Pytorch implementation for the paper: Contrastive Learning for Cold-start Recommendation

Related tags

Deep LearningCLCRec
Overview

Contrastive Learning for Cold-start Recommendation

This is our Pytorch implementation for the paper:

Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan Li, Xuanping Li, and Tat-Seng Chua (2021). Contrastive Learning for Cold-start Recommendation, Paper in ACM DL or Paper in arXiv. In ACM MM`21, Chengdu, China, Oct. 20-24, 2021
Author: Dr. Yinwei Wei (weiyinwei at hotmail.com)

Introduction

In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a new Contrastive Learning-based Cold-start Recommendation framework (CLCRec).

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{CLCRec,
  title     = {Contrastive Learning for Cold-start Recommendation},
  author    = {Wei, Yinwei and 
               Wang, Xiang and 
               Qi, Li and
               Nie, Liqiang and 
               Li, Yan and 
               Li, Xuanqing and 
               Chua, Tat-Seng},
  booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
  pages     = {--},
  year      = {2021}
}

Environment Requirement

The code has been tested running under Python 3.5.2. The required packages are as follows:

  • Pytorch == 1.1.0
  • torch-cluster == 1.4.2
  • torch-geometric == 1.2.1
  • torch-scatter == 1.2.0
  • torch-sparse == 0.4.0
  • numpy == 1.16.0

Example to Run the Codes

The instruction of commands has been clearly stated in the codes.

  • Movielens dataset
    python main.py --model_name='CLCRec' --l_r=0.001 --reg_weight=0.1 --num_workers=4 --num_neg=128 --has_a=True --has_t=True --has_v=True --lr_lambda=0.5 --temp_value=2.0 --num_sample=0.5

  • Amazon dataset
    python main.py --model_name='CLCRec' --data_path=amazon --l_r=0.001 --reg_weight=0.001 --num_workers=4 --num_neg=512 --has_v=True --lr_lambda=0.9 --num_sample=0.5

Some important arguments:

  • lr_lambda: It specifics the value of lambda to balance the U-I and R-E mutual information.

  • num_neg This parameter indicates the number of negative sampling.

  • num_sample: This parameter indicates the probability of hybrid contrastive training.

  • temp_value: It specifics the temprature value in density ratio functions.

Dataset

We provide two processed datasets: Movielens and Amazon. (The details could be found in our article) For Kwai and Tiktok datasets, due to the copyright, please connect the owners of datasets.

Owner
Thank you for your attention. If you have any questions, please email me.
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