Recommendation method based on knowledge‑awareness and cross-level contrastive learning

doi:10.11772/j.issn.1001-9081.2023050613

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1121-1127.DOI: 10.11772/j.issn.1001-9081.2023050613

• Data science and technology • Previous Articles

Recommendation method based on knowledge‑awareness and cross-level contrastive learning

Jie GUO¹, Jiayu LIN²(), Zuhong LIANG¹^,³, Xiaobo LUO¹, Haitao SUN¹

^1.School of Computer Science and Technology，Guangdong University of Technology，Guangzhou Guangdong 510006，China
^2.Library，Guangdong University of Technology，Guangzhou Guangdong 510006，China
^3.Experimental Teaching Department，Guangdong University of Technology，Guangzhou Guangdong 510006，China

Received:2023-05-22 Revised:2023-07-07 Accepted:2023-07-14 Online:2023-08-01 Published:2024-04-10
Contact: Jiayu LIN
About author:GUO Jie， born in 1998， M. S. candidate. Her research interests include recommendation system， data mining.
LIN Jiayu， born in 1981， M. S.， associate research librarian. Her research interests include information retrieval analysis， data mining.
LIANG Zuhong， born in 1980， Ph. D.， professor of engineering. His research interests include machine learning， intelligent computing.
LUO Xiaobo， born in 1997， M. S. candidate. His research interests include sentiment analysis， text data mining.
SUN Haitao， born in 1999， M. S. candidate. His research interests include text data mining.
Supported by:
University-Industry Cooperation Collaborative Education Project of Ministry of Education(220901229305933)

基于知识感知和跨层次对比学习的推荐方法

郭洁¹, 林佳瑜²(), 梁祖红¹^,³, 罗孝波¹, 孙海涛¹

^1.广东工业大学计算机学院，广州 510006
^2.广东工业大学图书馆，广州 510006
^3.广东工业大学实验教学部，广州 510006

通讯作者: 林佳瑜
作者简介:郭洁（1998—），女，湖南常德人，硕士研究生，CCF会员，主要研究方向：推荐系统、数据挖掘
林佳瑜（1981—），女，广东惠州人，副研究馆员，硕士，主要研究方向：信息检索分析、数据挖掘 linjiayu@gdut.edu.cn
梁祖红（1980—），男，广东惠州人，教授级高级工程师，博士，主要研究方向：机器学习、智能计算
罗孝波（1997—），男，湖南娄底人，硕士研究生，主要研究方向：情感分析、文本数据挖掘
孙海涛（1999—），男，湖南常德人，硕士研究生，主要研究方向：文本数据挖掘。
基金资助:
教育部产学合作协同育人项目(220901229305933)

Abstract

Abstract:

As a kind of side information， Knowledge Graph （KG） can effectively improve the recommendation quality of recommendation models， but the existing knowledge-awareness recommendation methods based on Graph Neural Network （GNN） suffer from unbalanced utilization of node information. To address the above problem， a new recommendation method based on Knowledge?awareness and Cross-level Contrastive Learning （KCCL） was proposed. To alleviate the problem of unbalanced node information utilization caused by the sparse interaction data and noisy knowledge graph that deviate from the true representation of inter-node dependencies during information aggregation， a contrastive learning paradigm was introduced into knowledge-awareness recommendation model of GNN. Firstly， the user-item interaction graph and the item knowledge graph were integrated into a heterogeneous graph， and the node representations of users and items were realized by a GNN based on the graph attention mechanism. Secondly， consistent noise was added to the information propagation aggregation layer for data augmentation to obtain node representations of different levels， and the obtained outermost node representation was compared with the innermost node representation for cross-level contrastive learning. Finally， the supervised recommendation task and the contrastive learning assistance task were jointly optimized to obtain the final representation of each node. Experimental results on DBbook2014 and MovieLens-1m datasets show that compared to the second prior contrastive method， the Recall@10 of KCCL is improved by 3.66% and 0.66%， respectively， and the NDCG@10 is improved by 3.57% and 3.29%， respectively， which verifies the effectiveness of KCCL.

Key words: Knowledge Graph (KG), Graph Neural Network (GNN), knowledge-awareness, graph attention mechanism, contrastive learning, data augmentation

摘要：

知识图谱（KG）作为一种辅助信息能够有效提高推荐模型的推荐质量，但现有的基于图神经网络（GNN）的知识感知推荐模型存在节点信息利用不均衡问题。为此，提出一种基于知识感知和跨层次对比学习的推荐方法（KCCL）。所提方法在GNN的知识感知推荐模型基础上引入对比学习范式，以缓解稀疏的交互数据和嘈杂的KG在信息聚合时节点间依赖的关系偏离真实表示导致节点信息利用不均衡的问题。首先，将用户?物品交互图和物品知识图整合为一个异质图，并通过基于图注意力机制的GNN实现用户和物品的节点表示；其次，在信息传播聚合层中加入一致的噪声进行数据增强，得到不同阶层的节点表示，并将获得的最外层节点表示与最内层节点表示进行跨层次对比学习；最后，联合优化推荐监督任务和对比学习辅助任务，得到最终各节点表示。在DBbook2014和MovieLens-1m数据集上的实验结果显示，相较于次优对比方法，KCCL的Recall@10分别提升了3.66%和0.66%，NDCG@10分别提升了3.57%和3.29%，验证了KCCL的有效性。

关键词: 知识图谱, 图神经网络, 知识感知, 图注意力机制, 对比学习, 数据增强

CLC Number:

TP181

Jie GUO, Jiayu LIN, Zuhong LIANG, Xiaobo LUO, Haitao SUN. Recommendation method based on knowledge‑awareness and cross-level contrastive learning[J]. Journal of Computer Applications, 2024, 44(4): 1121-1127.

郭洁, 林佳瑜, 梁祖红, 罗孝波, 孙海涛. 基于知识感知和跨层次对比学习的推荐方法[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1121-1127.

Figures/Tables 10

Fig. 1 Framework of KCCL

Fig. 2 Cross-level contrastive learning mechanism

Tab. 1 Datasets used for experiment

数据集	用户数	物品数	交互数	稀疏率/%	实体数	关系数	三元组数
DBbook2014	5 576	2 680	65 961	99.60	13 882	13	34 511
MovieLens-1m	6 040	3 240	998 539	94.90	14 708	20	434 189

Tab. 2 Hyperparameters setting of KCCL

超参数名称	值	超参数名称	值
用户、项目嵌入维度	64	学习率	0.001
实体、关系嵌入维度	64	批处理大小	13 882
优化器	Adam	L2正则化系数	0.001

Tab. 3 Experimental results of different models

模型	DBbook2014		MovieLens-1m
模型	Recall@10	NDCG@10	Recall@10	NDCG@10
BPR	0.160 2	0.113 1	0.128 8	0.334 6
LightGCN	0.204 6	0.144 6	0.137 2	0.340 8
CKE	0.163 3	0.112 3	0.132 1	0.326 1
RippleNet	0.092 9	0.059 5	0.086 5	0.240 8
KGCN	0.144 0	0.096 2	0.126 5	0.318 6
KGAT	0.188 0	0.119 8	0.132 1	0.322 4
KGIN	0.196 7	0.132 0	0.150 9	0.346 5
CG-KGR	0.213 1	0.148 5	0.130 5	0.319 7
KCCL	0.220 9	0.153 8	0.151 9	0.357 9

Tab. 4 Results of ablation experiments

模型	DBbook2014		MovieLens-1m
模型	Recall@10	NDCG@10	Recall@10	NDCG@10
KCCL	0.220 9	0.153 8	0.151 9	0.357 9
KCCL-CL	0.218 0	0.147 0	0.143 6	0.351 8
KCCL-N	0.218 5	0.147 7	0.144 1	0.352 0

Tab. 5 Impact of parameter λ1 on Recall and NDCG

$λ 1$	DBbook2014		MovieLens-1m
$λ 1$	Recall@10	NDCG@10	Recall@10	NDCG@10
1×10^-1	0.193 8	0.143 9	0.146 5	0.325 7
1×10^-2	0.220 9	0.153 8	0.151 9	0.357 9
1×10^-3	0.217 8	0.149 3	0.145 7	0.351 8
1×10^-4	0.212 0	0.142 4	0.143 4	0.351 8

Tab. 5 Impact of parameter λ1 on Recall and NDCG

$λ 1$	DBbook2014		MovieLens-1m
$λ 1$	Recall@10	NDCG@10	Recall@10	NDCG@10
1×10^-1	0.193 8	0.143 9	0.146 5	0.325 7
1×10^-2	0.220 9	0.153 8	0.151 9	0.357 9
1×10^-3	0.217 8	0.149 3	0.145 7	0.351 8
1×10^-4	0.212 0	0.142 4	0.143 4	0.351 8

Tab. 6 Impact of parameter τ on Recall and NDCG

$τ$	DBbook2014		MovieLens-1m
$τ$	Recall@10	NDCG@10	Recall@10	NDCG@10
0.1	0.218 3	0.147 5	0.150 9	0.352 5
0.2	0.220 9	0.153 8	0.151 9	0.357 9
0.3	0.219 9	0.151 8	0.148 5	0.352 9
0.4	0.218 7	0.151 6	0.144 7	0.350 0
0.5	0.217 6	0.149 7	0.143 5	0.348 9

Tab. 6 Impact of parameter τ on Recall and NDCG

$τ$	DBbook2014		MovieLens-1m
$τ$	Recall@10	NDCG@10	Recall@10	NDCG@10
0.1	0.218 3	0.147 5	0.150 9	0.352 5
0.2	0.220 9	0.153 8	0.151 9	0.357 9
0.3	0.219 9	0.151 8	0.148 5	0.352 9
0.4	0.218 7	0.151 6	0.144 7	0.350 0
0.5	0.217 6	0.149 7	0.143 5	0.348 9

Tab. 7 Impact of parameter p on Recall and NDCG

$p$	DBbook2014		MovieLens-1m
$p$	Recall@10	NDCG@10	Recall@10	NDCG@10
0.1	0.217 9	0.151 9	0.151 3	0.356 5
0.2	0.218 3	0.153 4	0.151 9	0.357 9
0.3	0.220 9	0.153 8	0.151 2	0.353 0
0.4	0.220 1	0.153 5	0.150 6	0.353 1
0.5	0.219 0	0.153 0	0.149 6	0.350 1

Tab. 7 Impact of parameter p on Recall and NDCG

$p$	DBbook2014		MovieLens-1m
$p$	Recall@10	NDCG@10	Recall@10	NDCG@10
0.1	0.217 9	0.151 9	0.151 3	0.356 5
0.2	0.218 3	0.153 4	0.151 9	0.357 9
0.3	0.220 9	0.153 8	0.151 2	0.353 0
0.4	0.220 1	0.153 5	0.150 6	0.353 1
0.5	0.219 0	0.153 0	0.149 6	0.350 1

Tab. 8 Influence of noisy knowledge graph on Recall and NDCG

模型	5%随机噪声		10%随机噪声
模型	Recall@10	NDCG@10	Recall@10	NDCG@10
KGAT	0.131 1	0.319 3	0.125 9	0.316 8
KGIN	0.148 7	0.342 1	0.144 8	0.340 2
KCCL	0.149 6	0.353 5	0.149 3	0.351 7

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Recommendation method based on knowledge‑awareness and cross-level contrastive learning

基于知识感知和跨层次对比学习的推荐方法

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