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recurrent_tensor_factorization's Introduction

Recurrent Tensor Factorization

This is the implementation of our paper:

Yiwen Zhang, Chunhui Yin, Zhihui Lu*, Dengcheng Yan, Meikang Qiu, Qifeng Tang. Recurrent Tensor Factorization for time-aware Service Recommendation, Applied Soft Computing 85 (2019) 105762. (SCI)

Author: Chun-hui Yin

Affiliate: Big Data and Cloud Service Lab, Anhui University

Last updated: 2019/10/05

Please cite our paper if you use our codes. Thanks!

Environment Requirement

This code can be run at following requirement but not limit to:

  • python = 3.6.6
  • tensorflow-gpu = 1.7.0
  • keras = 2.0.9
  • pandas = 0.23.4
  • numpy = 1.14.0
  • scikit-learn = 0.21
  • other installation dependencies required above

Example of Usage

>>>python RTF.py

>>>python GTF.py

>>>python PGRU.py

Dataset

  • To simulate the real-world situation, we sparse the original matrix at 4 densities and generate instances for training
  • Here we provide the preprocessed real-world dataset WS-Dream (dataset#2)
  • The original WS-DREAM dataset can be downloaded at InplusLab

Note

  • Experiments can be run on multi-core CPUs at 6 densities by turning on parallel mode

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recurrent_tensor_factorization's Issues

TypeError: list indices must be integers or slices, not tuple

When I run the PGRU model, the following error will appear。
The operating environment has been set in the readme。

error info:

Traceback (most recent call last):
File "D:/Desktop/Recurrent_Tensor_Factorization/PGRU.py", line 211, in
main()
File "D:/Desktop/Recurrent_Tensor_Factorization/PGRU.py", line 58, in main
model = PGRU(args, density)
File "D:/Desktop/Recurrent_Tensor_Factorization/PGRU.py", line 97, in init
self.model = self.load_model()
File "D:/Desktop/Recurrent_Tensor_Factorization/PGRU.py", line 142, in load_model
self.dropLayers)
File "D:/Desktop/Recurrent_Tensor_Factorization/PGRU.py", line 189, in build_model
gru_vector = layers([gru_vector, time_embedding])
File "D:\Desktop\Recurrent_Tensor_Factorization\venv\lib\site-packages\keras\layers\recurrent.py", line 519, in call
output = super(RNN, self).call(full_input, **kwargs)
File "D:\Desktop\Recurrent_Tensor_Factorization\venv\lib\site-packages\keras\engine\topology.py", line 603, in call
output = self.call(inputs, **kwargs)
File "D:\Desktop\Recurrent_Tensor_Factorization\venv\lib\site-packages\keras\layers\recurrent.py", line 1503, in call
self.cell._generate_dropout_mask(inputs, training=training)
File "D:\Desktop\Recurrent_Tensor_Factorization\venv\lib\site-packages\keras\layers\recurrent.py", line 1268, in _generate_dropout_mask
ones = K.ones_like(K.squeeze(inputs[:, 0:1, :], axis=1))
TypeError: list indices must be integers or slices, not tuple

Error: failed to fetch some objects

Hi, I'm interested in reproducing your code. SO I have downloaded the repository, After that, I've tried downloading the data using git lfs fetch but I face this problem:
fetch: Fetching reference refs/heads/master batch response: This repository is over its data quota. Account responsible for LFS bandwidth should purchase more data packs to restore access. error: failed to fetch some objects from 'https://github.com/yinchunhui-ahu/Recurrent_Tensor_Factorization.git/info/lfs'
Do you have any idea how to solve This?. Thank you in advance.

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