baituhuangyu / recomend-recq

RecQ: A Python Framework for Recommender Systems (TensorFlow Supported). clone from https://github.com/Coder-Yu/RecQ

RecQ

Latest News

We are now transfering RecQ to TensorFlow. A GPU based version would be provided in the coming weeks.
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...)

Introduction

Founder: @Coder-Yu
Main Contributors: @DouTong @Niki666 @HuXiLiFeng @BigPowerZ @flyxu
Released by School of Software Engineering, Chongqing University
More algorithms (ranking based and context-aware) can be found in another project of mine Yue

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.

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.
• Data visualization: RecQ can help visualize the input dataset without running any algorithm.

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.

          - readConfiguration()
          - 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.

           

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.
LOCABAL	Tang, Jiliang, et al. Exploiting local and global social context for recommendation, AAAI 2013.
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.
SocialFD	Yu et al., A Social Recommender Based on Factorization and Distance Metric Learning, IEEE Access.

               

Item Ranking	Paper
APR	He et al., Adversarial Personalized Ranking for Recommendation, SIGIR 2018.
ExpoMF	Liang et al., Modeling User Exposure in Recommendation, WWW 2016.
SERec	Wang et al., Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation, AAAI 2018.
CDAE	Wu et al., Collaborative Denoising Auto-Encoders for Top-N Recommender Systems, WSDM 2016.
DMF	Xue et al., Deep Matrix Factorization Models for Recommender Systems, IJCAI 2017.
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.
WRMF	Yifan Hu et al.Collaborative Filtering for Implicit Feedback Datasets, KDD 2009.
TBPR	Wang et al. Social Recommendation with Strong and Weak Ties, CIKM 2016.
IF-BPR	Yu et al. Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation, CIKM 2018.

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