DBML

This is the python implementation of DBML model for paper "Dynamic Bayesian Metric Learning for Personalized Product Search (Teng Xiao^*, Jiaxin Ren^*, Shangsong Liang and Zaiqiao Meng)"

Introduction

DBML is a novel probabilistic metric learning approach that is able to avoid the contradicts, keep the triangle inequality in the latent space, and correctly utilize implicit feedbacks. The inferred dynamic semantic representations of entities collaboratively inferred in a unified form by our DBML can benefit not only for improving personalized product search, but also for capturing the affinities between users, products and words. Please refer to the paper for further details.

Requirements

• pytorch(0.4 or later)
• nltk
• tqdm
• dateutil
• gzip

Datasets

Download Amazon review datasets from http://jmcauley.ucsd.edu/data/amazon/ (In our paper, we used 5-core review data and metedata).

Run

Run train_offline.py for offline model.

Where the data_name can be 'Electronics', 'Cell Phones and Accessories', 'Clothing, Shoes and Jewelry' or 'Toys and Games'.

Citation

if you want to use our codes in your research, please cite:

@inproceedings{dbml/cikm/2019,
  title={Dynamic Bayesian Metric Learning for Personalized Product Search},
  author={Xiao, Teng and Ren, Jiaxin and Liang, Shangsong and Meng, Zaiqiao},
  booktitle={Proceedings of the  28th ACM International Conference on Information and Knowledge Management},
  year={2019},
  organization={ACM}
}