Recommendation-System

Overview

Built 3 different types of recommendation system to predict the ratings/stars for given user ids and business ids. Used the validation dataset to evaluate the accuracy of recommendation systems.

1. Item-based CF recommendation system

   Implemented item-based recommendation system using Pearson similarity formula.

2. Model-based recommendation system

   Implemented Model-based recommendation system using XGBregressor(a regressor based on the decision tree) to train a model and use the validation dataset to validate your result.

3. Hybrid recommendation system

   Implemented Hybrid recommendation system by taking weighted average of item-based CF and Model-based recommendation system by using the below formula:

   final score = 𝛼 × score_{𝑖𝑡𝑒𝑚_𝑏𝑎𝑠𝑒𝑑} + (1−𝛼) × 𝑠𝑐𝑜𝑟𝑒_{𝑚𝑜𝑑𝑒𝑙_𝑏𝑎𝑠𝑒𝑑}

Link to Data files: https://drive.google.com/drive/folders/1kdQlFvqEKkQUXv3JmpH2fCS2NPS_Q5tw

I used RMSE to check the accuracy of my recommendation systems.

 Type of Recommendation System          RMSE  
 
Item-based CF recommendation system:    1.05  
Model-based recommendation system:      0.99  
Hybrid recommendation system:           0.98