QRec: A Python Framework for quick implementation of recommender systems (TensorFlow Based)

Last update: Dec 27, 2022

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Overview

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

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.

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.

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.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

Implement Your Model

1.Make your new algorithm generalize the proper base class.
2.Reimplement some of the following functions as needed.

          - readConfiguration()
          - printAlgorConfig()
          - initModel()
          - buildModel()
          - 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.

Related Datasets

Data Set	Basic Meta					User Context
Data Set	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

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.

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}
}

Comments

ImportError: No module named mkl

你好，在Macbook上运行main.py出现了一个问题，下载了依赖mkl，还是出现这个：

/usr/local/bin/python2.7 /Users/feanlau/Desktop/RecQ/main/main.py
Traceback (most recent call last):
  File "/Users/feanlau/Desktop/RecQ/main/main.py", line 3, in <module>
    from RecQ import RecQ
  File "/Users/feanlau/Desktop/RecQ/main/RecQ.py", line 9, in <module>
    import mkl
ImportError: No module named mkl

Process finished with exit code 1

jietu20181010-145943

请问一下，这个问题如何解决

opened by feanlau 12

Got 'InvalidArgumentError: indices[0] = 1875 is not in [0, 1795)' in running RSGAN

When running the RSGAN algorithm, this error below appears frequently that I can hardly get the predicting process completed. I was wondering whether the "Process Process-2" is right or not. Is it the problem of multi-process?

Predicting [1]...
RSGAN [1] progress:0/1753
RSGAN [1] progress:100/1753
RSGAN [1] progress:200/1753
RSGAN [1] progress:300/1753
RSGAN [1] progress:400/1753
Process Process-2:
Traceback (most recent call last):
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/multiprocessing/process.py", line 267, in _bootstrap
    self.run()
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/home/azure/RecQ-master/main/RecQ.py", line 116, in run
    measure[order] = algor.execute()
  File "../baseclass/Recommender.py", line 257, in execute
    self.evalRanking()
  File "../baseclass/Recommender.py", line 153, in evalRanking
    predictedItems = self.predictForRanking(user)
  File "../algorithm/ranking/RSGAN.py", line 448, in predictForRanking
    res = self.sess.run(self.d_output, {self.u_idx:[u]})
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 950, in run
    run_metadata_ptr)
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1173, in _run
    feed_dict_tensor, options, run_metadata)
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1350, in _do_run
    run_metadata)
  File "/home/azure/miniconda3/envs/py2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1370, in _do_call
    raise type(e)(node_def, op, message)
InvalidArgumentError: indices[0] = 1875 is not in [0, 1795)
         [[node discriminator/u_e (defined at ../algorithm/ranking/RSGAN.py:334) ]]

Errors may have originated from an input operation.
Input Source operations connected to node discriminator/u_e:
 U/read (defined at ../baseclass/DeepRecommender.py:24) 
 u_idx (defined at ../baseclass/DeepRecommender.py:19)

opened by HanlynnKe 11

Graph Neural Network for social recommendaiton

As you updated Deep Graph Collaborative Filtering recently, have you tried to implement another state-of-the-art algorithm GraphRec from the paper Graph Neural Network for Social Recommendation in RecQ?

I met some problems when implementing GraphRec as getting worse results than reported in the paper. I'm wondering if it's because my implementation is wrong or GraphRec doesn't work as reported.

opened by golsutsg 8
关于复现ESRF模型转为torch的问题

于老师，您好！我最近在将ESRF的代码转为torch的版本，但是在计算可替代邻居的部分占用了太大的内存消耗，self.alternativeNeighborhood = tf.vectorized_map(fn=lambda em:getAlternativeNeighborhood(em),elems=user_features)，这句我转换为了循环函数，for elems in user_features: alternativeNeighborhood.append(getAlternativeNeighborhood(elems))，但是在用稍微大一点的数据集如Ciao时，都会内存溢出（如Ciao,7375个user，在为第700多个用户计算可替代邻居时内存就满了，8G的显存）。在打印GPU的使用情况发现，就是这句代码为每个用户计算时，占用了特别大内存消耗。然后我在这部分仅仅计算每个batch的users的可替代邻居，但是最后计算损失函数，又没有可替代邻居的embedding时，因此想请教老师，有什么能解决这句代码内存消耗的方法吗？

opened by happylee 6
Implementation and formula problems about RSGAN.
Hi. I have some problems with both the implementation and several formulas in your RSGAN paper.

As you present the whole min-max loss of RSGAN in Eq. 9, however, in Eq. 8, the generator's loss is defined without the latter part of Eq. 9, log(sigmoid(x_uz-x_uj)), which is the difference between the scores of generated items and negative items. I think, to reach an equilibrium, the loss of the generator should maximize the whole loss of Eq.9. Why do you design the generator loss without the latter part of Eq.9?

As in your implementation, the loss of the generator is defined without the log part and is multiplied by 30. Is it implemented in this way to avoid too small gradients brought by the log function? And is this so-called implementation trick by others?

Thanks for your time. Your reply always helps a lot.
opened by iYiYaHa 6
关于MHCN论文结果复现的问题

我在lastfm上运行了代码中MHCN的模型，看到代码中实现的是s2-MHCN，但运行出的结果与论文中给出的实验数据有一定出入（如下： Precision:0.19607427055702917 Recall:0.1991364574123199 F1:0.197593500708107 NDCG:0.24458583342484325

opened by C-Comfundo 5
Typle Error

Hello professor!thanks for your code. when I run main.py and choose the algorithm . it always has the same question: Traceback (most recent call last): File "C:/Users/Administrator/Desktop/新建文件夹/RecQ/RecQ-master/main/main.py", line 67, in recSys.execute() File "C:\Users\Administrator\Desktop\新建文件夹\RecQ\RecQ-master\main\RecQ.py", line 100, in execute self.measure = [dict.setdafault(m)[i] for i in range(1,k+1)] File "C:\Users\Administrator\Desktop\新建文件夹\RecQ\RecQ-master\main\RecQ.py", line 100, in self.measure = [dict.setdafault(m)[i] for i in range(1,k+1)] AttributeError: type object 'dict' has no attribute 'setdafault' How can I solve it?looking for your reply.

opened by lux0818 5
LOCABAL梯度下降式子疑问

您好，我看到您写的LOCABAL算法了，我对您用的梯度下降公式有点疑问，您在哪儿找到的这个公式，我在原论文里没找到啊，然后我在其他论文里找到了部分，a synthetic approach for recommendation combining ratings social relations and reviews. 这个论文里使用了LOCABAL算法，他给出的迭代公式和您代码里写的还是有很大不同的。这里我还是有点疑惑的。

opened by flyxu 5
Problems of results of LightGCN

Hi, I've run LightGCN in QRec recently. But I find that the performance of it is not good. The results I get on Yelp are Precision:0.0252', 'Recall:0.0575', 'F1:0.0350', 'NDCG:0.0465 after 1000 epochs. While the results of SEPT on the same dataset are 'Precision:0.0310', 'Recall:0.0712', 'F1:0.0432', 'NDCG:0.0583'] after 30 epochs. And I also set if epoch > self.maxEpoch / 3: to if epoch > self.maxEpoch: in SEPT, which means SEPT will behave like LightGCN. But the results, which are ['Precision:0.0289', 'Recall:0.0641', 'F1:0.0399', 'NDCG:0.0532'] after 30 epochs, are also much better than the original one. BTW, the initial rec_loss of the degraded SEPT is much lower than the original LightGCN, ~300/batch vs ~1000/batch. So I want to know how to make the original LightGCN better. I've already tried the same normalization and initiliazation as SEPT, but it doesn't work.

opened by ShadowTinker 4
How to speed up the evaluation process

Hi, I noticed that you are evaluating one user at each time. While my test data has hundreds of thousands users, and the evaluation will take about a few minutes. While the train on GPU is very fast. So can you provide some fast evaluation method? Thank you!

opened by ll0ruc 4
存在一些问题

Initializing model [1]... Traceback (most recent call last): File "C:/Users/MACHENIKE/Desktop/论文代码/QRec-master/main.py", line 54, in recSys.execute() File "C:\Users\MACHENIKE\Desktop\论文代码\QRec-master\QRec.py", line 114, in execute eval(recommender).execute() File "C:\Users\MACHENIKE\Desktop\论文代码\QRec-master\base\recommender.py", line 214, in execute self.initModel() File "C:\Users\MACHENIKE\Desktop\论文代码\QRec-master\model\ranking\TBPR.py", line 41, in initModel self.t_s = self.weights[len(self.weights)/2+1:].sum()/(len(self.weights[len(self.weights)/2+1:])+0.0) TypeError: slice indices must be integers or None or have an index method

opened by Yang-YYY 4

Owner

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