Santander Product Recommendation Engine with Deep Learning

Using the Santander dataset (data can be downloaded from https://www.kaggle.com/competitions/santander-product-recommendation/data) , implemented Auto Encoder (AE), Variational Auto Encoder (VAE), to fill in the scoring matrix, and then make recommendations to users.

It is recommended to run the code in Google Colab environment, please modify the directory in the code according to your actual directory.

The main directory and files of this code are as follows:

--------- data (folder containing datasets)
--------- train_and_test_set_preparation.ipynb
--------- recommendation_engine_with_collaborative_filtering_using_auto_encoder.ipynb

1 Models:

1.1 Auto Encoder

1.2 Variational Auto Encoder

2 Experimental Results:

2.1 MSE of training loss and validation loss of Auto Encoder

2.2 MSE of training loss and validation loss of Variational Auto Encoder

2.3 Hit Ratio of different models

Model	Hit Ratio (n_recommend = 10)
Auto Encoder	0.53
Variational Auto Encoder	0.37