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README.md

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 (yangji9181@gmail.com)

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

About

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

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