I am requesting a feature enhancement in main.py to include Mean Absolute Error (MAE) as an evaluation metric for collaborative filtering. Currently, the evaluation uses RMSE and Spearman correlation. Please create a separate function to calculate MAE and integrate this function into the evaluation process wherever necessary.

Proposed Changes:

1. Implement a function to calculate MAE.
2. Integrate the MAE calculation function into the collaborative filtering evaluation process.
3. Ensure that the MAE metric is included alongside RMSE and Spearman correlation in the evaluation outputs.

The dependencies are not compatible with Python version 3.12 running into import errors.

I am requesting a feature enhancement for the similarity calculations in the project. Currently, NaN values are handled as 0, which may lead to inaccurate similarity scores. Instead, NaN values should be replaced with the mean value of the respective feature for more accurate similarity calculations.

Proposed Changes:

1. Identify the locations of NaN values in the dataset.
2. Replace NaN values with the mean of the respective feature (row/column) before performing similarity calculations.
3. Ensure that the updated similarity calculations are integrated into the existing collaborative filtering process.
   Benefits:

• Improved accuracy of similarity scores.
• Better handling of missing data by using statistical imputation.

The collaborative.py file currently calculates collaborative filtering using the baseline approach. I would like to request an enhancement to include code that calculates the time it takes to compute collaborative filtering both with and without the baseline approach.

Proposed Changes:

1. Add timing code to measure the duration of the collaborative filtering process using the baseline approach.
2. Add timing code to measure the duration of the collaborative filtering process without using the baseline approach.
3. Output or log the time taken for both approaches for comparison.
   This enhancement will help in evaluating the performance impact of using the baseline approach versus not using it.

I am requesting the addition of a function to invert a large, dense matrix using Singular Value Decomposition (SVD) from scipy.sparse.linalg. This will facilitate more efficient matrix operations for collaborative filtering or any other analogous operations.

Proposed Changes:

1. Implement a function to perform SVD on a large, dense matrix using scipy.sparse.linalg.svds.
2. Use the SVD results to compute the matrix inversion.
3. Integrate this functionality where necessary in the codebase.