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RecNet

Session based recommendation system using state of the art Transformers and Matrix Factorization on MovieLens-1M.

Install / Use

/learn @arnavc1712/RecNet
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

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

Model

Results

Results

Learned Item Embeddings from Scratch

Embeddings

Install Dependencies

pip install -r requirements.txt

Instructions to run code

  • Create save/ directory inside the ./Code folder in order to save checkpoints
  • Run python Code/train.py --max_seq_len 200 --num_layers 2 for the Transformer Model
  • Run python Code/trainRNN.py --max_seq_len 200 --num_layers 2 for the RNN Model

Tensorboard

tensorboard --logdir=runs

Related Skills

View on GitHub
GitHub Stars4
CategoryDevelopment
Updated2mo ago
Forks2

Languages

Jupyter Notebook

Security Score

70/100

Audited on Jan 4, 2026

No findings