# RSAlgorithms

**Repository Path**: hwang_zc/RSAlgorithms

## Basic Information

- **Project Name**: RSAlgorithms
- **Description**: Some algorithms about traditional and social recommendation.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-09-20
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

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

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

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

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

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

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

Ebesu, Travis, Bin Shen, and Yi Fang. "Collaborative Memory Network for Recommendation Systems." SIGIR, 2018. [code](https://github.com/tebesu/CollaborativeMemoryNetwork)

## 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)

```

## 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 ZhiHu: [Honglei Zhang](https://www.zhihu.com/people/hongleizhang)

My Gmail: hongleizhang1993@gmail.com