Collaborative Translational Metric Learning (TransCF)

Overview

Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based approaches have been proposed to overcome this issue, existing approaches typically project each user to a single point in the metric space, and thus do not suffice to properly model the intensity and the heterogeneity of user-item relationships of implicit feedback. In this paper, we propose TransCF to discover such latent user-item relationships embodied in implicit user-item interactions. Inspired by the translation mechanism popularized by knowledge graph embedding, we construct user-item specific translation vectors by employing the neighborhood information of users and items, and translate each user towards items regarding the user's relationships with the items.

Paper

• Translational Collaborative Metric Learning (ICDM 2018)
  • Chanyoung Park, Donghyun Kim, Xing Xie, Hwanjo Yu

Requirements

• Python version: 2.7
• Pytorch version: 0.3.0a0+669a99b
• fastrand (Fast random number generation in Python)
  • See installation instructions
• Multicore-TSNE (only required for t-SNE visualization)
  • See installation instructions

How to Run

git clone https://github.com/pcy1302/TransCF.git
cd TransCF
python main.py --recommender TransCF --dataset delicious --lRate 0.005 --mode Val

Configuration

You can evaluate TransCF with different settings. Below is a description of all the configurable parameters:

• --recommender : 'Choose a recommender.'
• --dataset : 'Choose a dataset.' (delicious, bookcrossing, ciao, cellphone)
• --embedding_dim : 'Number of embedding dimensions.'
• --lRate : 'Learning rate.'
• --margin' : 'Margin.'
• --reg1' : 'Distance regularizer.'
• --reg2' : 'Neighborhood regularizer.'
• --mode : 'Validation or Test' (Val, Test)
• --numEpoch : 'Number of epochs.'
• --num_negatives' : 'Number of negative samples.
• --batch_size' : 'Batch size.'
• --rand_seed' : 'Random seed.'
• --cuda : 'Speficy GPU number'
• --early_stop : 'Early stop iteration.'

Reproducing the qualitative experiments

• Tables 4 and 6
• Table 5 and 7, Figures 4 and 5
  • Ciao dataset
  • Amazon C&A dataset

Cite (Bibtex)

• If you find TransCF useful in your research, please cite the following paper:
  • Park, Chanyoung, et al. "Collaborative translational metric learning." 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018.
  • Bibtex

@inproceedings{park2018collaborative,
  title={Collaborative translational metric learning},
  author={Park, Chanyoung and Kim, Donghyun and Xie, Xing and Yu, Hwanjo},
  booktitle={2018 IEEE International Conference on Data Mining (ICDM)},
  pages={367--376},
  year={2018},
  organization={IEEE}
}