Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (11): 3443-3448.DOI: 10.11772/j.issn.1001-9081.2022101628
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Chuyuan WEI1(), Mengke WANG2, Chuanhao HU2, Guangqi ZHANG2
Received:
2022-10-31
Revised:
2023-03-17
Accepted:
2023-04-04
Online:
2023-05-24
Published:
2023-11-10
Contact:
Chuyuan WEI
About author:
WEI Chuyuan, born in 1977, Ph. D., associate professor. His research interests include natural language processing, data mining.Supported by:
通讯作者:
魏楚元
作者简介:
魏楚元(1977—),男,湖北武汉人,副教授,博士,CCF高级会员,主要研究方向:自然语言处理、数据挖掘 weichuyuan@bucea.edu.cn基金资助:
CLC Number:
Chuyuan WEI, Mengke WANG, Chuanhao HU, Guangqi ZHANG. Deep review attention neural network model for enhancing explainability of recommendation system[J]. Journal of Computer Applications, 2023, 43(11): 3443-3448.
魏楚元, 王梦珂, 户传豪, 张桄齐. 增强推荐系统可解释性的深度评论注意力神经网络模型[J]. 《计算机应用》唯一官方网站, 2023, 43(11): 3443-3448.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022101628
数据集 | 用户数 | 项目数 | 评论交互数 |
---|---|---|---|
M-I | 1 429 | 900 | 10 261 |
Patio | 1 686 | 962 | 13 254 |
Automotive | 2 928 | 1 835 | 20 441 |
Beauty | 22 362 | 12 101 | 198 502 |
Tab. 1 Information of datasets
数据集 | 用户数 | 项目数 | 评论交互数 |
---|---|---|---|
M-I | 1 429 | 900 | 10 261 |
Patio | 1 686 | 962 | 13 254 |
Automotive | 2 928 | 1 835 | 20 441 |
Beauty | 22 362 | 12 101 | 198 502 |
模型 | M-I | Automotive | Patio | Beauty |
---|---|---|---|---|
PMF | 1.135 4 | 1.146 8 | 1.250 1 | 1.247 6 |
SVD++ | 1.019 6 | 0.962 1 | 1.102 4 | 1.135 2 |
DeepCF | 1.009 7 | 0.986 2 | 1.081 8 | 1.118 8 |
DeepCoNN | 1.028 6 | 0.933 3 | 1.043 2 | 1.126 9 |
TEM | 0.984 0 | 0.907 6 | 1.013 4 | 1.120 3 |
DER | 0.973 0 | 0.906 7 | 1.003 8 | 1.119 7 |
DRANN | 0.947 1 | 0.902 1 | 1.003 7 | 1.112 4 |
Tab. 2 RMSE comparison of different models on Amazon dataset
模型 | M-I | Automotive | Patio | Beauty |
---|---|---|---|---|
PMF | 1.135 4 | 1.146 8 | 1.250 1 | 1.247 6 |
SVD++ | 1.019 6 | 0.962 1 | 1.102 4 | 1.135 2 |
DeepCF | 1.009 7 | 0.986 2 | 1.081 8 | 1.118 8 |
DeepCoNN | 1.028 6 | 0.933 3 | 1.043 2 | 1.126 9 |
TEM | 0.984 0 | 0.907 6 | 1.013 4 | 1.120 3 |
DER | 0.973 0 | 0.906 7 | 1.003 8 | 1.119 7 |
DRANN | 0.947 1 | 0.902 1 | 1.003 7 | 1.112 4 |
数据集 | RMSE | |
---|---|---|
未添加编码器 | 添加编码器 | |
Patio | 1.320 0 | 1.003 7 |
Beauty | 1.257 2 | 1.112 4 |
M-I | 1.032 0 | 0.971 2 |
Tab. 3 Influence of deep encoder on RMSE of model
数据集 | RMSE | |
---|---|---|
未添加编码器 | 添加编码器 | |
Patio | 1.320 0 | 1.003 7 |
Beauty | 1.257 2 | 1.112 4 |
M-I | 1.032 0 | 0.971 2 |
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