Get recommendations based on your ratings of other restaurants
- Browsing and reading reviews is time-consuming
- Limited focus on only top-rated restaurants
- No personalized recommendations
| Data Feature | Details |
|---|---|
| Cities | Seattle, San Francisco |
| Restaurants | 5,664 |
| Users | 510,240 |
| Ratings | 1,672,655 |
USPS.com
- Scrape all zip codes for Seattle and San Francisco
Yelp API
- Download restaurants for each zip code
Yelp.com
- Scrape all review pages for each restaurant
rating = avg rating + restaurant bias + user bias + residual
Non-negative Matrix Factorization trained on residual
- Implemented using PySpark ALS (Alternating Least Squares)
Included only users and restaurants with > 10 ratings
Tuned hyperparameters using cross validated grid search
- Rank = 76
- Regularization Parameter = 0.7
Normalized Discounted Cumulative Gain
- https://en.wikipedia.org/wiki/Discounted_cumulative_gain#Normalized_DCG
- Scoring metric that compares against ideal ordering
- Top 10 for each user
- Perfect score is 1.0
Scores of optimized model
| Data Set | NDCG@10 Score |
|---|---|
| Train | 0.9366 |
| Test | 0.9321 |
- Running on Amazon AWS EC2 instance
- Implemented algorithm to address “Cold Start” problem
- Dot product residuals of user ratings with restaurant latent features matrix to get best estimate of user's latent features.
- Allow users to login and save ratings
- Schedule nightly job to retrain model with new user ratings
- Add all restaurants for Washington, California, Oregon and Nevada
Sam Lee