https://github.com/hongleizhang/rsalgorithms

RSTutorials: A Curated List of Algorithms about Traditional and Social Recommender System.
https://github.com/hongleizhang/rsalgorithms

recommender-system social-recommendation traditional-recommendation

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RSTutorials: A Curated List of Algorithms about Traditional and Social Recommender System.

• Host: GitHub
• URL: https://github.com/hongleizhang/rsalgorithms
• Owner: hongleizhang
• Created: 2017-09-05T07:49:34.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2024-04-05T14:10:37.000Z (about 1 year ago)
• Last Synced: 2025-04-04T00:09:42.420Z (2 months ago)
• Topics: recommender-system, social-recommendation, traditional-recommendation
• Language: Python
• Homepage:
• Size: 5.35 MB
• Stars: 709
• Watchers: 29
• Forks: 204
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

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README

        
# Recommender System Suits： An open source toolkit for recommender system

This repository provides a set of classical **traditional recommendation methods** which make predictions only using rating data and **social recommendation methods** which utilize trust/social information in order to alleviate the sparsity of ratings data. Besides, we have collected some classical methods implemented by others for your convenience.

## Traditional recommendation

* **UserCF**[Resnick et al. 1994]

Resnick, Paul, et al. "GroupLens: an open architecture for collaborative filtering of netnews." Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 1994.

* **ItemCF**[Sarwar et al. 2001]

Sarwar, Badrul, et al. "Item-based collaborative filtering recommendation algorithms." Proceedings of the 10th international conference on World Wide Web. ACM, 2001.

* **FunkSVD**[Simon Funk. 2006]

http://sifter.org/~simon/journal/20061211.html

* **PMF**[Salakhutdinov. 2008]

Mnih, Andriy, and Ruslan R. Salakhutdinov. "Probabilistic matrix factorization." Advances in neural information processing systems (2008): 1257-1264.

* **IntegSVD**[Koren et al. 2008]

Koren, Yehuda. "Factorization meets the neighborhood: a multifaceted collaborative filtering model." Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008.

* **BiasSVD**[Koren et al. 2009]

Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009).

* **SVD++**[Koren et al. 2010]

Koren, Yehuda. "Factor in the neighbors: Scalable and accurate collaborative filtering." ACM Transactions on Knowledge Discovery from Data (TKDD) 4.1 (2010): 1.

## Social recommendation

* **SocialRec**[Ma et al. 2008]

Ma, Hao, et al. "Sorec: social recommendation using probabilistic matrix factorization." Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008.

* **RSTE**[Ma et al. 2009]

Ma, Hao, Irwin King, and Michael R. Lyu. "Learning to recommend with social trust ensemble." Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, 2009.

* **TrustWalker**[Jamali and Ester. 2009]

Jamali, Mohsen, and Martin Ester. "Trustwalker: a random walk model for combining trust-based and item-based recommendation." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009.

* **SocialMF**[Jamali and Ester 2010]

Jamali, Mohsen, and Martin Ester. "A matrix factorization technique with trust propagation for recommendation in social networks." Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.

* **SocialReg**[Ma et al. 2011]

Ma, Hao, et al. "Recommender systems with social regularization." Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.

* **TrustSVD**[Guo et al. 2015]

Guo, Guibing, Jie Zhang, and Neil Yorke-Smith. "TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings." AAAI. Vol. 15. 2015.

* **CUNE**[Zhang et al. 2017]

Chuxu Zhang, Lu Yu, Yan Wang, Chirag Shah, Xiangliang Zhang. "Collaborative User Network Embedding for Social Recommender Systems." SDM, 2017.

## RSAlgorithms implemented by Others

Sedhain et al. "Autorec: Autoencoders meet collaborative filtering." WWW, 2015. [code](https://github.com/gtshs2/Autorec)

Kim et al. "Convolutional matrix factorization for document context-aware recommendation." RecSys, 2016. [code](https://github.com/cartopy/ConvMF)

Liang et al. "Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence." RecSys, 2016. [code](https://github.com/dawenl/cofactor)

He et al. "Fast matrix factorization for online recommendation with implicit feedback." SIGIR, 2016. [code](https://github.com/hexiangnan/sigir16-eals)

Quadrana et al. "Personalizing session-based recommendations with hierarchical recurrent neural networks." RecSys, 2017. [code](https://github.com/mquad/hgru4rec)

He et al. "Neural collaborative filtering." WWW, 2017. [code](https://github.com/hexiangnan/neural_collaborative_filtering)

Ebesu et al. "Collaborative Memory Network for Recommendation Systems." SIGIR, 2018. [code](https://github.com/tebesu/CollaborativeMemoryNetwork)

Fan et al. "Graph Neural Networks for Social Recommendation." WWW, 2019. [code](https://github.com/Wang-Shuo/GraphRec_PyTorch)

Chong et al. "Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation." AAAI, 2020. [code](https://github.com/chenchongthu/EHCF)

## Requirements

* numpy==1.14.2

* scipy==1.0.1

* pandas==0.22.0

* matplotlib==2.2.2

## Code Structure

The structure of our project is presented in a tree form as follows:

```

Recommender System  # the root of project

│   README.md

│   __init__.py

│   .gitignore

|

└───configx  # configurate the global parameters and hyper parameters

│   │   configx.py   

|   │   

└───data  # store the rating and social data

│   │   ft_ratings.txt

|   │   ft_trust.txt

|   |

│   └───cv  # cross validation data

│       │   ft-0.txt

│       │   ft-1.txt

│       │   ft-2.txt

│       │   ft-3.txt

│       │   ft-4.txt

|       |

└───metrics  # the metrics to measure the prediction accuracy for rating prediction task

│   │   metric.py

|   |

└───model  # the set of methods of tranditional and social recommendation

│   │   bias_svd.py

│   │   funk_svd.py

│   │   pmf.py

│   │   integ_svd.py

|   |   item_cf.py

|   |   item_cf_big.py

|   |   mf.py

|   |   social_mf.py

|   |   social_rec.py

|   |   social_reg.py

|   |   social_rste.py

|   |   svd++.py

|   |   trust_svd.py

|   |   trust_walker.py

|   |   user_cf.py

|   |

└───reader  # data generator for rating and social data

│   │   rating.py

│   │   trust.py

|   |

└───utility  # other commonly used tools

    │   cross_validation.py

    │   data_prepro.py

    │   data_statistics.py

    │   draw_figure.py

    │   matrix.py

    │   similarity.py

    │   tools.py

    │   util.py

```

## Parameters Settings

If you want to change the default hyparameters, you can set it in `configx.py`. The meanings of the hyparameters is as follows:

#### Dataset Parameters

`dataset_name`: the short name of dataset, the default value is `ft`.

`k_fold_num`: the num of cross validation, the default value is `5`.

`rating_path `: the path of raw ratings data file, the default value is `../data/ft_ratings.txt`.

`rating_cv_path`: the cross validation path of ratings data, the default value is `../data/cv/`.

`trust_path`: the path of raw trust data file, the default value is `../data/ft_trust.txt`.

`sep`: the separator of rating and trust data in triple tuple, the default value is ` `.

`random_state`: the seed of random number, the default value is `0`.

`size`: the ratio of train set, the default value is `0.8`.

`min_val`: the minimum rating value, the default value is `0.5`.

`max_val`: the maximum rating value, the default value is `4.0`.

#### Model HyperParameters

`coldUserRating`: the number of ratings a cold start user rated on items, the default value is `5`.

`factor`: the size of latent dimension for user and item, the default value is `10`.

`threshold`: the threshold value of model training, the default value is `1e-4`.

`lr`: the learning rate, the default value is `0.01`.

`maxIter`: the maximum number of iterations, the default value is `100`.

`lambdaP`: the parameter of user regularizer, the default value is `0.001`.

`lambdaQ`: the parameter of item regularizer, the default value is `0.001`.

`gamma`: momentum coefficient, the default value is `0.9`.

`isEarlyStopping`: early stopping flag, the default value is `false`.

#### Output Parameters

`result_path`: the main directory of results, the default value is `../results/`.

`model_path`: the directory of well-trained variables, the default value is `../results/model/`.

`result_log_path`: the directory of logs when training models, the default value is `../results/log/`.

## Usage

Next, I will take `pmf` as an example to introduce how to execute our code.

First, we should split our rating data into several parts for training, testing and cross validation.

```

from utility.cross_validation import split_5_folds

from configx.configx import ConfigX

if __name__ == "__main__":

    configx = ConfigX()

    configx.k_fold_num = 5 

    configx.rating_path = "../data/ft_ratings.txt"

    configx.rating_cv_path = "../data/cv/"

    

    split_5_folds(configx)

```

Next, we open the `pmf.py` file in `model` folder, and configure the hyperparameters for training and execute the following code：

```

if __name__ == '__main__':

    rmses = []

    maes = []

    bmf = FunkSVDwithR()

    for i in range(bmf.config.k_fold_num):

        bmf.train_model(i)

        rmse, mae = bmf.predict_model()

        print("current best rmse is %0.5f, mae is %0.5f" % (rmse, mae))

        rmses.append(rmse)

        maes.append(mae)

    rmse_avg = sum(rmses) / 5

    mae_avg = sum(maes) / 5

    print("the rmses are %s" % rmses)

    print("the maes are %s" % maes)

    print("the average of rmses is %s " % rmse_avg)

    print("the average of maes is %s " % mae_avg)

```

## Citing

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

    @inproceedings{pricai2018sotricf,

        title="Social Collaborative Filtering Ensemble",

        author="Zhang, Honglei and Liu, Gangdu and Wu, Jun",

        booktitle="PRICAI",

        pages="1005--1017"

        year="2018",

    }

    @inproceedings{ijcnn2019MFDGE,

        title={Integrating dual user network embedding with matrix factorization for social recommender systems},

        author={Chen, Liying and Zhang, Honglei and Wu, Jun},

        booktitle={IJCNN},

        pages={1--8},

        year={2019},

    }

## RSPapers

Recently, we have launched an open source project [**RSPapers**](https://github.com/hongleizhang/RSPapers), which includes some classical **Surveys**, **Classical Recommender System**, **Social Recommender System**, **Deep Learning based Recommender System**, **Cold Start Problem in Recommender System** and **POI Recommender System**. 

## Acknowledgements

Specially summerize the Traditional and Social recommendations for you, and if you have any questions, please contact me generously. Last but not least, I sincerely look forward to working with you to contribute it.

Greatly thank @**yunzhan2014** for making contributions to it.

My Gmail: [email protected]