Synthetic Customer and Hotel Profiles for Recommendation System Testing

Synthetic data designed for developing a fuzzy-semantic decision model for personalized hotel recommendations. The dataset contains 10 customer preference profiles and 5 hotel attribute profiles, generated through a controlled randomization process. It was created by Ditdit Nugeraha Utama and published on figshare in May 2026.

Use Cases

• Testing personalized recommendation algorithms based on semantic distance calculation.
• Benchmarking fuzzy inference mechanisms for matching customer preferences to hotel attributes.
• Developing models that consider parameters like budget, service quality, and cultural compatibility.

Strengths

• Dataset is small (3.8 KB), facilitating quick download and experimentation.
• Includes 14 paired decision parameters covering aspects like budget, cleanliness, and digital facilities.
• Data was generated through a controlled randomization process for consistency.

Limitations

• The dataset is very small, with only 10 customer and 5 hotel records, limiting statistical power.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Generated through a controlled randomization process.

Freshness

Last updated 2026-05-15 03:19:27.