Introduction
QRec is a Python framework for recommender systems (Supported by Python 3.7.4 and Tensorflow 1.14+) in which a number of influential and newly state-of-the-art recommendation models are implemented. QRec has a lightweight architecture and provides user-friendly interfaces. It can facilitate model implementation and evaluation.
Founder and principal contributor: @Coder-Yu
Other contributors: @DouTong @Niki666 @HuXiLiFeng @BigPowerZ @flyxu
Supported by: @AIhongzhi (A/Prof. Hongzhi Yin, UQ), @mingaoo (A/Prof. Min Gao, CQU)
What's New
12/10/2021 - BUIR proposed in SIGIR'21 paper has been added.
30/07/2021 - We have transplanted QRec from py2 to py3.
07/06/2021 - SEPT proposed in our KDD'21 paper has been added.
16/05/2021 - SGL proposed in SIGIR'21 paper has been added.
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.
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.
20/02/2019 - IRGAN proposed in SIGIR'17 has been added.
12/02/2019 - CFGAN proposed in CIKM'18 has been added.
Architecture
Workflow
Features
- Cross-platform: QRec can be easily deployed and executed in any platforms, including MS Windows, Linux and Mac OS.
- Fast execution: QRec is based on Numpy, Tensorflow and some lightweight structures, which make it run fast.
- Easy configuration: QRec configs recommenders with a configuration file and provides multiple evaluation protocols.
- Easy expansion: QRec provides a set of well-designed recommendation interfaces by which new algorithms can be easily implemented.
Requirements
- gensim==4.1.2
- joblib==1.1.0
- mkl==2022.0.0
- mkl_service==2.4.0
- networkx==2.6.2
- numba==0.53.1
- numpy==1.20.3
- scipy==1.6.2
- tensorflow==1.14.0
Usage
There are two ways to run the recommendation models in QRec:
- 1.Configure the xx.conf file in the directory named config. (xx is the name of the model you want to run)
- 2.Run main.py.
Or
- Follow the codes in snippet.py.
For more details, we refer you to the handbook of QRec.
Configuration
Essential Options
| Entry | Example | Description |
|---|---|---|
| ratings | D:/MovieLens/100K.txt | Set the file path of the dataset. Format: each row separated by empty, tab or comma symbol. |
| social | D:/MovieLens/trusts.txt | Set the file path of the 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. |
| social.setup | -columns 0 1 2 | -columns: (trustor, trustee, weight) columns of social data are used. |
| mode.name | UserKNN/ItemKNN/SlopeOne/etc. | name of the recommendation model. |
| evaluation.setup | -testSet ../dataset/testset.txt | Main option: -testSet, -ap, -cv (choose one of them) -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 the test set. e.g. -ap 0.2) -cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5) -predict path/to/user list/file (predict for a given list of users without evaluation; need to mannually specify the user list file (each line presents a user)) Secondary option:-b, -p, -cold, -tf, -val (multiple choices) -val ratio (model test would be conducted on the validation set which is generated by randomly sampling the training dataset with the given ratio.) -b thres （binarizing the rating values. Ratings equal or greater than thres will be changed into 1, and ratings lower than thres will be left out. 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 will be conducted on TensorFlow (only applicable and needed for shallow models)) -cold thres (evaluation on cold-start users; users in the training set with rated items more than thres 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. *QRec 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.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.epoch | 100/200/number | Set the maximum number of epoch 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 |
Implement Your Model
- 1.Make your new algorithm generalize the proper base class.
- 2.Reimplement some of the following functions as needed.
- printAlgorConfig()
- initModel()
- trainModel()
- saveModel()
- loadModel()
- predictForRanking()
- predict()
For more details, we refer you to the handbook of QRec.
Implemented Algorithms
| 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. |
| SGL | Wu et al. Self-supervised Graph Learning for Recommendation, SIGIR'21. |
| SEPT | Yu et al. Socially-Aware Self-supervised Tri-Training for Recommendation, KDD'21. |
| BUIR | Lee et al. Bootstrapping User and Item Representations for One-Class Collaborative Filtering, SIGIR'21. |
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 |
| Yelp [5] | 19,539 | 21,266 | 450,884 | implicit | 0.11% | 19,539 | 864,157 | Trust |
| Amazon-Book [6] | 52,463 | 91,599 | 2,984,108 | implicit | 0.11% | - | - | - |
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
[5]. Yu et al. Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation, WWW'21.
[6]. He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, SIGIR'20.
Acknowledgment
This project is supported by the Responsible Big Data Intelligence Lab (RBDI) at the school of ITEE, University of Queensland, and Chongqing University.
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}
}
@inproceedings{yu2021self,
title={Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation},
author={Yu, Junliang and Yin, Hongzhi and Li, Jundong and Wang, Qinyong and Hung, Nguyen Quoc Viet and Zhang, Xiangliang},
booktitle={Proceedings of the Web Conference 2021},
pages={413--424},
year={2021}
}
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