Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (5): 1485-1492.DOI: 10.11772/j.issn.1001-9081.2023050756
Special Issue: 第十九届中国机器学习会议(CCML 2023)
• The 19th China Conference on Machine Learning (CCML 2023) • Previous Articles Next Articles
Jiong WANG1, Taotao TANG1, Caiyan JIA1,2()
Received:
2023-06-13
Revised:
2023-07-05
Accepted:
2023-07-09
Online:
2023-08-01
Published:
2024-05-10
Contact:
Caiyan JIA
About author:
WANG Jiong, born in 1999, M. S. candidate. His research interests include graph neural network, contrastive learning, recommendation system.通讯作者:
贾彩燕
作者简介:
汪炅(1999—),男,安徽黄山人,硕士研究生,CCF会员,主要研究方向:图神经网络、对比学习、推荐系统CLC Number:
Jiong WANG, Taotao TANG, Caiyan JIA. PAGCL: positive augmentation graph contrastive learning recommendation method without negative sampling[J]. Journal of Computer Applications, 2024, 44(5): 1485-1492.
汪炅, 唐韬韬, 贾彩燕. 无负采样的正样本增强图对比学习推荐方法PAGCL[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1485-1492.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050756
方法 | Yelp2018 | Amazon-Book | Amazon-Kindle | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.041 0 | 0.031 8 | 0.205 7 | 0.131 5 |
LightGCN-WN | 0.031 4 | 0.026 0 | 0.008 3 | 0.006 7 | 0.001 7 | 0.000 9 |
SGL | 0.067 5 | 0.055 5 | 0.047 8 | 0.037 9 | 0.209 0 | 0.135 2 |
SGL-WN | 0.067 8 | 0.055 9 | 0.048 2 | 0.038 1 | 0.211 2 | 0.136 2 |
Tab. 1 Experiment results of LightGCN and SGL with different losses
方法 | Yelp2018 | Amazon-Book | Amazon-Kindle | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.041 0 | 0.031 8 | 0.205 7 | 0.131 5 |
LightGCN-WN | 0.031 4 | 0.026 0 | 0.008 3 | 0.006 7 | 0.001 7 | 0.000 9 |
SGL | 0.067 5 | 0.055 5 | 0.047 8 | 0.037 9 | 0.209 0 | 0.135 2 |
SGL-WN | 0.067 8 | 0.055 9 | 0.048 2 | 0.038 1 | 0.211 2 | 0.136 2 |
数据集 | 用户数 | 项目数 | 交互次数 | 密度/% |
---|---|---|---|---|
Yelp2018 | 31 668 | 38 048 | 1 561 406 | 0.130 |
Amazon-Kindle | 138 333 | 98 572 | 1 909 965 | 0.014 |
Alibaba-iFashion | 300 000 | 81 614 | 1 607 813 | 0.007 |
Tab. 2 Statistics of experiment datasets
数据集 | 用户数 | 项目数 | 交互次数 | 密度/% |
---|---|---|---|---|
Yelp2018 | 31 668 | 38 048 | 1 561 406 | 0.130 |
Amazon-Kindle | 138 333 | 98 572 | 1 909 965 | 0.014 |
Alibaba-iFashion | 300 000 | 81 614 | 1 607 813 | 0.007 |
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion |
---|---|---|---|
SGL | |||
SimGCL | |||
XSimGCL | |||
PAGCL |
Tab. 3 Best hyperparameters of different constrative learning methods
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion |
---|---|---|---|
SGL | |||
SimGCL | |||
XSimGCL | |||
PAGCL |
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.205 7 | 0.131 5 | 0.105 3 | 0.050 5 |
SGL | 0.067 5(+5.6%) | 0.055 5(+5.7%) | 0.209 0(+1.6%) | 0.135 2(+2.8%) | 0.109 3(+3.8%) | 0.053 1(+5.1%) |
NCL | 0.067 0(+4.9%) | 0.056 2(+7.0%) | 0.209 0(+1.6%) | 0.134 8(+2.5%) | 0.108 8(+3.3%) | 0.052 8(+4.6%) |
MixGCF | 0.071 3(+11.6%) | 0.058 9(+12.2%) | 0.209 8(+2.0%) | 0.135 5(+3.0%) | 0.108 5(+3.0%) | 0.052 0(+3.0%) |
SimGCL | 0.072 1(+12.8%) | 0.060 1(+14.5%) | 0.210 4(+2.3%) | 0.137 4(+4.5%) | 0.115 1(+9.3%) | 0.056 7(+12.3%) |
XSimGCL | 0.072 3(+13.1%) | 0.060 4(+15.0%) | 0.214 7(+4.4%) | 0.141 5(+7.6%) | 0.119 6(+13.6%) | 0.058 6(+16.0%) |
PAGCL | 0.073 2(+14.6%) | 0.061 2(+16.6%) | 0.217 1(+5.5%) | 0.143 9(+9.4%) | 0.120 8(+14.7%) | 0.059 4(+17.6%) |
Tab. 4 Overall performance comparison among different SOTA methods on three benchmarks
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.205 7 | 0.131 5 | 0.105 3 | 0.050 5 |
SGL | 0.067 5(+5.6%) | 0.055 5(+5.7%) | 0.209 0(+1.6%) | 0.135 2(+2.8%) | 0.109 3(+3.8%) | 0.053 1(+5.1%) |
NCL | 0.067 0(+4.9%) | 0.056 2(+7.0%) | 0.209 0(+1.6%) | 0.134 8(+2.5%) | 0.108 8(+3.3%) | 0.052 8(+4.6%) |
MixGCF | 0.071 3(+11.6%) | 0.058 9(+12.2%) | 0.209 8(+2.0%) | 0.135 5(+3.0%) | 0.108 5(+3.0%) | 0.052 0(+3.0%) |
SimGCL | 0.072 1(+12.8%) | 0.060 1(+14.5%) | 0.210 4(+2.3%) | 0.137 4(+4.5%) | 0.115 1(+9.3%) | 0.056 7(+12.3%) |
XSimGCL | 0.072 3(+13.1%) | 0.060 4(+15.0%) | 0.214 7(+4.4%) | 0.141 5(+7.6%) | 0.119 6(+13.6%) | 0.058 6(+16.0%) |
PAGCL | 0.073 2(+14.6%) | 0.061 2(+16.6%) | 0.217 1(+5.5%) | 0.143 9(+9.4%) | 0.120 8(+14.7%) | 0.059 4(+17.6%) |
方法 | 邻接矩阵 | 图编码 | 推荐 | 对比 |
---|---|---|---|---|
SGL | 2Bd | BMd | ||
SimGCL | 2Bd | BMd | ||
XSimGCL | 2Bd | BMd | ||
PAGCL | Bd | BMd |
Tab. 5 Time complexities of contrast methods
方法 | 邻接矩阵 | 图编码 | 推荐 | 对比 |
---|---|---|---|---|
SGL | 2Bd | BMd | ||
SimGCL | 2Bd | BMd | ||
XSimGCL | 2Bd | BMd | ||
PAGCL | Bd | BMd |
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
PAGCL-2L | 0.073 2 | 0.061 2 | 0.214 5 | 0.140 5 | 0.120 8 | 0.059 4 |
PAGCL-3L | 0.072 6 | 0.060 4 | 0.217 1 | 0.143 9 | 0.120 4 | 0.058 7 |
Tab. 6 Results of PAGCL with different layers L
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
PAGCL-2L | 0.073 2 | 0.061 2 | 0.214 5 | 0.140 5 | 0.120 8 | 0.059 4 |
PAGCL-3L | 0.072 6 | 0.060 4 | 0.217 1 | 0.143 9 | 0.120 4 | 0.058 7 |
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.205 7 | 0.131 5 | 0.105 3 | 0.050 5 |
LightGCN-WN | 0.031 4 | 0.026 0 | 0.001 7 | 0.000 9 | 0.000 1 | 0.000 0 |
SGL | 0.067 5 | 0.055 5 | 0.209 0 | 0.135 2 | 0.109 3 | 0.053 1 |
SGL-WN | 0.067 8 | 0.055 9 | 0.211 2 | 0.136 2 | 0.101 8 | 0.050 1 |
SimGCL | 0.072 1 | 0.060 1 | 0.210 4 | 0.137 4 | 0.115 1 | 0.056 7 |
SimGCL-WN | 0.072 0 | 0.060 1 | 0.211 3 | 0.138 7 | 0.115 1 | 0.056 5 |
XSimGCL | 0.072 3 | 0.060 4 | 0.214 7 | 0.141 5 | 0.119 6 | 0.058 6 |
XSimGCL-WN | 0.072 6 | 0.060 4 | 0.216 1 | 0.143 2 | 0.118 6 | 0.058 2 |
PAGCL-N | 0.072 9 | 0.061 1 | 0.215 5 | 0.140 2 | 0.121 1 | 0.059 3 |
PAGCL | 0.073 2 | 0.061 2 | 0.217 1 | 0.143 9 | 0.120 8 | 0.059 4 |
Tab. 7 Result comparison of SOTA CL-based methods with different losses
方法 | Yelp2018 | Amazon-Kindle | Alibaba-iFashion | |||
---|---|---|---|---|---|---|
R@20 | N@20 | R@20 | N@20 | R@20 | N@20 | |
LightGCN | 0.063 9 | 0.052 5 | 0.205 7 | 0.131 5 | 0.105 3 | 0.050 5 |
LightGCN-WN | 0.031 4 | 0.026 0 | 0.001 7 | 0.000 9 | 0.000 1 | 0.000 0 |
SGL | 0.067 5 | 0.055 5 | 0.209 0 | 0.135 2 | 0.109 3 | 0.053 1 |
SGL-WN | 0.067 8 | 0.055 9 | 0.211 2 | 0.136 2 | 0.101 8 | 0.050 1 |
SimGCL | 0.072 1 | 0.060 1 | 0.210 4 | 0.137 4 | 0.115 1 | 0.056 7 |
SimGCL-WN | 0.072 0 | 0.060 1 | 0.211 3 | 0.138 7 | 0.115 1 | 0.056 5 |
XSimGCL | 0.072 3 | 0.060 4 | 0.214 7 | 0.141 5 | 0.119 6 | 0.058 6 |
XSimGCL-WN | 0.072 6 | 0.060 4 | 0.216 1 | 0.143 2 | 0.118 6 | 0.058 2 |
PAGCL-N | 0.072 9 | 0.061 1 | 0.215 5 | 0.140 2 | 0.121 1 | 0.059 3 |
PAGCL | 0.073 2 | 0.061 2 | 0.217 1 | 0.143 9 | 0.120 8 | 0.059 4 |
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