VRICR: Variational Reasoning over Incomplete KGs Conversational Recommender
The source code for WSDM 2023 Paper "Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation"
Overview
We propose Variational Reasoning over Incomplete KGs Conversational Recommender. Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete knowledge graphs; and adopt the variational Bayesian method to perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.
Saved Models
We have trained our model on two datasets and saved the parameters, all of which have been uploaded to Google Drive.
The downloaded ckpt files should be moved into data/ckpt
.
Quick-Start
We run all experiments and tune hyperparameters on a RTX3090 with 24GB memory, you can adjust train_batch_size
and test_batch_size
according to your GPU, and then the optimization hyperparameters also need to be tuned.
sh script/redial/redial_rec_pretrain.sh
sh script/redial/redial_rec_finetune.sh # remember to change --task_ID_for_pretrain and --last_ckpt_path_for_pretrain
sh script/redial/redial_conv.sh
sh script/tgredial/train/redial_rec_pretrain.sh
sh script/tgredial/tgredial_rec_finetune.sh # remember to change --task_ID_for_pretrain and --last_ckpt_path_for_pretrain
sh script/tgredial/tgredial_conv.sh
You can also test the model has been saved by us.
sh script/redial/redial_rec_eval.sh
sh script/redial/redial_conv_eval.sh
sh script/tgredial/eval/tgredial_rec_eval.sh
sh script/tgredial/eval/tgredial_conv_eval.sh
Contact
If you have any questions for our paper or codes, please send an email to xiaoyu.zhang@mail.sdu.edu.cn.
Acknowledgement
Our datasets and data process code are developed based on C2-CRS
Any scientific publications that use our codes should cite our paper as the reference.