AFGSRec: A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features

doi:10.12141/j.issn.1000-565X.220180

Abstract

Abstract:

Previous session-based recommendation systems usually capture users’ consumption preferences from their recent transaction records, and this method ignores the influence of global transaction information and friends’ preferences on users’ transaction behavior, resulting in less accurate recommendation results of the model. To solve the problem, this paper proposed a social recommendation model AFGSRec based on an adaptive fusion of global collaborative features. Firstly, a heterogeneous graph neural network was used to model users and their historical transaction information on the social network for capturing global collaborative features and social influence among friends. Secondly, this paper designed a graph neural network based on a selection mechanism that effectively filters out the node transition features irrelevant to the current session and captures user preferences more accurately. Thirdly, an adaptive fusion method was designed to capture the impact of global collaborative features on users’ current preferences dynamically and improve the model’s recommendation accuracy. Finally, this paper used a dynamic cyclical learning rate to help the model better handle saddle points during the training process to improve the convergence speed of model AFGSRec. The experimental results show that AFGSRec is robust; both the HR (Hit Rate) and MRR (Mean Reciprocal Rank) of AFGSRec outperform the state-of-art model SERec. On the Gowalla dataset, HR@10 and HR@20 are increased by 1.91% and 1.15%, respectively; MRR@10 and MRR@20 are increased by 5.05% and 4.83%, respectively. On the Delicious dataset, HR@10 and HR@20 are increased by 2.45% and 1.19%, respectively; MRR@10 and MRR@20 are increased by 4.84% and 4.32%, respectively.

Key words: graph neural network, recommendation system, social networking, global collaborative feature, dynamic cyclical learning rate

CLC Number:

TP391

CAI Xiaodong, ZENG Zhiyang . AFGSRec: A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 71-79.

Figures/Tables 9

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数据集	用户数	交易物品数	交易次数	社交关系	序列数	用户平均邻居节点数	用户平均交易数	单个序列平均交易数
Gowalla	33 661	41 229	1 218 599	283 778	258 732	8.43	36.20	4.71
Delicious	1 313	5 793	266 190	9 130	60 397	6.95	202.73	4.41

K	模型	Gowalla		Delicious
K	模型	HR/%	MRR/%	HR/%	MRR/%
10	SR-GNN	41.31	22.39	37.01	19.57
	DGRec	42.18	23.04	37.78	20.07
	SERec	46.01	25.14	40.02	21.29
	AFGSRec	46.89	26.41	41.00	22.32
20	SR-GNN	49.31	22.94	45.74	20.20
	DGRec	49.95	23.58	47.36	20.73
	SERec	53.72	25.67	49.53	21.98
	AFGSRec	54.34	26.91	50.12	22.93

K	模型	Gowalla		Delicious
K	模型	HR/%	MRR/%	HR/%	MRR/%
10	AFGSRec-wSPI	46.31	26.02	40.49	22.09
	AFGSRec-wAFC	46.43	26.18	40.28	21.91
	AFGSRec-wCYC	46.22	25.39	40.58	22.10
	AFGSRec	46.89	26.41	41.00	22.32
20	AFGSRec-wSPI	53.83	26.03	49.70	22.52
	AFGSRec-wAFC	53.74	26.33	49.71	22.57
	AFGSRec-wCYC	53.55	25.87	49.81	22.60
	AFGSRec	54.34	26.91	50.12	22.93

K	融合方式	Gowalla		Delicious
K	融合方式	HR/%	MRR/%	HR/%	MRR/%
10	CAT	45.96	25.21	40.21	21.65
	SUM	45.60	25.14	40.37	21.57
	AFC	46.19	25.48	40.46	21.70
20	CAT	53.69	26.03	49.54	22.27
	SUM	53.02	25.64	49.60	22.18
	AFC	53.95	26.00	49.62	22.38

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