Coder-Yu / Recq
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RecQ: A Python Framework for Recommender Systems (TensorFlow Based)
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RecQ
Introduction
Founder: @Coder-Yu
Main Contributors: @DouTong @Niki666 @HuXiLiFeng @BigPowerZ @flyxu
RecQ is a Python library for recommender systems (Supported by Python 2.7.x and Tensorflow) in which a number of influential and newly state-of-the-art recommendation models are implemented. To run RecQ easily, see requirements.txt to install all dependencies.
What's New
16/01/2021 - MHCN proposed in our WWW'21 paper has been added. 22/09/2020 - DiffNet proposed in SIGIR'19 has been added. 19/09/2020 - DHCF proposed in KDD'20 has been added for comparison, althought it doesn't work very well. 29/07/2020 - ESRF proposed in my TKDE paper has been added. 23/07/2020 - LightGCN proposed in SIGIR'20 has been added. 17/09/2019 - NGCF proposed in SIGIR'19 has been added. 13/08/2019 - RSGAN proposed in ICDM'19 has been added. 09/08/2019 - Our paper is accepted as full research paper by ICDM'19. 02/20/2019 - IRGAN proposed in SIGIR'17 has been added (tuning...) 02/12/2019 - CFGAN proposed in CIKM'18 has been added. 02/04/2019 - NeuMF proposed in WWW'17 has been added. 10/09/2018 - An Adversarial training based Model: APR has been implemented. 10/02/2018 - Two deep models: DMF CDAE have been implemented. 07/12/2018 - Algorithms supported by TensorFlow: BasicMF, PMF, SVD, EE (Implementing...)
Architecture of RecQ
Features
- Cross-platform: as a Python software, RecQ can be easily deployed and executed in any platforms, including MS Windows, Linux and Mac OS.
- Fast execution: RecQ is based on the fast scientific computing libraries such as Numpy and some light common data structures, which make it run much faster than other libraries based on Python.
- Easy configuration: RecQ configs recommenders using a configuration file.
- Easy expansion: RecQ provides a set of well-designed recommendation interfaces by which new algorithms can be easily implemented.
How to Run it
- 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the algorithm you want to run)
- 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure it
Essential Options
| Entry | Example | Description |
|---|---|---|
| ratings | D:/MovieLens/100K.txt | Set the path to input dataset. Format: each row separated by empty, tab or comma symbol. |
| social | D:/MovieLens/trusts.txt | Set the path to input social dataset. Format: each row separated by empty, tab or comma symbol. |
| ratings.setup | -columns 0 1 2 | -columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data |
| social.setup | -columns 0 1 2 | -columns: (trustor, trustee, weight) columns of social data are used;
-header: to skip the first head line when reading data |
| recommender | UserKNN/ItemKNN/SlopeOne/etc. | Set the recommender to use. |
| evaluation.setup | -testSet ../dataset/testset.txt | Main option: -testSet, -ap, -cv
-testSet path/to/test/file (need to specify the test set manually)
-ap ratio (ap means that the ratings are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)
-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
Secondary option:-b, -p, -cold -b val （binarizing the rating values. Ratings equal or greater than val will be changed into 1, and ratings lower than val will be changed into 0. e.g. -b 3.0） -p (if this option is added, the cross validation wll be executed parallelly, otherwise executed one by one) -tf (model training would be conducted on TensorFlow if TensorFlow has been installed) -cold threshold (evaluation on cold-start users, users in training set with ratings more than threshold will be removed from the test set) |
| item.ranking | off -topN -1 | Main option: whether to do item ranking -topN N1,N2,N3...: the length of the recommendation list. *RecQ can generate multiple evaluation results for different N at the same time |
| output.setup | on -dir ./Results/ | Main option: whether to output recommendation results -dir path: the directory path of output results. |
Memory-based Options
| similarity | pcc/cos | Set the similarity method to use. Options: PCC, COS; |
| num.shrinkage | 25 | Set the shrinkage parameter to devalue similarity value. -1: to disable simialrity shrinkage. |
| num.neighbors | 30 | Set the number of neighbors used for KNN-based algorithms such as UserKNN, ItemKNN. |
Model-based Options
| num.factors | 5/10/20/number | Set the number of latent factors |
| num.max.iter | 100/200/number | Set the maximum number of iterations for iterative recommendation algorithms. |
| learnRate | -init 0.01 -max 1 | -init initial learning rate for iterative recommendation algorithms; -max: maximum learning rate (default 1); |
| reg.lambda | -u 0.05 -i 0.05 -b 0.1 -s 0.1 | -u: user regularizaiton; -i: item regularization; -b: bias regularizaiton; -s: social regularization |
How to extend it
- 1.Make your new algorithm generalize the proper base class.
- 2.Rewrite some of the following functions as needed.
- printAlgorConfig()
- initModel()
- buildModel()
- saveModel()
- loadModel()
- predict()
Algorithms Implemented
Note: We use SGD to obtain the local minimum. So, there have some differences between the original papers and the code in terms of fomula presentation. If you have problems in understanding the code, please open an issue to ask for help. We can guarantee that all the implementations are carefully reviewed and tested.
Any suggestions are welcome. We will make efforts to improve RecQ.
| Rating prediction | Paper |
|---|---|
| SlopeOne | Lemire and Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, SDM'05. |
| PMF | Salakhutdinov and Mnih, Probabilistic Matrix Factorization, NIPS'08. |
| SoRec | Ma et al., SoRec: Social Recommendation Using Probabilistic Matrix Factorization, SIGIR'08. |
| SVD++ | Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, SIGKDD'08. |
| RSTE | Ma et al., Learning to Recommend with Social Trust Ensemble, SIGIR'09. |
| SVD | Y. Koren, Collaborative Filtering with Temporal Dynamics, SIGKDD'09. |
| SocialMF | Jamali and Ester, A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks, RecSys'10. |
| EE | Khoshneshin et al., Collaborative Filtering via Euclidean Embedding, RecSys'10. |
| SoReg | Ma et al., Recommender systems with social regularization, WSDM'11. |
| LOCABAL | Tang, Jiliang, et al. Exploiting local and global social context for recommendation, AAAI'13. |
| SREE | Li et al., Social Recommendation Using Euclidean embedding, IJCNN'17. |
| CUNE-MF | Zhang et al., Collaborative User Network Embedding for Social Recommender Systems, SDM'17. |
| Item Ranking | Paper |
|---|---|
| BPR | Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback, UAI'09. |
| WRMF | Yifan Hu et al.Collaborative Filtering for Implicit Feedback Datasets, KDD'09. |
| SBPR | Zhao et al., Leveraing Social Connections to Improve Personalized Ranking for Collaborative Filtering, CIKM'14 |
| ExpoMF | Liang et al., Modeling User Exposure in Recommendation, WWW''16. |
| CoFactor | Liang et al., Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence, RecSys'16. |
| TBPR | Wang et al. Social Recommendation with Strong and Weak Ties, CIKM'16'. |
| CDAE | Wu et al., Collaborative Denoising Auto-Encoders for Top-N Recommender Systems, WSDM'16'. |
| DMF | Xue et al., Deep Matrix Factorization Models for Recommender Systems, IJCAI'17'. |
| NeuMF | He et al. Neural Collaborative Filtering, WWW'17. |
| CUNE-BPR | Zhang et al., Collaborative User Network Embedding for Social Recommender Systems, SDM'17'. |
| IRGAN | Wang et al., IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models, SIGIR'17'. |
| SERec | Wang et al., Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation, AAAI'18'. |
| APR | He et al., Adversarial Personalized Ranking for Recommendation, SIGIR'18'. |
| IF-BPR | Yu et al. Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation, CIKM'18'. |
| CFGAN | Chae et al. CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks, CIKM'18. |
| NGCF | Wang et al. Neural Graph Collaborative Filtering, SIGIR'19'. |
| DiffNet | Wu et al. A Neural Influence Diffusion Model for Social Recommendation, SIGIR'19'. |
| RSGAN | Yu et al. Generating Reliable Friends via Adversarial Learning to Improve Social Recommendation, ICDM'19'. |
| LightGCN | He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, SIGIR'20. |
| DHCF | Ji et al. Dual Channel Hypergraph Collaborative Filtering, KDD'20. |
| ESRF | Yu et al. Enhancing Social Recommendation with Adversarial Graph Convlutional Networks, TKDE'20. |
| MHCN | Yu et al. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation, WWW'21. |
Category
| Generic Recommenders | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| UserKNN | ItemKNN | BasicMF | SlopeOne | SVD | PMF | SVD++ | EE | BPR | WRMF |
| ExpoMF | NGCF | LightGCN | IRGAN | CFGAN | |||||
| Social Recommenders | |||||||||
| RSTE | SoRec | SoReg | SocialMF | SBPR | SREE | LOCABAL | SocialFD | TBPR | SERec |
| RSGAN | IF-BPR | MHCN | CUNE | ESRF | |||||
| Deep Recommenders | |||||||||
| APR | CDAE | DMF | NeuMF | CFGAN | IRGAN | LightGCN | NGCF | MHCN | DHCF |
| Diffnet | ESRF | ||||||||
| Baselines | |||||||||
| UserMean | ItemMean | MostPopular | Rand | ||||||
Related Datasets
| Data Set | Basic Meta | User Context | ||||||
|---|---|---|---|---|---|---|---|---|
| Users | Items | Ratings (Scale) | Density | Users | Links (Type) | |||
| Ciao [1] | 7,375 | 105,114 | 284,086 | [1, 5] | 0.0365% | 7,375 | 111,781 | Trust |
| Epinions [2] | 40,163 | 139,738 | 664,824 | [1, 5] | 0.0118% | 49,289 | 487,183 | Trust |
| Douban [3] | 2,848 | 39,586 | 894,887 | [1, 5] | 0.794% | 2,848 | 35,770 | Trust |
| LastFM [4] | 1,892 | 17,632 | 92,834 | implicit | 0.27% | 1,892 | 25,434 | Trust |
Reference
[1]. Tang, J., Gao, H., Liu, H.: mtrust:discerning multi-faceted trust in a connected world. In: International Conference on Web Search and Web Data Mining, WSDM 2012, Seattle, Wa, Usa, February. pp. 93–102 (2012)
[2]. Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems. pp. 17–24. ACM (2007)
[3]. G. Zhao, X. Qian, and X. Xie, “User-service rating prediction by exploring social users’ rating behaviors,” IEEE Transactions on Multimedia, vol. 18, no. 3, pp. 496–506, 2016.
[4]. Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. 2nd Workshop on Information Heterogeneity and Fusion in Recom- mender Systems (HetRec 2011). In Proceedings of the 5th ACM conference on Recommender systems (RecSys 2011). ACM, New York, NY, USA
Thanks
If our project is helpful to you, please cite one of these papers. @inproceedings{yu2018adaptive, title={Adaptive implicit friends identification over heterogeneous network for social recommendation}, author={Yu, Junliang and Gao, Min and Li, Jundong and Yin, Hongzhi and Liu, Huan}, booktitle={Proceedings of the 27th ACM International Conference on Information and Knowledge Management}, pages={357--366}, year={2018}, organization={ACM} } @article{yu2019generating, title={Generating Reliable Friends via Adversarial Training to Improve Social Recommendation}, author={Yu, Junliang and Gao, Min and Yin, Hongzhi and Li, Jundong and Gao, Chongming and Wang, Qinyong}, booktitle={2019 IEEE International Conference on Data Mining (ICDM)}, pages={768--777}, year={2019}, organization={IEEE}, }Note that the project description data, including the texts, logos, images, and/or trademarks,
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