pepler's Introduction

PEPLER (PErsonalized Prompt Learning for Explainable Recommendation)

Paper

Lei Li, Yongfeng Zhang, Li Chen. Personalized Prompt Learning for Explainable Recommendation. ACM Transactions on Information Systems (TOIS), 2023.

A T5 version that can perform multiple recommendation tasks is available at POD!

A small unpretrained Transformer version is available at PETER!

A small ecosystem for Recommender Systems-based Natural Language Generation is available at NLG4RS!

Datasets to download

TripAdvisor Hong Kong
Amazon Movies & TV
Yelp 2019

For those who are interested in how to obtain (feature, opinion, template, sentiment) quadruples, please refer to Sentires-Guide.

Usage

Below are examples of how to run PEPLER (continuous prompt, discrete prompt, MF regularization and MLP regularization).

python -u main.py \
--data_path ../TripAdvisor/reviews.pickle \
--index_dir ../TripAdvisor/1/ \
--cuda \
--checkpoint ./tripadvisor/ >> tripadvisor.log

python -u discrete.py \
--data_path ../TripAdvisor/reviews.pickle \
--index_dir ../TripAdvisor/1/ \
--cuda \
--checkpoint ./tripadvisord/ >> tripadvisord.log

python -u reg.py \
--data_path ../TripAdvisor/reviews.pickle \
--index_dir ../TripAdvisor/1/ \
--cuda \
--use_mf \
--checkpoint ./tripadvisormf/ >> tripadvisormf.log

python -u reg.py \
--data_path ../TripAdvisor/reviews.pickle \
--index_dir ../TripAdvisor/1/ \
--cuda \
--rating_reg 1 \
--checkpoint ./tripadvisormlp/ >> tripadvisormlp.log

Code dependencies

Python 3.6
PyTorch 1.6
transformers 4.18.0

Code reference

mkultra: Prompt Tuning Toolkit for GPT-2

Citation

@article{TOIS23-PEPLER,
	title={Personalized Prompt Learning for Explainable Recommendation},
	author={Li, Lei and Zhang, Yongfeng and Chen, Li},
	journal={ACM Transactions on Information Systems (TOIS)},
	year={2023}
}

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