Collaborative Filtering | BUIR

An impementation of "Bootstrapping User and Item Representations for One-Class Collaborative Filtering".

Additionally, formulation of cold start problem over the existing paper.

Run

Store ml-100k dataset under data

Training:

python3 train.py

Options for Training:

python3 train.py --exp_name [experiment name] --exp_disc [experiment discription] --model [type of model used]
                 --latent_size [latent embeddings size] --epochs [num of epochs] --lr [learning rate]
                 --weight_decay [weight decay] --batch_size [] --momentum [] --train_ratio [train-test split ratio]
                 --num_workers [workers for dataLoader] --cold_start [flag for performing cold start]
                 --cold_start_clusters [num of cluster in cold start kmeans]

Output logs and Plots will be saved in ./experiments/{exp_name}