Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 469-476.DOI: 10.11772/j.issn.1001-9081.2023020180
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Zhiwen JING, Yujia ZHANG, Boting SUN, Hao GUO()
Received:
2023-02-27
Revised:
2023-04-20
Accepted:
2023-05-04
Online:
2024-02-22
Published:
2024-02-10
Contact:
Hao GUO
About author:
JING Zhiwen, born in 1997, M. S. candidate. His research interests include recommendation system, deep learning.Supported by:
通讯作者:
郭浩
作者简介:
荆智文(1997—),男,山西运城人,硕士研究生,主要研究方向:推荐系统、深度学习基金资助:
CLC Number:
Zhiwen JING, Yujia ZHANG, Boting SUN, Hao GUO. Two-stage recommendation algorithm of Siamese graph convolutional neural network[J]. Journal of Computer Applications, 2024, 44(2): 469-476.
荆智文, 张屿佳, 孙伯廷, 郭浩. 二阶段孪生图卷积神经网络推荐算法[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 469-476.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023020180
符号 | 定义 |
---|---|
用户集合 | |
商品集合 | |
用户 | |
用户及商品文档集合 | |
用户 | |
商品 | |
用户侧深度神经网络输出的特征矩阵 | |
商品侧深度神经网络输出的特征矩阵 | |
评分矩阵 | |
单个用户子图 | |
深度神经网络参数矩阵 | |
孪生卷积神经网络参数矩阵 |
Tab. 1 Definition of important notations
符号 | 定义 |
---|---|
用户集合 | |
商品集合 | |
用户 | |
用户及商品文档集合 | |
用户 | |
商品 | |
用户侧深度神经网络输出的特征矩阵 | |
商品侧深度神经网络输出的特征矩阵 | |
评分矩阵 | |
单个用户子图 | |
深度神经网络参数矩阵 | |
孪生卷积神经网络参数矩阵 |
数据集 | 用户数 | 商品数 | 评分数 | 评分范围 |
---|---|---|---|---|
MovieLens | 10 000 | 9 395 | 1 462 905 | [0.5, 5] |
豆瓣电影 | 2 712 | 34 893 | 1 278 401 | [ |
Tab. 2 Statistics of experimental datasets
数据集 | 用户数 | 商品数 | 评分数 | 评分范围 |
---|---|---|---|---|
MovieLens | 10 000 | 9 395 | 1 462 905 | [0.5, 5] |
豆瓣电影 | 2 712 | 34 893 | 1 278 401 | [ |
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TF⁃IDF | 0.013 4 | 0.024 2 | 0.035 9 | 0.021 3 | 0.044 2 | 0.052 2 | 0.035 1 | 0.047 9 | 0.068 9 |
FM | 0.018 9 | 0.040 6 | 0.063 4 | 0.031 2 | 0.050 3 | 0.065 9 | 0.058 9 | 0.063 6 | 0.071 2 |
YoutubeDNN | 0.021 2 | 0.037 8 | 0.059 9 | 0.030 3 | 0.048 7 | 0.059 3 | 0.052 4 | 0.059 1 | 0.072 3 |
DSSM | 0.029 4 | 0.046 4 | 0.073 2 | 0.037 5 | 0.058 7 | 0.067 3 | 0.062 5 | 0.069 7 | 0.082 1 |
STAN | 0.033 2 | 0.048 8 | 0.077 5 | 0.043 0 | 0.062 9 | 0.073 3 | 0.067 5 | 0.075 8 | 0.087 4 |
HIRS | 0.034 9 | 0.049 9 | 0.080 3 | 0.045 7 | 0.064 6 | 0.076 9 | 0.068 6 | 0.078 8 | 0.089 4 |
DAT | 0.038 8 | 0.051 9 | 0.083 6 | 0.053 6 | 0.068 8 | 0.082 4 | 0.071 1 | 0.081 4 | 0.092 3 |
TSN(GC) | 0.040 3 | 0.055 2 | 0.087 9 | 0.056 8 | 0.072 2 | 0.092 9 | 0.074 1 | 0.080 2 | 0.093 6 |
Tab. 3 Experimental results of different models on MovieLens dataset
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TF⁃IDF | 0.013 4 | 0.024 2 | 0.035 9 | 0.021 3 | 0.044 2 | 0.052 2 | 0.035 1 | 0.047 9 | 0.068 9 |
FM | 0.018 9 | 0.040 6 | 0.063 4 | 0.031 2 | 0.050 3 | 0.065 9 | 0.058 9 | 0.063 6 | 0.071 2 |
YoutubeDNN | 0.021 2 | 0.037 8 | 0.059 9 | 0.030 3 | 0.048 7 | 0.059 3 | 0.052 4 | 0.059 1 | 0.072 3 |
DSSM | 0.029 4 | 0.046 4 | 0.073 2 | 0.037 5 | 0.058 7 | 0.067 3 | 0.062 5 | 0.069 7 | 0.082 1 |
STAN | 0.033 2 | 0.048 8 | 0.077 5 | 0.043 0 | 0.062 9 | 0.073 3 | 0.067 5 | 0.075 8 | 0.087 4 |
HIRS | 0.034 9 | 0.049 9 | 0.080 3 | 0.045 7 | 0.064 6 | 0.076 9 | 0.068 6 | 0.078 8 | 0.089 4 |
DAT | 0.038 8 | 0.051 9 | 0.083 6 | 0.053 6 | 0.068 8 | 0.082 4 | 0.071 1 | 0.081 4 | 0.092 3 |
TSN(GC) | 0.040 3 | 0.055 2 | 0.087 9 | 0.056 8 | 0.072 2 | 0.092 9 | 0.074 1 | 0.080 2 | 0.093 6 |
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TF⁃IDF | 0.016 7 | 0.023 2 | 0.031 7 | 0.012 1 | 0.020 4 | 0.028 9 | 0.015 2 | 0.022 7 | 0.031 1 |
FM | 0.022 9 | 0.030 4 | 0.042 2 | 0.019 7 | 0.027 3 | 0.040 2 | 0.022 9 | 0.030 2 | 0.043 1 |
YoutubeDNN | 0.034 4 | 0.046 7 | 0.050 3 | 0.024 4 | 0.035 6 | 0.047 7 | 0.033 4 | 0.041 4 | 0.050 7 |
DSSM | 0.051 1 | 0.060 3 | 0.065 9 | 0.033 2 | 0.046 5 | 0.052 6 | 0.049 7 | 0.050 3 | 0.055 8 |
STAN | 0.052 9 | 0.066 7 | 0.068 7 | 0.039 0 | 0.051 6 | 0.060 9 | 0.050 8 | 0.054 4 | 0.062 9 |
HIRS | 0.054 9 | 0.068 1 | 0.071 1 | 0.041 3 | 0.053 9 | 0.062 2 | 0.052 7 | 0.056 6 | 0.064 7 |
DAT | 0.059 4 | 0.071 1 | 0.077 2 | 0.046 7 | 0.067 6 | 0.068 6 | 0.056 6 | 0.067 6 | 0.069 6 |
TSN(GC) | 0.065 3 | 0.078 7 | 0.083 9 | 0.057 9 | 0.075 3 | 0.078 8 | 0.067 0 | 0.074 5 | 0.079 8 |
Tab. 4 Experimental results of different models on Douban movie dataset
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TF⁃IDF | 0.016 7 | 0.023 2 | 0.031 7 | 0.012 1 | 0.020 4 | 0.028 9 | 0.015 2 | 0.022 7 | 0.031 1 |
FM | 0.022 9 | 0.030 4 | 0.042 2 | 0.019 7 | 0.027 3 | 0.040 2 | 0.022 9 | 0.030 2 | 0.043 1 |
YoutubeDNN | 0.034 4 | 0.046 7 | 0.050 3 | 0.024 4 | 0.035 6 | 0.047 7 | 0.033 4 | 0.041 4 | 0.050 7 |
DSSM | 0.051 1 | 0.060 3 | 0.065 9 | 0.033 2 | 0.046 5 | 0.052 6 | 0.049 7 | 0.050 3 | 0.055 8 |
STAN | 0.052 9 | 0.066 7 | 0.068 7 | 0.039 0 | 0.051 6 | 0.060 9 | 0.050 8 | 0.054 4 | 0.062 9 |
HIRS | 0.054 9 | 0.068 1 | 0.071 1 | 0.041 3 | 0.053 9 | 0.062 2 | 0.052 7 | 0.056 6 | 0.064 7 |
DAT | 0.059 4 | 0.071 1 | 0.077 2 | 0.046 7 | 0.067 6 | 0.068 6 | 0.056 6 | 0.067 6 | 0.069 6 |
TSN(GC) | 0.065 3 | 0.078 7 | 0.083 9 | 0.057 9 | 0.075 3 | 0.078 8 | 0.067 0 | 0.074 5 | 0.079 8 |
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TSN(w/o TS,GC) | 0.032 2 | 0.044 5 | 0.049 6 | 0.022 1 | 0.033 9 | 0.043 5 | 0.031 2 | 0.038 7 | 0.047 5 |
TSN(U⁃GC) | 0.057 3 | 0.067 4 | 0.071 2 | 0.045 5 | 0.060 6 | 0.062 3 | 0.056 4 | 0.057 6 | 0.064 1 |
TSN(I⁃GC) | 0.055 2 | 0.063 3 | 0.070 2 | 0.045 9 | 0.061 7 | 0.063 3 | 0.054 8 | 0.057 9 | 0.062 7 |
TSN(TS) | 0.065 0 | 0.079 4 | 0.082 1 | 0.057 6 | 0.075 0 | 0.077 5 | 0.066 7 | 0.074 1 | 0.080 7 |
TSN(GC) | 0.065 3 | 0.078 7 | 0.083 9 | 0.057 9 | 0.075 3 | 0.078 8 | 0.067 0 | 0.074 5 | 0.079 8 |
Tab. 5 Experimental results of TSN(GC) and its variants on Douban movie dataset
模型 | HR@N | NDCG@N | MRR@N | ||||||
---|---|---|---|---|---|---|---|---|---|
N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | N=10 | N=50 | N=100 | |
TSN(w/o TS,GC) | 0.032 2 | 0.044 5 | 0.049 6 | 0.022 1 | 0.033 9 | 0.043 5 | 0.031 2 | 0.038 7 | 0.047 5 |
TSN(U⁃GC) | 0.057 3 | 0.067 4 | 0.071 2 | 0.045 5 | 0.060 6 | 0.062 3 | 0.056 4 | 0.057 6 | 0.064 1 |
TSN(I⁃GC) | 0.055 2 | 0.063 3 | 0.070 2 | 0.045 9 | 0.061 7 | 0.063 3 | 0.054 8 | 0.057 9 | 0.062 7 |
TSN(TS) | 0.065 0 | 0.079 4 | 0.082 1 | 0.057 6 | 0.075 0 | 0.077 5 | 0.066 7 | 0.074 1 | 0.080 7 |
TSN(GC) | 0.065 3 | 0.078 7 | 0.083 9 | 0.057 9 | 0.075 3 | 0.078 8 | 0.067 0 | 0.074 5 | 0.079 8 |
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