This question involves the use of Python for implementing a collaborative filtering recommendation system. Before you start, download the required CSV file Download CSV file that consists of movie ratings with user IDs, movie IDs, ratings, and timestamps. The following IPYNB file Download $$IPYNB file contains essential code for establishing the foundations of a recommendation system, including the code to compute user$-$user similarity using cosine similarity.

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Given the code that reads the $$ratings$.$csv$$ file, splits the data, and prepares the user$-$item interaction matrix:

Use the provided user$-$user similarity matrix to predict user ratings for movies.

Assume that the system tests on movies that have been previously rated by the users.

Set the random state to $1$ and split the data into $70\%$ for training and $30\%$ for testing.

Employ the RMSE metric for evaluation.

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If we were to create an item$-$based recommendation system using the same dataset and settings, which system would likely perform better, achieving a lower RMSE score?

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Hint: To modify the existing user$-$user collaborative filtering setup to an item$-$item collaborative filtering system, consider what the $$pivot$$ function is doing in the code. It$$s currently creating a matrix with users on the rows and movies on the columns. For item$-$item collaborative filtering, you$$ll want to pivot your data such that movies become the rows and users become the columns, as this will prepare your data to calculate similarities between items $($movies$)$ instead of users.

This question involves the use of Python for implementing a collaborative filtering recommendation system. Before you start, download the required CSV file Download CSV file that consists of movie ratings with user IDs, movie IDs, ratings, and timestamps. The following IPYNB file Download $$IPYNB file contains essential code for establishing the foundations of a recommendation system, including the code to compute user$-$user similarity using cosine similarity.

$$

Given the code that reads the $$ratings$.$csv$$ file, splits the data, and prepares the user$-$item interaction matrix:

Use the provided user$-$user similarity matrix to predict user ratings for movies.

Assume that the system tests on movies that have been previously rated by the users.

Set the random state to $1$ and split the data into $70\%$ for training and $30\%$ for testing.

Employ the RMSE metric for evaluation.

$$

If we were to create an item$-$based recommendation system using the same dataset and settings, which system would likely perform better, achieving a lower RMSE score?

$$

Hint: To modify the existing user$-$user collaborative filtering setup to an item$-$item collaborative filtering system, consider what the $$pivot$$ function is doing in the code. It$$s currently creating a matrix with users on the rows and movies on the columns. For item$-$item collaborative filtering, you$$ll want to pivot your data such that movies become the rows and users become the columns, as this will prepare your data to calculate similarities between items $($movies$)$ instead of users.

User$-$based collaborative filtering outperforms item$-$based collaborative filtering.

Item$-$based collaborative filtering outperforms user$-$based collaborative filtering. $-$ My question is which option is correct as per the description, I$\text{'}$m sure code descriptions are there.