Abstract

Content-Based Movie Recommender System built using the cosine similarity scores

Table of Content

1. Executive Summary

2. Introduction

3. Algorithm

3.1. Content-Based filtering

3.2. Cosine Similarity

4. Objective

5. Methodology

5.1. Sample Database schema

5.2. Python-Oracle Database connection

5.3. Preparing the sample dataset

5.4. Identifying the highest rated movies and getting the best movie details

5.5. Merging key features and building a cosine similarity matrix

5.6. Generating Recommendations

5.7. Results

6. Conclusion

7. Project Repository

8. References

1. Executive Summary

Whenever we visit a digital platform, we no longer need to worry about what to watch next as we are served with a bunch of recommendations to choose from. But how exactly the platform decides what to recommend to a specific user, and if the user is going to like that.

In this project, we attempt to build a specific kind of recommendation system by extending the RELMDB oracle database. We attempt to build a recommender system that identifies the best movie, based on the number of IMDb votes and the average IMDb rating, and suggest movies with similar content to the user. We start by importing and munging data from the oracle database to create our base dataset. We, then, identify the best movie and use the cosine similarity algorithm to recommend the top 10 similar movies to the user.

#recommendation-system #machine-learning-python #data-science #python