/Recurrent_Tensor_Factorization

This is the implementation of our paper "Recurrent Tensor Factorization for Time-aware Service Recommendation"

Primary LanguagePython

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