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KG-Conv-Rec

"Suggest me a movie for tonight": Leveraging Knowledge Graphs for Conversational Recommendation

Rajdeep Sarkar, Koustava Goswami, Mihael Arcan, John McCrae.
In The 28th International Conference on Computational Linguistics (COLING 2020)

Prerequisites

  • Python==3.6
  • torch==1.3.0
  • torch-cluster==1.4.5
  • torch-geometric==1.3.2
  • torch-sparse==0.4.3
  • torchtext==0.6.0
  • torchvision==0.4.1

Getting Started

Installation

Clone this repo.

git clone https://github.com/rajbsk/KG-conv-rec.git
cd KG-conv-rec/

Please install dependencies by

pip install -r requirements.txt

Dataset

  • We use the ReDial dataset, which will be automatically downloaded by the script.
  • The models and the dataset used in this work are stored in google drive. Download data.zip from Google Drive and extract inside the KG-conv-rec folder.

Training

To train the recommender part, run:

bash scripts/both.sh <subgraph_model_name> <num_exps> <gpu_id>

Where subgraph_model_name takes values 2_hop, 3_hop, 5_hop, pr, pr_0.7, pr_0.9 for the models build using subgraphs constructed using 2 hop, 3 hop, 5hop, PageRank, Personalized PageRank(alpha=0.7), Personalized PageRank(alpha=0.9) respectively. num_exps is the number of experiments and gpu_id is the id pf the gpu you want to run your code on.

Logging

TensorBoard logs and models will be saved in saved/ folder.

Cite

Please cite our paper if you use this code in your own work:

@article{sarkar2020kgrec,
  title={"Suggest me a movie for tonight": Leveraging Knowledge Graphs for Conversational Recommendation},
  author={Rajdeep Sarkar and Koustava Goswami and Mihael Arcan and John McCrae},
  booktitle = {Proceedings of the 28th International Conference on Computational
               Linguistics, {COLING} 2020, Barcelona, Spain, December 8-11,
               2020},
  year={2020}
}