Building a Personalized Recommendation System

Author

Pascal Hermann Kouogang Tafo

INTRODUCTION

This assignment builds a personalized recommended system using survey data from “TV_Show_ratings.csv” that i manually created. The goal is to predict user preferences and recommend new TV shows, the users are likely to enjoy. To accomplish that goal, we will be implementing a User-to-User Collaborative Filtering algorithm using cosine similarity, generate top-N recommendations, and evaluate model performance.

APPROACH

The User-to-User Collaborative Filtering algorithm identifies users with similar tastes based on their past ratings and predicts ratings for items a user has not seen yet by aggregating the ratings of their “nearest neighbors”. To build that personalized recommended system, we will be using the following approach:

  • Load the “TV_Show_ratings.csv” from my public GitHub and convert it into a wide-format user-item matrix.

  • Calculate the average rating for each user and subtract it from their ratings to account for individual rating biases.

  • Use Cosine Similarity on the centered ratings to determine how similar users are to one another.

  • For any unwatched show, calculate a weighted average of the ratings given by the most similar users.