Datasets:

MinhDS
/

ViHoRec

Following the Datasheets for Datasets framework (Gebru et al., 2021). All statistics below are produced automatically by scripts/quality_control.py, scripts/anonymize.py, and scripts/make_benchmark_split.py.

1. Motivation

Purpose. There is no publicly documented Vietnamese hotel recommendation dataset. ViHoRec fills this gap for research on collaborative filtering, content-based, and hybrid recommendation, and on cold-start handling.
Created by. The authors (University of Information Technology, VNU-HCM).
Not for. Commercial use (see LICENSE) or re-identification of individuals.

2. Composition

Three released tables (release/):

File	Rows	Columns
`interactions.csv`	18,267	user_id, hotel_id, rating, date, source
`users.csv`	6,832	user_id, n_interactions
`hotels.csv`	560	hotel_id, name, location
content metadata (`data_content_based_raw.csv`)	309	11 attributes (facilities, surroundings, vicinity, price, distance, ...)

Instances. A row in interactions.csv is one user–hotel rating (0–10) with a timestamp and its originating site.
Sources. Booking.com (7,597), Traveloka (6,273), Ivivu (4,404).
Ratings span 1.0–10.0; dates span 2011-10-15 to 2023-12-09.
Sensitive data. Direct identifiers (reviewer display names) are removed before release; user ids are salted-HMAC pseudonyms (see §6).

3. Collection Process

How. Automated crawling with requests/BeautifulSoup and JSON review APIs where available; manual collection for content metadata (no public API).
Sampling. Sites and hotels selected by credibility and user volume; hotels concentrated in Vietnamese destinations (Đà Lạt, Đà Nẵng, Nha Trang, Vũng Tàu, Phú Quốc, Phan Thiết, ...).
Timeframe. Reviews were posted 2011–2023; crawling performed in 2023.

4. Preprocessing / Cleaning / Quality Control

Reproduced by scripts/quality_control.py. Reported measures:

Check	Result
Field completeness	0% missing after collection-time imputation (see limitation below)
Exact duplicate interactions	7 (0.038%) removed
Near-duplicates (reviewer + canonical hotel + date)	11 (0.060%)
Invalid / out-of-range ratings	0 (dirty token `8..5` repaired)
Unparsable dates	0
Raw hotel names → canonical hotels	581 → 560 (21 spelling variants merged, 3.6%)
Hotels appearing on ≥2 sites	78
Hotels with conflicting location	1 (flagged)

Entity resolution. Cross-site hotel matching uses an accent-free, stopword-stripped, order-independent canonical key (textnorm.py), replacing the original naïve LabelEncoder(NameHotel) exact-string matching.
Manual validation. annotation_agreement.py draws a stratified sample (default n≈250: interactions + hotels) for ≥2 annotators and reports percent agreement and Cohen's / Fleiss' κ, plus an estimated record-accuracy rate.

5. Uses

Recommended: benchmarking CF/CB/hybrid recommenders, cold-start studies, Vietnamese-language RecSys, low-resource / sparse-data research.
Known limitations.
- Small scale (18k interactions) vs. MovieLens-100k / Amazon; sparse (97.5% sparsity in the benchmark split).
- Reviewer display names were partially imputed/normalised at crawl time (missing names were replaced), so n_interactions per user and the number of distinct users are approximate — user identity is derived from a low-cardinality name string and may merge distinct individuals.
- Ratings are aggregate scores, not multi-criteria.

6. Ethics, Terms of Service & Legal

Terms of Service. Booking.com, Traveloka, and Ivivu restrict automated scraping and commercial reuse in their ToS. To stay within a defensible research-use position we: (a) collected only publicly visible review text and ratings, no private/account data; (b) do not redistribute raw HTML or full review text, only derived numeric ratings and hotel metadata; (c) release under CC BY-NC 4.0 (non-commercial); (d) provide takedown on request. Users of this dataset must comply with the source platforms' ToS.
Personal data / anonymisation. No emails, account ids, or full names are released. anonymize.py drops the display name entirely and assigns a salted HMAC-SHA256(secret_salt, name)[:12] pseudonym; the secret salt is kept off-repo (VIHOREC_SALT) and the name→id lookup (reports/_private_mapping.csv) is never published.
Risk. Re-identification risk is low: no free-text, no geolocation beyond city, and pseudonymous ids.

7. Distribution & Maintenance

Hosted with a versioned DOI (e.g., Zenodo); this repository is the canonical build pipeline. Report issues / request takedown to the corresponding author.