Building a Recommender System

Starting the project and first Impressions

Exploratory Data Analysis

Question 1: What are the counts for User Ratings?

Building the Model

The model was built using ALS and the parameters were tuned using CrossValidation. The model takes in ratings for movies and then recommends a given number of recommendations based on those ratings. For the recommended movies, the model predicts the score the user would give those movies and recommends the movies with the highest predicted ratings.

Modernizing the Model

To push the project further, I attempted to modernize the model by changing the ratings to a binary system to match a Thumbs Up or Thumbs Down system which is the type of recommender engine that has become most popular. Any rating given under a 3 was considered a 0, or Thumbs Down, while a 3 or above was a 1, or Thumbs Up.

Future Work

My future goals for this project would be to try and improve the system which could be done by trying different parameters on the current model or trying new model types other than ALS altogether. More insights on how the model could be improved could be acquired through more intensive EDA. This project could be useful to learn how to create a user interface, which would make the utilization of this code much easier for the average person to use and receive film recommendations.



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