Chengyang Zhang, Xianying Huang*, Jiahao An.
MACR: Multi-information Augmented Conversational Recommender
We propose a Multi-information Augmented Conversational Recommender (MACR), which improves the performance of recommendation and response generation by mining the underlying category preferences in users' utterances and incorporating item introductions.
Please download data from the link, after unzipping, move it into data. Note that the folder data needs to be created by yourself.
- python == 3.6.1
- pytorch == 1.7.0
- torch_geometric == 2.0.1
- cuda == 11.0
Run the recommendation module:
python run_train_test_copy.py
Run the dialogue module:
python run_train_test_copy.py --is_finetune True
@article{ZHANG2023118981,
title = {MACR: Multi-information Augmented Conversational Recommender},
journal = {Expert Systems with Applications},
volume = {213},
pages = {118981},
year = {2023},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2022.118981},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422019996},
author = {Chengyang Zhang and Xianying Huang and Jiahao An},
keywords = {Conversational recommender system, Knowledge graph, Category information, Item introduction}
}