RecNet
Session based recommendation system using state of the art Transformers and Matrix Factorization on MovieLens-1M.
Install / Use
/learn @arnavc1712/RecNetREADME
RecNet
Session based recommendation system using state of the art Transformers and Matrix Factorization on MovieLens-1M. <br/> Please refer to the Report to view a detailed explanation of our project.
Model Architecture

Results

Learned Item Embeddings from Scratch

Install Dependencies
pip install -r requirements.txt
Instructions to run code
- Create
save/directory inside the./Codefolder in order to save checkpoints - Run
python Code/train.py --max_seq_len 200 --num_layers 2for the Transformer Model - Run
python Code/trainRNN.py --max_seq_len 200 --num_layers 2for the RNN Model
Tensorboard
tensorboard --logdir=runs
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