越南语推荐研究缺数据？ViHoRec 提供了 18K 条跨平台高质量交互，还附带了冷启动基准，方便你对比模型在稀疏场景下的表现。
ViHoRec 数据集包含来自 Booking.com、Traveloka 和 Ivivu 三个平台的 18,267 条用户-酒店交互记录，涉及 6,832 名用户和 560 家酒店。数据集采用可复现的构建流程，包括跨平台实体解析和定量质量控制，并通过 HMAC 假名保护隐私。在时序留一法冷启动分割下，BPR-MF 的 Recall@10 从 0.120（长历史用户）降至 0.065（短历史用户），而 UserKNN 整体表现最优，凸显了该数据集的稀疏和冷启动特性。所有数据已在 GitHub 开源。
ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark
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.