ViHoRec：一个质量可控的越南酒店推荐数据集及冷启动基准测试

中文摘要 AI 辅助

越南推荐系统研究因缺乏公开且记录良好的酒店交互资源而受限。构建这样的资源具有挑战性，原因包括跨平台酒店名称需协调、质量要用可重复度量审核、公开发布要保护隐私并在现实冷启动条件下可作为基准测试。我们引入ViHoRec，这是一个质量可控的越南酒店推荐数据集，包含6832个用户与560家酒店的18267次交互。贡献有：可重复构建管道、隐私保护发布、公共冷启动基准测试。在公共划分上，短历史用户的学习模型性能大幅下降，UserKNN总体最强，确立了ViHoRec作为低资源推荐的稀疏、冷启动主导测试平台。所有数据可公开获取。

英文摘要

Recommender-system research for Vietnamese remains limited by the absence of a public, well-documented hotel interaction resource. Building such a resource is challenging for three reasons: cross-platform hotel names must be reconciled before interactions are comparable; quality must be audited with reproducible metrics rather than ad hoc cleaning; and public release must preserve privacy while remaining benchmarkable under realistic cold-start conditions. We introduce ViHoRec, a quality-controlled Vietnamese hotel recommendation dataset of 18{,}267 interactions between 6{,}832 users and 560 hotels, crawled from Booking.com, Traveloka, and Ivivu. Our contributions are: (i) a reproducible construction pipeline with cross-platform entity resolution and quantitative quality control; (ii) a privacy-preserving release with HMAC pseudonyms; and (iii) a public cold-start benchmark with temporal leave-last-one-out split, data-centric ablations, and dependency-free baselines. On the public split, learned models degrade sharply for users with short histories (BPR-MF Recall@10: 0.065 vs. 0.120), while UserKNN remains strongest overall, establishing ViHoRec as a sparse, cold-start-dominated testbed for low-resource recommendation. All data are publicly available at https://github.com/MinhNguyenDS/ViHoRec.

发表机构

• Faculty of Information Technology, University of Science, Ho Chi Minh City（信息科技学院，科学大学，胡志明市）
• Vietnam National University, Ho Chi Minh City（越南国家大学，胡志明市）

机构由 AI 辅助整理，请以论文原文为准。