| # Datasheet for the ViHoRec Dataset
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| Following the *Datasheets for Datasets* framework (Gebru et al., 2021). All
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| statistics below are produced automatically by `scripts/quality_control.py`,
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| `scripts/anonymize.py`, and `scripts/make_benchmark_split.py`.
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| ## 1. Motivation
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| - **Purpose.** There is no publicly documented Vietnamese hotel recommendation
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| dataset. ViHoRec fills this gap for research on collaborative filtering,
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| content-based, and hybrid recommendation, and on cold-start handling.
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| - **Created by.** The authors (University of Information Technology, VNU-HCM).
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| - **Not for.** Commercial use (see LICENSE) or re-identification of individuals.
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| ## 2. Composition
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| Three released tables (`release/`):
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| | File | Rows | Columns |
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| |---|---|---|
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| | `interactions.csv` | 18,267 | user_id, hotel_id, rating, date, source |
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| | `users.csv` | 6,832 | user_id, n_interactions |
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| | `hotels.csv` | 560 | hotel_id, name, location |
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| | content metadata (`data_content_based_raw.csv`) | 309 | 11 attributes (facilities, surroundings, vicinity, price, distance, ...) |
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| - **Instances.** A row in `interactions.csv` is one user–hotel rating (0–10)
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| with a timestamp and its originating site.
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| - **Sources.** Booking.com (7,597), Traveloka (6,273), Ivivu (4,404).
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| - **Ratings** span 1.0–10.0; **dates** span 2011-10-15 to 2023-12-09.
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| - **Sensitive data.** Direct identifiers (reviewer display names) are **removed**
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| before release; user ids are salted-HMAC pseudonyms (see §6).
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| ## 3. Collection Process
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| - **How.** Automated crawling with `requests`/BeautifulSoup and JSON review
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| APIs where available; manual collection for content metadata (no public API).
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| - **Sampling.** Sites and hotels selected by credibility and user volume; hotels
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| concentrated in Vietnamese destinations (Đà Lạt, Đà Nẵng, Nha Trang, Vũng Tàu,
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| Phú Quốc, Phan Thiết, ...).
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| - **Timeframe.** Reviews were posted 2011–2023; crawling performed in 2023.
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| ## 4. Preprocessing / Cleaning / Quality Control
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| Reproduced by `scripts/quality_control.py`. Reported measures:
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| | Check | Result |
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| |---|---|
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| | Field completeness | 0% missing after collection-time imputation (see limitation below) |
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| | Exact duplicate interactions | 7 (0.038%) removed |
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| | Near-duplicates (reviewer + canonical hotel + date) | 11 (0.060%) |
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| | Invalid / out-of-range ratings | 0 (dirty token `8..5` repaired) |
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| | Unparsable dates | 0 |
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| | Raw hotel names → canonical hotels | 581 → 560 (21 spelling variants merged, 3.6%) |
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| | Hotels appearing on ≥2 sites | 78 |
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| | Hotels with conflicting location | 1 (flagged) |
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| - **Entity resolution.** Cross-site hotel matching uses an accent-free,
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| stopword-stripped, order-independent canonical key (`textnorm.py`), replacing
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| the original naïve `LabelEncoder(NameHotel)` exact-string matching.
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| - **Manual validation.** `annotation_agreement.py` draws a stratified sample
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| (default n≈250: interactions + hotels) for ≥2 annotators and reports percent
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| agreement and Cohen's / Fleiss' κ, plus an estimated record-accuracy rate.
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| ## 5. Uses
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| - Recommended: benchmarking CF/CB/hybrid recommenders, cold-start studies,
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| Vietnamese-language RecSys, low-resource / sparse-data research.
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| - **Known limitations.**
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| - Small scale (18k interactions) vs. MovieLens-100k / Amazon; sparse
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| (97.5% sparsity in the benchmark split).
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| - Reviewer display names were partially imputed/normalised at crawl time
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| (missing names were replaced), so `n_interactions` per user and the
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| number of distinct users are approximate — user identity is derived from
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| a low-cardinality name string and may merge distinct individuals.
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| - Ratings are aggregate scores, not multi-criteria.
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| ## 6. Ethics, Terms of Service & Legal
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| - **Terms of Service.** Booking.com, Traveloka, and Ivivu restrict automated
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| scraping and commercial reuse in their ToS. To stay within a defensible
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| research-use position we: (a) collected only publicly visible review text and
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| ratings, no private/account data; (b) do **not** redistribute raw HTML or
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| full review text, only derived numeric ratings and hotel metadata;
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| (c) release under **CC BY-NC 4.0** (non-commercial); (d) provide takedown on
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| request. Users of this dataset must comply with the source platforms' ToS.
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| - **Personal data / anonymisation.** No emails, account ids, or full names are
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| released. `anonymize.py` drops the display name entirely and assigns a
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| salted `HMAC-SHA256(secret_salt, name)[:12]` pseudonym; the secret salt is
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| kept off-repo (`VIHOREC_SALT`) and the name→id lookup
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| (`reports/_private_mapping.csv`) is **never** published.
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| - **Risk.** Re-identification risk is low: no free-text, no geolocation beyond
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| city, and pseudonymous ids.
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| ## 7. Distribution & Maintenance
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| - Hosted with a versioned DOI (e.g., Zenodo); this repository is the canonical
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| build pipeline. Report issues / request takedown to the corresponding author.
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