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+# PPGN
+
+Codes for CIKM 2019 paper [Cross-Domain Recommendation via Preference Propagation GraphNet](https://doi.org/10.1145/3357384.3358166).
+
+## Citation
+
+Please cite our paper if you find this code useful for your research:
+
+```
+@inproceedings{cikm19:ppgn,
+  author    = {Cheng Zhao and
+               Chenliang Li and
+               Cong Fu},
+  title     = {Cross-Domain Recommendation via Preference Propagation GraphNet},
+  booktitle = {The 28th ACM International Conference on Information and Knowledge Management, {CIKM} 2019, Beijing, China,
+               November 3-7, 2019},
+  pages     = {2165--2168},
+  year      = {2019}
+}
+```
+
+## Requirement
+* Python 3.6
+* Tensorflow 1.10.0
+* Numpy
+* Pandas
+* Scipy
+
+
+## Files in the folder
+- `data/`
+  - `data_prepare.py`: constructing cross-domain scenario from overlapping users;
+  - `dataset.py`: defining the class of cross-domain dataset;
+- `src/`
+  - `main.py`: the main function (including the configurations);
+  - `model.py`: the detail implementation of PPGN;
+  - `train.py`: training and evaluation;
+- `utils`
+  - `metrics.py`: evaluation metrics.
+
+
+## Running the code
+1. Download the original data from [Amazon-5core](http://jmcauley.ucsd.edu/data/amazon/index.html), 
+choose two relevant categories (*e.g.*, Books, Movies and TV) and put them under the same directory in data/.
+
+2. run python data_prepare.py.
+
+3. run python main.py.
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