Yelp-Recommender

Recommender system project for DSCI 553 - Foundations and Applications of Data Mining at the University of Southern California (USC).

• Goal: Given a user-business pair on Yelp, accurately predict the rating given to the business by the user.
  • Training dataset of 455,854 points
  • Validation dataset of 142,044 points
• Weighted hybrid recommender system with model-based and item-based collaborative filtering components
• Final RMSE: 0.98477 stars
• Execution time: 96 seconds
• Error distribution:

Error range (stars)	Number of observations	Percentage of validation set
>=0 and <=1	101,530	71.48%
>1 and <=2	33,427	23.53%
>2 and <=3	6,327	4.45%
>3 and <=4	760	0.54%
>4 and <=5	0	0.00%

• Future improvements:
  • Including even more features from dataset: number of Yelp friends a user has, compliments on Yelp profile, etc.
  • Upgrading existing packages (XGBoost, PySpark, Scikit-Learn) to take advantage of latest features
    • Fitting XGBoost model with reg:squarederror loss function, learning rate scheduler, dynamic early stopping threshold
    • Dynamically weighting hybrid recommender system or focusing on features to determine weights