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.

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.

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}
}