movie_user_mat = df_ratings_drop_users.pivot(index='movieId', columns='userId', values='rating').fillna(0)

When I run this code, I get an error: "ValueError: Unstacked DataFrame is too big, causing int32 overflow". So how do you resolve this problem?

@KevinLiao159 May I know how to make_recommendation out of this model

predictions = GMF_model.predict([df_test.userId.values, df_test.movieId.values])

Thanks

Keras expects float in model training so model definition should be;

train model

history = train_model(GMF_model, 'adam', BATCH_SIZE, EPOCHS, VAL_SPLIT,
inputs=[df_train.userId.values.astype(np.float32), df_train.movieId.values.astype(np.float32)],
outputs=df_train.rating.values)

Hi Kevin, thanks for the nice code.

But I found something which is really weirded.
When I want to search recommendations for 'Day After Tomorrow', it seems that it is recommending a movie with movie ID of 8069, which does not even in the training set movie_user_mat_sparse. Thus gives an error when looking up the value for key of 8069 in reverse_mapper, which does not even have a 8069 key.