Neural Network based Movie Recommendation

Project Walkthrough

Movie recommendation is based on the neural collaborative filtering technique. Movielens dataset consisting of 100K records is analyzed.

Phase 1 - Data preprocessing

1. Data Analysis and manipulation using pandas
   
2. Data Visualization using matplotlib
   
3. Test Train split
   

Phase 2 - Neural Network building

1. Created movie embedding path
   
2. Created user embedding path
   
3. Concatenated movie and user vectors
   
4. Weight initialization using the random uniform technique
   
5. Added 5 fully connected layers with activation function as ReLu
   
6. Adam optimizer used
   
7. Model has complied
   

Phase 3 - Training

1. Model is run for 18 epochs with verbosity 1
   
2. Achieved loss of 0.6
   
3. As seen below graph of Training error vs epoch, there is a reduction in error continuously after epoch=5
   

4. Minimum RSE of 0.8 is at epoch 18
   

Phase 4 - Movies suggested to the user

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Email - [email protected]
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