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Implementation of paper Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks
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README.md

README.md

NeuACF

This is an implementation of paper (Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks).

Please refer our paper if you use this code and the bibtex of this paper is:

@inproceedings{han2018aspect,
   title={Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks.},
   author="\textbf{Han, Xiaotian} and Shi, Chuan and Wang, Senzhang and Philip, S Yu and Song, Li",
   booktitle={IJCAI},
   pages={3393--3399},
   year={2018}
 }

Requirements

  • Python 3.6
  • Tensorflow 1.2.1
  • docopt 0.6.2
  • numpy 1.13.3
  • sklearn 0.18.1
  • pandas 0.20.1
  • scipy 1.0.0

How to Run

  1. unzip dataset.7z
  2. Compute the aspect-level similarity matrix with the matlab code
  3. Run the model with the python code acf.py

example:

 python ./acf.py ../dataset/amazon/ amovie --mat "U.UIU,I.IUI,U.UICIU,I.ICI" --epochs 40 --last_layer_size 64 --batch_size 1024 --num_of_neg 10 --learn_rate 0.00005 --num_of_layers 2 --mat_select median

Parameters

Parameter Note
--mat sim_mat [default: ""]
--epochs Embedding size [default: 40]
--last_layer_size The number of iterations [default: 64]
--num_of_layers The number of layers [default: 2]
--num_of_neg The number of negs [default: 2]
--learn_rate The learn_rate [default: 0.00005]
--batch_size batch_size [default: 1024]
--mat_select mat select type [default: median]
--merge batch_size [default: attention]

Link

For more information, visit the webpage http://www.shichuan.org

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