SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity

This repository is the official implementation of the paper "SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity". Access the full paper here: https://www.amazon.science/publications/simrec-mitigating-the-cold-start-problem-in-sequential-recommendation-by-integrating-item-similarity

Datasets

data_preprocessing contains the code for generating the datasets.

This includes 2 steps:

1. Generating the dataset, by using the jupyter notebook preprocessing_data.ipynb that can be found in each sub-directory.
2. Calcualting the similarity scores using the jupyter notebook calculate_similarity_scores.ipynb

After you create the dataset(s) you can move to training the model.

Model Training

SimRec contains the code for training SimRec on the generated datasets.

To run SimRec:

cd SimRec

Train the model on Beauty dataset:

bash SimRec/scripts/Beauty/train.sh

Train the model on Tools dataset:

bash SimRec/scripts/Tools/train.sh

Train the model on Pet Supplies dataset:

bash SimRec/scripts/PetSupplies/train.sh

Train the model on Home & Kitchen dataset:

bash SimRec/scripts/HomeKitchen/train.sh

Train the model on ML-1M dataset:

bash SimRec/scripts/ML-1M/train.sh

Train the model on Steam dataset:

bash SimRec/scripts/Steam/train.sh

Note that the implementation of SimRec is based on SASRec.pytorch repo.

Requirements

pip install -r requirements

Hardware

In general, all experiments can run on either GPU or CPU.

License

This project is licensed under the Apache-2.0 License.