works and datasets in the hotel domain are limited: the largest hotel review dataset is below the million samples. Additionally, the hotel domain suffers from a higher data sparsity than traditional recommendation datasets and therefore, traditional collaborative-filtering approaches cannot be applied to such data. In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews. To the best of our knowledge, HotelRec is the largest publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). We release HotelRec for further research: https://github.com/Diego999/HotelRec.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
