Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1121-1127.DOI: 10.11772/j.issn.1001-9081.2023050613
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Jie GUO1, Jiayu LIN2(), Zuhong LIANG1,3, Xiaobo LUO1, Haitao SUN1
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.Supported by:
郭洁1, 林佳瑜2(), 梁祖红1,3, 罗孝波1, 孙海涛1
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、数据挖掘基金资助:
CLC Number:
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.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050613
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. 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 |
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. 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 |
模型 | DBbook2014 | MovieLens-1m | ||
---|---|---|---|---|
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 | ||
CG-KGR | 0.130 5 | 0.319 7 | ||
KCCL | 0.220 9 | 0.153 8 | 0.151 9 | 0.357 9 |
Tab. 3 Experimental results of different models
模型 | DBbook2014 | MovieLens-1m | ||
---|---|---|---|---|
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 | ||
CG-KGR | 0.130 5 | 0.319 7 | ||
KCCL | 0.220 9 | 0.153 8 | 0.151 9 | 0.357 9 |
模型 | DBbook2014 | MovieLens-1m | ||
---|---|---|---|---|
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. 4 Results of ablation experiments
模型 | DBbook2014 | MovieLens-1m | ||
---|---|---|---|---|
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 |
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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 |
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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 |
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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
DBbook2014 | MovieLens-1m | |||
---|---|---|---|---|
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 |
模型 | 5%随机噪声 | 10%随机噪声 | ||
---|---|---|---|---|
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 |
Tab. 8 Influence of noisy knowledge graph on Recall and NDCG
模型 | 5%随机噪声 | 10%随机噪声 | ||
---|---|---|---|---|
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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