SREPS

Implementation of "Social Recommendation with an Essential Preference Space".

If you find this method helpful for your research, please cite this paper:

@inproceedings{LiuZWHG18,
  author    = {Chun{-}Yi Liu and
               Chuan Zhou and
               Jia Wu and
               Yue Hu and
               Li Guo},
  title     = {Social Recommendation with an Essential Preference Space},
  booktitle = {Proceedings of the Thirty-Second {AAAI} Conference on Artificial Intelligence (AAAI-18)},
  pages     = {346--353},
  year      = {2018}
}

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Requirement

• python >= 3.4
• numpy
• tqdm

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Dataset

The dataset used in this paper can be obtained from the original papers.

Dataset	Paper
FilmTrust	G. Guo, J. Zhang, and N. Yorke-Smith, “A novel bayesian similarity measure for recommender systems,” in Proc of (IJCAI), 2013, pp. 2619–2625.
Flixster	M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks,” in Proc. of RecSys, 2010, pp. 135–142.
Epinions	J. Tang, H. Gao, and H. Liu, “mtrust: discerning multi-faceted trust in a connected world,” in Proc. of WSDM, 2012, pp. 93–102.
Ciao	J. Tang, H. Gao, H. Liu, and A. D. Sarma, “etrust: understanding trust evolution in an online world,” in Porc. of KDD, 2012, pp. 253–261.

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How to use

Step 1. Format The Dataset

The four datasets are different in the input files on the social links. We first format them into a same format by:

python main.py --mode=prepro --dataset=filmtrust --data_input=xxxx --data_output=yyy

• The dataset is the name of dataset, and the filmtrust, flixster, epinions and ciao are supported.

• The data_input is the path (a folder) of the input file. The preprocessing will read the file with the predefined name in the path. For filmtrust, epinions and ciao, the rating file (i.e. ratings.txt) and the trust link file (trust.txt) must exist. And for flixster, the rating file (i.e. ratings.txt) and link file (links.txt) must exist. The predefined name can be modified in the preprocess.py.

• The data_out is the path (a folder) to save the formated files. The rating file is saved as ${data_out}/ratings.total, and the social link file is saved as ${data_out}/links.total.

Step 2: Train The Model

In the first run, we can train our model by:

python main.py --mode=run --rating_file=xxxx --link_file=yyy --train_file=mmm --dev_file=nnn

The input ratings will first be divided into a training dataset and a evaluation dataset. And they are saved in the ${train_file} and ${dev_file} respectively.

After the data division, the training will start. In the training process, we can observe the performance on the evaluation set.

For the non-first run, we can simply start with

python main.py --mode=run --train_file=mmm --dev_file=nnn

The train_file and dev_file are loaded, and the model will train with the ${train_file}. The performance on the evaluation set can also be observed.

In the training process, the model will automatically save in the save_path every save_step. Note only one model are saved, and the users should monitor the performance and manually conduct the early stopping.

Note: other hyper-parameters of the model and the training process can be found in the main.py.

Step 3: Evaluation.

Given the evaluation dataset, we can conduct the evaluation with:

python main.py --mode=eval --dev_file=xxx --save_path=yyy

The model will loaded from the ${save_path}, and the performance of the evaluation set ${dev_file} will be reported.

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Disclaimer

If you find any bugs, please report them to me.