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Codes for paper:
Wenhui Yu, Xiao Lin, Junfeng Ge, Wenwu Ou, and Zheng Qin. 2020. Semisupervised Collaborative Filtering by Text-enhanced Domain Adaptation. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), August 23-27, 2020, Virtual Event, CA, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3394486.3403264

This project is for our model TMN, TCF, and TDAR.

* Environment:
  Python 3.6.8 :: Anaconda, Inc.
* Libraries:
  tensorflow 1.12.0
  numpy 1.16.4
  pandas 0.18.1
  openpyxl 2.3.2
  xlrd 1.0.0
  xlutils 2.0.0

Please follow the steps below:
1. Download datasets and text features:
   https://drive.google.com/open?id=1Kk2S3JtEf9LHKpMPbrXL2KxbnVzl6f0f
   https://pan.baidu.com/s/1jVQV5Vyin9rlxWT0xb57Dg (password: 76af)
You can choose one of these two URLs for downloading. Download and unzip TDAR_dataset.zip and use it to replace the folder dataset in our project.

2. MF, TMN, and TCF are in folder 2.review2vec.
   Running file _main.py in 2.review2vec.
   If you want to save the embeddings, set IF_SAVE_EMB in params.py as 1 (set _SAVE_EMB as 1 only if you are sure to save the embeddings, or the previous embeddings will be overwritten).
3. TDAR is in folder 3.tranfer_rec.
   Running file _main.py in 3.tranfer_rec.
4. Process the results with result_collection.


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code for KDD paper Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

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