Collaborative Filtering is a method used by recommender systems to
make predictions about an interest of an specific user by collecting
taste or preferences information from many other users. The technique of
Collaborative Filtering has the underlying assumption that if a user A
has the same taste or opinion on an issue as the person B, A is more
likely to have B’s opinion on a different issue.
In this project I predict the ratings a user would give a movie based
on this user's taste and the taste of other users who watched and rated
the same and similar movies.
Devide the ratings.dat file from ml-1m.zip into training and testing datasets train.dat and test.dat. by using the command
python srcdatatrain_test_split.py
Use shell to make TF_Record files out of the both train.dat and test.dat files by executing the command:
python srcdatatf_record_writer.py
Use shell to start the training by executing the command (optionally parse your hyperparameters):
python training.py
Training Results
During the training after each epoch the loss on the training and
testing data set is shown. The loss is a root mean squared error loss
(MSE). The mean absolute error (mean_abs_error) is a better metric to
validate the performance however.mean_abs_error tells the differences
between predicted ratings and true ratings. E.g. a mean_abs_error of
0.923 means that on an average the predicted rating deviates from the
actual rating by 0.923 stars.