Zero-Inflated Exponential Family Embeddings

Introduction

This repo implements the embedding models in the 2017 ICML paper "Zero-Inflated Exponential Family Embeddings"

Zero-Inflated Exponential Family Embedding (ZIE) model is designed to learn embedding vectors of items on sparse data. It uses zero-inflated distributions as the conditional in the embedding model. Fitting a ZIE naturally downweights the zeros and dampens their influence on the model. Please see the details in the paper.

Running the code

python demo.py

Note: this repo does not contain any data -- it only use some random data to show how to use the code. The code requires numpy, scipy, and tensorflow.

Contact and cite

If you have any questions, please contact the Li-Ping Liu (liping.liulp at gmail).

If you have used the code in your work, please cite:

@inproceedings{zie17,
title = {Zero-Inflated Exponential Family Embeddings},
author = {Li-Ping Liu and David M. Blei},
booktitle ={Proceedings of the 34th International Conference on Machine Learning},
pages = {2140--2148},
year = {2017},
editor = {Doina Precup and Yee Whye Teh},
volume = {70},
series = {Proceedings of Machine Learning Research},
address = {International Convention Centre, Sydney, Australia},
month = {06--11 Aug},
publisher ={PMLR}
}