KECRS

Towards Knowledge-Enriched Conversational Recommendation System.

Prerequisites

• Python 3.6
• PyTorch 1.4.0
• Torch-Geometric 1.4.2

Getting Started

Installation

Clone this repo.

git clone https://github.com/xiaolan98/KECRS.git
cd KECRS/parlai/task/crs/

Dataset

All the data are in * ./KECRS/data/crs/ *folder

• ReDial dataset
• The Movie Domain Knowledge Graph, TMDKG

Training

To train the recommender part, run:

python train_kecrs.py

To train the dialog part, run:

python train_transformer_rec.py

Logging

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

Evaluation

All results on testing set will be shown after training.

Discussion

If you have difficulties to get things working in the above steps, please let us know.

Cite

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

@inproceedings{zhang-etal-2022-toward,
    title = "Toward Knowledge-Enriched Conversational Recommendation Systems",
    author = "Zhang, Tong  and
      Liu, Yong  and
      Li, Boyang  and
      Zhong, Peixiang  and
      Zhang, Chen  and
      Wang, Hao  and
      Miao, Chunyan",
    booktitle = "Proceedings of the 4th Workshop on NLP for Conversational AI",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.nlp4convai-1.17",
    doi = "10.18653/v1/2022.nlp4convai-1.17",
    pages = "212--217",
    }