CO2 removal

EGA plot - LC - all items - WNM condition - contiguous order

EGA plot of LC data - all items - WNM condition - random order

Briefly describe the recommender system that you’re going to build out from a business perspective, e.g. “This system recommends data science books to readers.” • Find a dataset, or build out your own toy dataset. As a minimum requirement for complexity, please include numeric ratings for at least five users, across at least five items, with some missing data. • Load your data into (for example) an R or pandas dataframe, a Python dictionary or list of lists, (or another data structure of your choosing). From there, create a user-item matrix. • If you choose to work with a large dataset, you’re encouraged to also create a small, relatively dense “user-item” matrix as a subset so that you can hand-verify your calculations. • Break your ratings into separate training and test datasets. • Using your training data, calculate the raw average (mean) rating for every user-item combination. • Calculate the RMSE for raw average for both your training data and your test data. • Using your training data, calculate the bias for each user and each item. • From the raw average, and the appropriate user and item biases, calculate the baseline predictors for every user-item combination. • Calculate the RMSE for the baseline predictors for both your training data and your test data. • Summarize your results.

EGA plot - LC data - original items only - WNM condition - contiguous order

EGA plot - original items only - WNM condition - random order

Autores: - Marcos David Carrillo Builes - Tomás Escobar Rivera Monsalve - Jose Fernando López Ramírez - Esteban Vásquez Pérez

EGA plot - LC data - all items - MNW condition - contiguous order

EFA plot of LC data - all items - MNW condition - random order