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Path conditioned Graph Convolutional Network

pgcn 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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#  Path conditioned Graph Convolutional Network (PGCN)

**Key words**: graph convolution, recommender system, recommendation, vertex representation, interaction sequence

This is our implementation for the paper:

Yanan Xu, Yanmin Zhu, Yanyan Shen, and Jiadi Yu (2019). **Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation.** In Proceedings of IJCAI'19, Macao, China, August 10-16, 2019.

Two graph convolutional neural networks: PGCN-C and PGCN-A.

PGCN-C uses static transition probability as the weights of neighbors in convolution. 
PGCN-A utilizes attention mechanism to calculate weights of neighbors.

**Please cite our IJCAI'19 paper if you use our codes. Thanks!**

Author: Yanan Xu

## Main Idea of This Paper

### 1. Construct three heterogeneous graphs with user interaction sequences.

![Illustration of converting interaction sequences to heterogeneous graphs.](https://raw.githubusercontent.com/ynxu15/pgcn/master/fig/heteroGraph.jpg)

The user-item graph, item-item graph, user-subseq graph denote user preference, item dependency, and user similarity on behaviors, respectively.

### 2. Train the PGCN with the graphs.

![ Structure of PGCN. V and E are one-hot representation and embedding matrices of vertexes, respectively. Zs and Hs are feature
tensors of virtual vertexes after neighbor pooling and real vertexes after convolution, respectively. Ws are weight matrices](fig/pgcnStructure.jpg)

### 3. Deal with a varying number of neighbors for each vertex in the graphs.

![Illustration of pooling and convolution operations (dashed line box) on an image or a graph.](https://raw.githubusercontent.com/ynxu15/pgcn/master/fig/poolConv.jpg)

(1) Cluster neighbors of each vertex according to their distance and path type.

(2) Perform neighbor pooling operation on each cluster and generate a virtual vertex.

We provide two kinds of neighbor pooling operation: using transition probability (PGCN-C) or attention mechanism (PGCN-A).

(3) Apply convolution to virtual vertexes and generate representations for each user and item.

## Environment Settings
We use TensorFlow to implement our model. 
- TensorFlow version:  '1.4.0'

## Structure of This Project

## Example to run the codes.
```
$ python pgcnaMain.py
```
or 
```
$ python pgcncMain.py
```

### Dataset
We provide two processed datasets: [MovieLens-100k](https://grouplens.org/datasets/movielens/) and [Retailrocket](https://www.kaggle.com/retailrocket/ecommerce-dataset).

The processed data are stored with pickle.

Last Update Date: May 30, 2019

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