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MOVIE RECOMMENDATION SYSTEM - Machine Learning Project Help


Introduction

In today's digital era, online streaming platforms offer an extensive library of movies, making it challenging for users to discover new content they will enjoy. To address this challenge and improve customer satisfaction, a movie recommendation system comes to the rescue. In this blog post, we will delve into the world of movie recommendation systems, their significance, and how they can enhance the overall movie-watching experience.


Overview

A movie recommendation system is a sophisticated software or algorithm that provides personalized movie suggestions to users based on their preferences and viewing history. By analyzing a user's behavior, such as ratings, reviews, and movie interactions, these systems predict and recommend movies that align with their taste.


Dataset

For this, we will use ratings.csv dataset.

The ratings dataset contains the following attributes:

  • userId

  • movieId

  • rating

  • timestamp



Tasks

In this project, we will be building various recommendation systems:

  • Knowledge/Rank based recommendation system

  • Similarity-Based Collaborative filtering

  • Matrix Factorization Based Collaborative Filtering


While building those recommendation systems, we will address these questions:

Q 1.1 Print the top 5 rows of the dataset

Q 1.2 Describe the distribution of ratings.

Q 1.3 What is the total number of unique users and unique movies?

Q 1.4 Is there a movie in which the same user interacted with it more than once?

Q 1.5 Which is the most interacted movie in the dataset?

Q 1.6 Which user interacted the most with any movie in the dataset?

Q 1.7 What is the distribution of the user-movie interactions in this dataset?

Question 2: Create Rank-Based Recommendation System and perform the followings:

  • Recommending top 5 movies with 50 minimum interactions based on popularity.

  • Recommending top 5 movies with 100 minimum interactions based on popularity.

  • Recommending top 5 movies with 200 minimum interactions based on popularity.

  • Build the first baseline similarity based recommendation system using cosine similarity and KNN.

Q 3.1 What is the RMSE for baseline user based collaborative filtering recommendation system?

Q 3.2 What is the Predicted rating for a user with userId=4 and for movieId=10 and movieId=3?

Q 3.3 Perform hyperparameter tuning for the baseline user based collaborative filtering recommendation system and find the RMSE for tuned user based collaborative filtering recommendation system?

Q 3.4 What is the Predicted rating for a user with userId =4 and for movieId= 10 and movieId=3 using tuned user based collaborative filtering?

Q 3.5 Predict the top 5 movies for userId=4 with similarity based recommendation system

Q 4.1 What is the RMSE for baseline item based collaborative filtering recommendation system ?

Q 4.2 What is the Predicted rating for a user with userId =4 and for movieId= 10 and movieId=3?

Q 4.3 Perform hyperparameter tuning for the baseline item based collaborative filtering recommendation system and find the RMSE for tuned item based collaborative filtering recommendation system?

Q 4.4 What is the Predicted rating for an item with userId =4 and for movieId= 10 and movieId=3 using tuned item based collaborative filtering?

Q 4.5 Predict the top 5 movies for userId=4 with similarity based recommendation system

Q 5.1 What is the RMSE for baseline SVD based collaborative filtering recommendation system?

Q 5.2 What is the Predicted rating for a user with userId =4 and for movieId= 10 and movieId=3?

Q 5.3 Perform hyperparameter tuning for the baseline SVD based collaborative filtering recommendation system and find the RMSE for tuned SVD based collaborative filtering recommendation system?

Q 5.4 What is the Predicted rating for a user with userId =4 and for movieId= 10 and movieId=3 using SVD based collaborative filtering?

Q 5.5 Predict the top 5 movies for userId=4 with SVD based recommendation system?

Question 6: Compute the precision and recall, for each of the 6 models, at k = 5 and 10. This is 6 x 2 = 12 numerical values?

Question 7 ( 5 Marks)

7.1 Compare the results from the base line user-user and item-item based models.

7.2 How do these baseline models compare to each other with respect to the tuned user-user and item-item models?

7.3 The matrix factorization model is different from the collaborative filtering models. Briefly describe this difference. Also, compare the RMSE and precision recall for the models.

7.4 Does it improve? Can you offer any reasoning as to why that might be?




Movie recommendation systems have revolutionized the way users discover and enjoy movies. By leveraging advanced algorithms and techniques like collaborative filtering and matrix factorization, these systems provide tailored suggestions, leading to a more satisfying viewing experience. In this blog post, we explored different recommendation systems and their performance metrics, highlighting the importance of such systems in enhancing customer satisfaction and boosting revenue for online streaming platforms.


If you need implementation for the above problem or any of its variants, feel free to contact us.

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