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InBox: Recommendation with Knowledge Graph using Interest Box Embedding

This repository contains the code for the paper "InBox: Recommendation with Knowledge Graph using Interest Box Embedding," which has been accepted by VLDB.

Code Organization

The codebase is organized as follows:

  • main.py: The main script to run the project.
  • model.py: Contains the model definitions.
  • dataloader.py: Contains the data loading utilities.
  • utils/: Contains utility functions and scripts for data processing and evaluation.

Running the Project

CUDA_VISIBLE_DEVICES=0 python main.py --cuda --dataset=alibaba-fashion -pre -pre_i -train -test

CUDA_VISIBLE_DEVICES=0 python main.py --cuda --dataset=yelp2018 -pre -pre_i -train -test

CUDA_VISIBLE_DEVICES=0 python main.py --cuda --dataset=last-fm -pre -pre_i -train -test

CUDA_VISIBLE_DEVICES=0 python main.py --cuda --dataset=amazon-book -pre -pre_i -train -test -pre_epoch 8

Datasets

The project includes the following datasets:

  • Alibaba Fashion
  • Amazon Book
  • Last.fm
  • Yelp 2018

Each dataset directory contains multiple files such as entity_list.txt, item_list.txt, kg_final.txt, etc.

Hardware/Software Requirements

  • Python 3.6+
  • PyTorch 1.7+
  • CUDA 10.1+
  • tensorboardX
  • scikit-learn
  • tqdm

Ensure that you have a compatible GPU and CUDA installed for running the project.