Founder: @Coder-Yu
Main Contributors: @DouTong @Niki666 @HuXiLiFeng @BigPowerZ
Released by School of Software Engineering, Chongqing University
RecQ is a Python library for recommender systems (Python 2.7.x). It implements a suit of state-of-the-art recommendations. To run RecQ easily (no need to setup packages used in RecQ one by one), the leading open data science platform Anaconda is strongly recommended. It integrates Python interpreter, common scientific computing libraries (such as Numpy, Pandas, and Matplotlib), and package manager, all of them make it a perfect tool for data science researcher.
To design it exquisitely, we refer to the library LibRec, which is implemented with Java.
- 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.
- Data visualization: RecQ can help visualize the input dataset without running any algorithm.
- 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.
| 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 -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 excuted parallelly, otherwise excuted one by one) |
| item.ranking | off -topN -1 | Main option: whether to do item ranking -topN N: the length of the recommendation list for item recommendation, default -1 for full list; |
| output.setup | on -dir ./Results/ | Main option: whether to output recommendation results -dir path: the directory path of output results. |
| 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. |
| 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 |
- 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()
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.
| Rating prediction | Paper |
|---|---|
| SlopeOne | Lemire and Maclachlan, Slope One Predictors for Online Rating-Based Collaborative Filtering, SDM 2005. |
| PMF | Salakhutdinov and Mnih, Probabilistic Matrix Factorization, NIPS 2008. |
| SoRec | Ma et al., SoRec: Social Recommendation Using Probabilistic Matrix Factorization, SIGIR 2008. |
| SocialMF | Jamali and Ester, A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks, RecSys 2010. |
| RSTE | Ma et al., Learning to Recommend with Social Trust Ensemble, SIGIR 2009. |
| SVD | Y. Koren, Collaborative Filtering with Temporal Dynamics, SIGKDD 2009. |
| SVD++ | Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, SIGKDD 2008. |
| SoReg | Ma et al., Recommender systems with social regularization, WSDM 2011. |
| EE | Khoshneshin et al., Collaborative Filtering via Euclidean Embedding, RecSys2010. |
| CoFactor | Liang et al., Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence, RecSys2016. |
| SREE | Li et al., Social Recommendation Using Euclidean embedding, IJCNN 2017. |
| CUNE-MF | Zhang et al., Collaborative User Network Embedding for Social Recommender Systems, SDM 2017. |
| Item Ranking | Paper |
|---|---|
| BPR | Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback, UAI 2009. |
| SBPR | Zhao et al., Leveraing Social Connections to Improve Personalized Ranking for Collaborative Filtering, CIKM 2014 |
| CUNE-BPR | Zhang et al., Collaborative User Network Embedding for Social Recommender Systems, SDM 2017. |
| 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 |
[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.