Multi-behavior recommendation based on cascading residual graph convolutional network

doi:10.11772/j.issn.1001-9081.2024040461

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (4): 1223-1231.DOI: 10.11772/j.issn.1001-9081.2024040461

• Data science and technology • Previous Articles Next Articles

Multi-behavior recommendation based on cascading residual graph convolutional network

Weichao DANG, Chujun SONG(), Gaimei GAO, Chunxia LIU

College of Computer Science and Technology，Taiyuan University of Science and Technology，Taiyuan Shanxi 030024，China

Received:2024-04-18 Revised:2024-06-12 Accepted:2024-06-19 Online:2025-04-08 Published:2025-04-10
Contact: Chujun SONG
About author:DANG Weichao， born in 1974， Ph. D.， associate professor. His research interests include intelligent computing， software reliability.
GAO Gaimei， born in 1978， Ph. D.， associate professor. Her research interests include network security， cryptography.
LIU Chunxia， born in 1977， M. S.， associate professor. Her research interests include software engineering， database.
Supported by:
Shanxi Provincial Natural Science Foundation(202203021211194);Doctoral Research Start-up Fund Project of Taiyuan University of Science and Technology(20202063);Graduate Education Innovation Project of Taiyuan University of Science and Technology(SY2022063)

基于级联残差图卷积网络的多行为推荐

党伟超, 宋楚君(), 高改梅, 刘春霞

太原科技大学计算机科学与技术学院，太原 030024

通讯作者: 宋楚君
作者简介:党伟超（1974—），男，山西运城人，副教授，博士，CCF会员，主要研究方向：智能计算、软件可靠性
高改梅（1978—），女，山西吕梁人，副教授，博士，CCF会员，主要研究方向：网络安全、密码学
刘春霞（1977—），女，山西大同人，副教授，硕士，CCF会员，主要研究方向：软件工程、数据库。
基金资助:
山西省自然科学基金资助项目(202203021211194);太原科技大学博士科研启动基金资助项目(20202063);太原科技大学研究生教育创新项目(SY2022063)

Abstract

Abstract:

A Multi-Behavior Recommendation based on Cascading Residual graph convolutional network （CRMBR） model was proposed to address the problems of data sparsity and neglecting the complex connections among multiple behaviors in multi-behavior recommendation research. Firstly， the global embeddings of users and items were learned from a unified isomorphic graph constructed from the interactions of all behaviors and used as initialization embeddings. Secondly， the embeddings of different types of behaviors were refined continuously to improve the user preferences by capturing the connections among different behaviors through cascading residual blocks. Finally， user and item embeddings were aggregated through two different aggregation strategies， respectively， and optimized using Multi-Task Learning （MTL）. Experimental results on several real datasets show that the recommendation performance of CRMBR model is better than that of the current mainstream models. Compared with the advanced benchmark model — Multi-Behavior Hierarchical Graph Convolutional Network （MB-HGCN）， the proposed model has the Hit Rate （HR@20） and Normalized Discount Cumulative Gain （NDCG@20） improved by 3.1% and 3.9% on Tmall dataset， increased by 15.8% and 16.9% on Beibei dataset， and improved by 1.0% and 3.3% on Jdata dataset， respectively， which validates the effectiveness of the proposed model.

Key words: multi-behavior recommendation, cascading residual, Graph Convolutional Network (GCN), aggregation strategy, Multi-Task Learning (MTL)

摘要：

针对多行为推荐研究中存在的数据稀疏和忽视多行为之间复杂联系的问题，提出一种基于级联残差图卷积网络的多行为推荐（CRMBR）模型。首先，从由所有行为的相互作用构建的统一同构图中学习用户和项目的全局嵌入，并将这些嵌入用作初始化嵌入；其次，通过级联残差块捕获不同行为之间的联系，以不断细化不同类型行为的嵌入，从而完善用户偏好；最后，通过2种不同的聚合策略分别聚合用户和项目嵌入，并采用多任务学习（MTL）优化这些嵌入。在多个真实数据集上的实验结果表明，CRMBR模型的推荐性能优于目前的主流模型。与先进的基准模型——多行为分层图卷积网络（MB-HGCN）相比，在Tmall数据集上，所提模型的命中率（HR@20）和归一化折损累积增益（NDCG@20）分别提升了3.1%和3.9%；在Beibei数据集上，则分别提升了15.8%和16.9%；在Jdata数据集上，则分别提升了1.0%和3.3%，验证了所提模型的有效性。

CLC Number:

TP391.4

Weichao DANG, Chujun SONG, Gaimei GAO, Chunxia LIU. Multi-behavior recommendation based on cascading residual graph convolutional network[J]. Journal of Computer Applications, 2025, 45(4): 1223-1231.

党伟超, 宋楚君, 高改梅, 刘春霞. 基于级联残差图卷积网络的多行为推荐[J]. 《计算机应用》唯一官方网站, 2025, 45(4): 1223-1231.

Figures/Tables 12

Fig. 1 Overall architecture of CRMBR model

Fig. 2 From initial embeddings to global embeddings

Fig. 3 Correlation graph of multiple behaviors

Tab. 1 Statistical information of datasets

数据集	用户数	项目数	购买数	加购数	收藏数	浏览数
Tmall	41 738	11 953	255 586	1 996	221 514	1 813 498
Beibei	21 716	7 997	304 576	642 622	—	2 412 586
Jdata	93 334	24 624	333 383	49 891	45 613	1 681 430

Tab. 2 Overall performance comparison results

数据集	评价指标	单行为			多行为								提高/%
数据集	评价指标	MF-BPR	NCF	LightGCN	R-GCN	NMTR	MBGCN	GNMR	S-MBRec	CRGCN	MB-HGCN	CRMBR	提高/%
Tmall	HR@10	0.023 0	0.030 1	0.039 3	0.031 6	0.051 7	0.054 9	0.039 3	0.069 4	0.084 0	0.146 1	0.150 2	2.8
	NDCG@10	0.012 4	0.015 3	0.020 9	0.015 7	0.025 0	0.028 5	0.019 3	0.036 2	0.044 2	0.077 0	0.080 1	4.0
	HR@20	0.031 6	0.042 0	0.053 8	0.048 9	0.084 7	0.079 9	0.061 9	0.100 9	0.123 8	0.207 2	0.213 6	3.1
	NDCG@20	0.014 4	0.018 2	0.024 3	0.019 8	0.033 0	0.034 5	0.024 7	0.043 8	0.054 0	0.092 0	0.095 6	3.9
	HR@50	0.043 4	0.067 8	0.081 3	0.082 6	0.149 8	0.128 5	0.107 1	0.155 3	0.199 4	0.314 9	0.318 6	1.2
	NDCG@50	0.016 6	0.023 1	0.029 5	0.026 2	0.045 6	0.043 8	0.033 2	0.054 4	0.068 5	0.113 0	0.116 0	2.7
Beibei	HR@10	0.026 8	0.029 6	0.030 9	0.032 7	0.031 5	0.037 3	0.039 6	0.048 9	0.053 9	0.061 9	0.069 4	12.1
	NDCG@10	0.013 9	0.014 6	0.016 1	0.016 1	0.014 6	0.019 3	0.021 9	0.025 3	0.025 9	0.029 7	0.034 3	15.5
	HR@20	0.042 7	0.045 3	0.047 8	0.056 1	0.058 7	0.063 9	0.064 0	0.077 0	0.094 4	0.101 9	0.118 0	15.8
	NDCG@20	0.017 9	0.018 5	0.020 4	0.021 9	0.021 4	0.025 9	0.028 0	0.032 4	0.036 1	0.039 7	0.046 4	16.9
	HR@50	0.079 3	0.080 9	0.088 0	0.111 8	0.127 6	0.128 7	0.121 9	0.123 4	0.181 7	0.200 9	0.229 7	14.3
	NDCG@50	0.025 0	0.021 6	0.028 2	0.032 9	0.034 8	0.038 6	0.039 4	0.041 5	0.053 2	0.059 2	0.068 4	15.5
Jdata	HR@10	0.185 0	0.209 0	0.225 2	0.240 6	0.314 2	0.280 3	0.306 8	0.412 5	0.500 1	0.533 8	0.541 1	1.4
	NDCG@10	0.123 8	0.141 0	0.143 6	0.144 4	0.171 7	0.157 2	0.158 1	0.277 9	0.291 4	0.323 8	0.329 2	1.7
	HR@20	0.219 2	0.246 1	0.282 5	0.341 8	0.408 6	0.360 3	0.369 4	0.495 7	0.619 0	0.645 0	0.651 5	1.0
	NDCG@20	0.132 5	0.150 4	0.158 2	0.158 8	0.196 6	0.179 0	0.194 4	0.298 9	0.322 5	0.353 3	0.364 8	3.3
	HR@50	0.265 2	0.293 4	0.365 8	0.487 3	0.522 7	0.504 5	0.460 7	0.603 6	0.768 5	0.774 9	0.783 6	1.1
	NDCG@50	0.141 7	0.159 9	0.174 7	0.189 1	0.219 8	0.198 4	0.202 9	0.320 3	0.353 5	0.380 4	0.390 2	2.6

Tab. 3 Influence of different designs on residual block

$L 2$	SC	Tmall				Beibei				Jdata
$L 2$	SC	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
√		0.025 8	0.011 7	0.039 4	0.017 2	0.014 9	0.008 4	0.033 7	0.010 6	0.163 9	0.089 2	0.220 1	0.102 8
	√	0.119 4	0.062 2	0.170 3	0.074 7	0.042 8	0.018 1	0.077 2	0.025 5	0.445 7	0.237 8	0.517 1	0.275 1
√	√	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Tab. 3 Influence of different designs on residual block

$L 2$	SC	Tmall				Beibei				Jdata
$L 2$	SC	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
√		0.025 8	0.011 7	0.039 4	0.017 2	0.014 9	0.008 4	0.033 7	0.010 6	0.163 9	0.089 2	0.220 1	0.102 8
	√	0.119 4	0.062 2	0.170 3	0.074 7	0.042 8	0.018 1	0.077 2	0.025 5	0.445 7	0.237 8	0.517 1	0.275 1
√	√	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Tab. 4 Influence of embedding learning in global graph G

方法	Tmall				Beibei				Jdata
方法	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
$w / o . G$	0.102 8	0.053 5	0.151 6	0.065 6	0.030 7	0.015 1	0.076 5	0.019 4	0.335 1	0.193 4	0.356 2	0.229 7
$w . G$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Tab. 4 Influence of embedding learning in global graph G

方法	Tmall				Beibei				Jdata
方法	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
$w / o . G$	0.102 8	0.053 5	0.151 6	0.065 6	0.030 7	0.015 1	0.076 5	0.019 4	0.335 1	0.193 4	0.356 2	0.229 7
$w . G$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Tab. 5 Influence of aggregation strategies

方法	Tmall				Beibei				Jdata
方法	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
$s u m a g g .$	0.108 4	0.065 8	0.168 2	0.071 5	0.552 7	0.027 3	0.101 9	0.031 7	0.447 0	0.259 9	0.583 3	0.307 0
$l i n e a r a g g .$	0.135 3	0.071 9	0.184 3	0.082 2	0.060 2	0.029 7	0.107 8	0.037 9	0.519 3	0.302 5	0.607 8	0.346 3
$a d a p t i v e a g g .$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.649 5	0.364 8
$f i x γ i k$	0.135 9	0.073 6	0.193 2	0.077 5	0.061 1	0.029 3	0.082 7	0.032 9	0.485 6	0.289 1	0.581 7	0.298 1
$w / o . ω k$	0.145 3	0.078 3	0.202 8	0.083 1	0.063 0	0.031 2	0.097 2	0.038 2	0.503 2	0.318 0	0.598 2	0.345 0
$w . ω k$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Tab. 5 Influence of aggregation strategies

方法	Tmall				Beibei				Jdata
方法	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20	HR@10	NDCG@10	HR@20	NDCG@20
$s u m a g g .$	0.108 4	0.065 8	0.168 2	0.071 5	0.552 7	0.027 3	0.101 9	0.031 7	0.447 0	0.259 9	0.583 3	0.307 0
$l i n e a r a g g .$	0.135 3	0.071 9	0.184 3	0.082 2	0.060 2	0.029 7	0.107 8	0.037 9	0.519 3	0.302 5	0.607 8	0.346 3
$a d a p t i v e a g g .$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.649 5	0.364 8
$f i x γ i k$	0.135 9	0.073 6	0.193 2	0.077 5	0.061 1	0.029 3	0.082 7	0.032 9	0.485 6	0.289 1	0.581 7	0.298 1
$w / o . ω k$	0.145 3	0.078 3	0.202 8	0.083 1	0.063 0	0.031 2	0.097 2	0.038 2	0.503 2	0.318 0	0.598 2	0.345 0
$w . ω k$	0.150 2	0.080 1	0.213 6	0.095 6	0.069 4	0.034 3	0.118 0	0.046 4	0.541 1	0.329 2	0.651 5	0.364 8

Fig. 4 Influence of different GCN layers on final performance of model

Fig. 5 Performance comparison of CRMBR’s different order and number of behaviors on Tmall dataset

Fig. 6 Performance comparison of CRMBR’s different order and number of behaviors on Beibei dataset

Fig. 7 Performance comparison of CRMBR’s different order and number of behaviors on Jdata dataset

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Multi-behavior recommendation based on cascading residual graph convolutional network

基于级联残差图卷积网络的多行为推荐

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