Datasets

Overview

Planwise combines multiple data sources to create a comprehensive recommendation system. Our data foundation includes user-place interactions, place metadata, and user reviews.

Core Data Sources

User-Place Ratings

File: rating-California.csv

This dataset contains user ratings of places on a scale of 1-5 stars:

• user_id: Unique identifier for each user
• place_id: Unique identifier for each place
• rating: Numerical rating from 1-5
• timestamp: When the rating was recorded

Active User List

File: filtered_users.csv

To ensure quality recommendations, we focus on users with significant engagement:

• Lists Google Maps IDs for users who have contributed at least 20 ratings
• Ensures our models learn from genuinely engaged participants

Place Metadata

File: meta-California.json

Rich metadata about each venue:

• Name
• Latitude/longitude coordinates
• Average community rating
• Total review count
• Array of Google Place types (e.g., [“art_gallery”, “museum”, …])

Madrid Places

File: combined_places.csv

Comprehensive dataset of Madrid venues:

• Place ID
• Name
• Location coordinates
• Place categories
• Average ratings
• Review count
• Descriptions

Review Sample

File: review-California_10.json

Sample of user reviews:

• Loaded in 1,000-line chunks for schema inspection
• Provides qualitative insights
• Useful for future feature ideas and sentiment analysis

Derived Datasets

Through our preprocessing pipeline, we generate several intermediate datasets:

Merged User-Place-Category Dataset

File: users_ratings_categories.csv

• Combines raw ratings with the active-user list
• Adds primary category information
• Columns: user_id, place_id, rating, category1

User-Category Aggregation

Files:

• average_user_ratings_per_category.csv
• user_counts_per_category.csv

These files provide aggregated metrics of how users rate different categories:

• Average rating per (user, category) combination
• Count of ratings per (user, category) combination

Active-Category Filtered Dataset

File: filtered_users_over_20_categories.csv

• Contains IDs of users who have rated more than 20 distinct primary categories
• Used to filter the dataset to users with diverse experience

Normalized Category Dataset

File: final_users_over_20_categories.csv

• Maps raw category codes to human-friendly buckets
• Example: “art_gallery” → “Art & Culture”
• This is the final cleaned dataset that feeds directly into all recommendation models

Data Quality

All datasets undergo thorough validation:

• Removal of duplicate entries
• Handling of missing values
• Validation of rating ranges
• Geocoding verification for place coordinates