GRU4Rec_TensorFlow

This is the TensorFlow implementation of GRu4Rec, which is descibed in ICLR'2016 paper "Session-based Recommendations With Recurrent Neural Networks". See paper: http://arxiv.org/abs/1511.06939.

• Codes under gru4rec_BPTT use BPTT to train RNNs. This usually performs better than original gru4rec.
• Codes under gru4rec_BP use back propagation only to train RNNs, which is the optimization method adopted in original paper.

External Link

We develop a general sequence-aware recommendation library at here.

Requirements

Python: 2.7

Pandas < 0.17

Numpy 1.12.1 or later

TensorFlow: 0.12.1

Usage

Train/Test file should consists of three columns:

 First column: SessionId  
 Second column: ItemId  
 Third column: Timestamps

To train a model with default parameter settings:

$ python main.py    

Other optional parameters include:   
 --layer: Number of GRU layers. Default is 1.  
 --size: Number of hidden units in GRU model. Default is 100.   
 --epoch: Runing epochs. Default is 3.   
 --lr : Initial learning rate. Default is 0.001.   
 --train: Specify whether training(1) or evaluating(0). Default is 1.   
 --hidden_act: Activation function used in GRU units. Default is tanh.   
 --final_act: Final activation function. Default is softmax.    
 --loss: Loss functions, cross-entropy, bpr or top1 loss. Default is cross-entropy.      
 --dropout: Dropout rate. Default is 0.5.

To evaluate a trained model:

$ python main.py --train 0

One optional parameter is:    
 --test: Specify which saved model to evaluate(only used when --train is 0). Default is 2.

Acknowledgement

This repository refers a lot to the original Theano implementation.