YelpRecommendation

Yelp Recommendation: Collaborative Filtering, XGboost, RBM, Auto-encoder

I build a recommendation system to predict a rating that a user will give to a restaurant. I use dataset from Yelp Dataset Challenge (https://www.yelp.com/dataset/challenge).

The original dataset consists of 1,326,101 users, 174,567 business, and 5,261,669 reviews.

There are four parts:

1. Data exploration.

Code 'dataExploration'

2.Applying Collaborative Filtering Algorithm, Latent Factor Model.

Code 'CF_RecommendationsALS.scala', 'CollaborativeFilter.py', 'lfm-tf.py'

3.Applying AutoEncoder, Restricted Boltzmann Machine (RBM).

Code 'AE_final.py', 'RBM_final.py'.

4. Using other available attributes to make a prediction, Logistic Regression, XGBoost.

Code 'MachineLearningmain.py'

5. Combined all the models to a voting classfier.

Code 'combine.py' and 'main.py