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DATASHEET.md

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+# Datasheet for the ViHoRec Dataset
+
+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.

LICENSE

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+ViHoRec Dataset License
+=======================
+
+The ViHoRec dataset (the anonymised files under `dataset_release/release/`:
+interactions.csv, users.csv, hotels.csv, and the benchmark split) is released
+under the
+
+    Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
+
+license. Full legal text: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+You are free to:
+  * Share  - copy and redistribute the material in any medium or format.
+  * Adapt  - remix, transform, and build upon the material.
+
+Under the following terms:
+  * Attribution - You must give appropriate credit by citing the dataset paper
+    (see CITATION below), provide a link to this license, and indicate if
+    changes were made.
+  * NonCommercial - You may not use the material for commercial purposes.
+
+The accompanying source code under `dataset_release/scripts/` is released under
+the MIT License and may be reused, including commercially.
+
+Rationale for NonCommercial
+---------------------------
+The underlying reviews were published by users on third-party booking platforms
+(Booking.com, Traveloka, Ivivu). To respect those platforms' Terms of Service
+and the non-commercial spirit of academic data sharing, the derived dataset is
+restricted to research and educational (non-commercial) use only. See
+DATASHEET.md for the full ethical and legal discussion.
+
+CITATION
+--------
+If you use this dataset, please cite:
+
+@article{vihorec,
+  title  = {ViHoRec: A Vietnamese Hotel Recommendation Dataset with a
+            Documented Collection and Quality-Control Pipeline},
+  author = {Nguyen, Hoang Minh},
+  year   = {2026}
+}

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+language:
+- vi
+pretty_name: ViHoRec
+size_categories:
+- 10K<n<100K
+task_categories:
+- other
+tags:
+- recommendation
+- recommender-systems
+- hotel
+- vietnamese
+- cold-start
+- tabular
+- entity-resolution
+- parquet
+- csv
+configs:
+- config_name: interactions
+  default: true
+  data_files:
+  - split: train
+    path: data/interactions/train-*.parquet
+- config_name: users
+  data_files:
+  - split: train
+    path: data/users/train-*.parquet
+- config_name: hotels
+  data_files:
+  - split: train
+    path: data/hotels/train-*.parquet
+- config_name: benchmark
+  data_files:
+  - split: train
+    path: data/benchmark/train-*.parquet
+  - split: test
+    path: data/benchmark/test-*.parquet
+dataset_info:
+- config_name: interactions
+  features:
+  - name: user_id
+    dtype: string
+  - name: hotel_id
+    dtype: string
+  - name: rating
+    dtype: float32
+  - name: date
+    dtype: string
+  - name: source
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 18267
+- config_name: users
+  features:
+  - name: user_id
+    dtype: string
+  - name: n_interactions
+    dtype: int64
+  splits:
+  - name: train
+    num_examples: 6832
+- config_name: hotels
+  features:
+  - name: hotel_id
+    dtype: string
+  - name: name
+    dtype: string
+  - name: location
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 560
+- config_name: benchmark
+  features:
+  - name: userID
+    dtype: int64
+  - name: itemID
+    dtype: int64
+  - name: rating
+    dtype: float32
+  - name: timestamp
+    dtype: int64
+  splits:
+  - name: train
+    num_examples: 9787
+  - name: test
+    num_examples: 800
+---
+
+# ViHoRec — Vietnamese Hotel Recommendation Dataset
+
+A **quality-controlled, anonymised, benchmark-ready** Vietnamese hotel
+recommendation dataset for recommender-systems research.
+
+| Resource | Count |
+|---|---|
+| Interactions (cleaned) | 18,267 |
+| Users | 6,832 |
+| Hotels | 560 |
+| Benchmark split | 800 users × 535 hotels (9,787 train / 800 test) |
+
+Sources: Booking.com, Traveloka, Ivivu. License: **CC BY-NC 4.0** (data), MIT (code on GitHub).
+
+## Dataset Viewer / subsets
+
+The Hub Dataset Viewer is configured via the YAML `configs` block above.
+Use the **Subset** dropdown:
+
+| Subset | Splits | Rows | Description |
+|---|---|---|---|
+| `interactions` (default) | `train` | 18,267 | user–hotel ratings |
+| `users` | `train` | 6,832 | user aggregates |
+| `hotels` | `train` | 560 | hotel metadata |
+| `benchmark` | `train` / `test` | 9,787 / 800 | public LOO split |
+
+Data files live under `data/<config>/<split>-00000-of-00001.parquet` (plus CSV twins for convenience).
+
+## Load with 🤗 Datasets
+
+```python
+from datasets import load_dataset
+
+# default subset = interactions
+ds = load_dataset("MinhDS/ViHoRec")
+print(ds["train"][0])
+
+interactions = load_dataset("MinhDS/ViHoRec", "interactions")
+users = load_dataset("MinhDS/ViHoRec", "users")
+hotels = load_dataset("MinhDS/ViHoRec", "hotels")
+benchmark = load_dataset("MinhDS/ViHoRec", "benchmark")  # train + test
+
+# streaming (no full download)
+stream = load_dataset("MinhDS/ViHoRec", "interactions", split="train", streaming=True)
+for row in stream.take(3):
+    print(row)
+```
+
+Or with pandas / Polars:
+
+```python
+import pandas as pd
+
+interactions = pd.read_parquet("hf://datasets/MinhDS/ViHoRec/data/interactions/train-00000-of-00001.parquet")
+train = pd.read_parquet("hf://datasets/MinhDS/ViHoRec/data/benchmark/train-00000-of-00001.parquet")
+test = pd.read_parquet("hf://datasets/MinhDS/ViHoRec/data/benchmark/test-00000-of-00001.parquet")
+```
+
+## Repository layout (Hub)
+
+```
+data/
+├── interactions/train-00000-of-00001.{parquet,csv}
+├── users/train-00000-of-00001.{parquet,csv}
+├── hotels/train-00000-of-00001.{parquet,csv}
+└── benchmark/
+    ├── train-00000-of-00001.{parquet,csv}
+    └── test-00000-of-00001.{parquet,csv}
+benchmark/                   # reference artifacts (not in Viewer configs)
+├── user_map.csv / item_map.csv
+├── split_config.json
+└── baseline_results*.csv
+DATASHEET.md
+LICENSE
+README.md
+```
+
+## Key statistics
+- Raw interactions: 18,274 (Booking 7,597 / Traveloka 6,273 / Ivivu 4,404)
+- After cleaning: **18,267** interactions, **6,832** users, **560** hotels
+- Entity matching merged 21 cross-site name variants; 78 hotels on ≥2 sites
+- Benchmark split: 800 users × 535 items, 9,787 train / 800 test, 97.53% sparse
+
+## Full pipeline & code
+The reproducible construction scripts live on GitHub:
+[MinhNguyenDS/ViHoRec](https://github.com/MinhNguyenDS/ViHoRec).
+
+```bash
+# rebuild Hub-ready parquet/csv layout from release/
+python scripts/prepare_hf_hub.py
+# then: huggingface-cli upload MinhDS/ViHoRec hf_hub/ . --repo-type dataset
+```
+
+## Citation
+Please cite the ViHoRec data-descriptor paper when using this dataset.
+See `DATASHEET.md` for provenance, ethics, and known limitations.

benchmark/baseline_results.csv

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benchmark/baseline_results.md

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+| Method | MRR | MAP@5 | NDCG@5 | Precision@5 | Recall@5 | MAP@10 | NDCG@10 | Precision@10 | Recall@10 |
+|---|---|---|---|---|---|---|---|---|---|
+| Random | 0.0119 | 0.0036 | 0.0046 | 0.0016 | 0.0079 | 0.0054 | 0.0093 | 0.0023 | 0.0225 |
+| MostPop | 0.0496 | 0.0310 | 0.0385 | 0.0123 | 0.0612 | 0.0368 | 0.0528 | 0.0106 | 0.1062 |
+| ItemKNN-cosine | 0.0401 | 0.0212 | 0.0277 | 0.0095 | 0.0475 | 0.0252 | 0.0376 | 0.0079 | 0.0788 |
+| UserKNN-cosine | 0.0630 | 0.0387 | 0.0465 | 0.0140 | 0.0700 | 0.0472 | 0.0671 | 0.0134 | 0.1338 |
+| BPR-MF | 0.0512 | 0.0297 | 0.0369 | 0.0119 | 0.0592 | 0.0358 | 0.0519 | 0.0106 | 0.1058 |
+| Content-TFIDF | 0.0275 | 0.0118 | 0.0165 | 0.0063 | 0.0312 | 0.0153 | 0.0249 | 0.0057 | 0.0575 |

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benchmark/split_config.json

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+  "protocol": "leave-last-one-out (temporal)",
+  "min_interactions": 4,
+  "seed": 42,
+  "n_users": 800,
+  "n_items": 535,
+  "n_train": 9787,
+  "n_test": 800,
+  "sparsity_pct": 97.5264
+}

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