Tajuddeen / PACE2017

A step-by-step Keras implementation of PACE (Preference And Context Embedding) described in our KDD 2017 paper.

Implementation of PACE, KDD 2017.

Please cite the following work if you find the code useful.

@inproceedings{yang2017bridging,
	Author = {Yang, Carl and Bai, Lanxiao and Zhang, Chao and Yuan, Quan and Han, Jiawei},
	Booktitle = {KDD},
	Title = {Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation},
	Year = {2017}
}

Contact: Carl Yang ([e-mail])

Usage:

To run the code, you need to have Python3 and iPython Notebook installed.

• Visit https://snap.stanford.edu/data/loc-gowalla.html or https://www.yelp.com/dataset/challenge to download the Gowalla or Yelp datasets. Please refer to dataset.py the paper for data preprocessing.
• Start iPython Notebook Server ipython3 notebook and sequentially run cells in train.ipynb

If you are using remote machine, you can:

• Start iPython Notebook Server on remote machine: ipython notebook --no-browser --port=8889
• Redirect ssh connection to localhost ssh -N -f -L localhost:8880:localhost:8889 <user>@<host>
• Open browser and go to <user>@<host>:8880