Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (10): 3136-3141.DOI: 10.11772/j.issn.1001-9081.2022101489
Special Issue: 数据科学与技术
• Data science and technology • Previous Articles Next Articles
Xiaohan YANG, Guosheng HAO, Xiehua ZHANG(), Zihao YANG
Received:
2022-10-11
Revised:
2023-01-13
Accepted:
2023-01-16
Online:
2023-04-12
Published:
2023-10-10
Contact:
Xiehua ZHANG
About author:
YANG Xiaohan, born in 1995, M. S. candidate. Her research interests include machine learning, recommender system.Supported by:
通讯作者:
张谢华
作者简介:
杨晓菡(1995—),女,江苏徐州人,硕士研究生,主要研究方向:机器学习、推荐系统基金资助:
CLC Number:
Xiaohan YANG, Guosheng HAO, Xiehua ZHANG, Zihao YANG. Collaborative filtering algorithm based on collaborative training and Boosting[J]. Journal of Computer Applications, 2023, 43(10): 3136-3141.
杨晓菡, 郝国生, 张谢华, 杨子豪. 基于协同训练与Boosting的协同过滤算法[J]. 《计算机应用》唯一官方网站, 2023, 43(10): 3136-3141.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022101489
数据集 | 用户数 | 物品数 | 评分数 | 稀疏度/% |
---|---|---|---|---|
ML-100K | 943 | 1 682 | 100 000 | 93.695 |
ml-latest-small | 9 742 | 610 | 100 837 | 98.303 |
Filmtrust | 1 508 | 2 071 | 35 497 | 98.863 |
CiaoDVD | 17 615 | 16 121 | 72 665 | 99.974 |
Tab. 1 Scoring data statistics of datasets
数据集 | 用户数 | 物品数 | 评分数 | 稀疏度/% |
---|---|---|---|---|
ML-100K | 943 | 1 682 | 100 000 | 93.695 |
ml-latest-small | 9 742 | 610 | 100 837 | 98.303 |
Filmtrust | 1 508 | 2 071 | 35 497 | 98.863 |
CiaoDVD | 17 615 | 16 121 | 72 665 | 99.974 |
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD | 0.927 6 | 0.003 9 | 0.731 7 | 0.003 8 | 0.867 3 | 0.002 9 | 0.664 5 | 0.001 2 | 0.786 8 | 0.007 7 | 0.608 3 | 0.007 0 | 0.939 5 | 0.004 9 | 0.725 3 | 0.005 8 |
SVD++ | 0.910 5 | 0.006 0 | 0.714 5 | 0.005 4 | 0.857 2 | 0.003 0 | 0.656 0 | 0.001 9 | 0.783 8 | 0.006 1 | 0.602 5 | 0.006 3 | 0.934 6 | 0.002 8 | 0.717 8 | 0.004 4 |
CFCTB | 0.898 5 | 0.004 8 | 0.702 4 | 0.005 0 | 0.845 2 | 0.004 0 | 0.643 2 | 0.002 6 | 0.772 9 | 0.008 9 | 0.588 5 | 0.008 4 | 0.919 4 | 0.004 4 | 0.699 9 | 0.005 2 |
Tab. 2 Performance comparison of three recommendation algorithms under combination of SVD and SVD++
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD | 0.927 6 | 0.003 9 | 0.731 7 | 0.003 8 | 0.867 3 | 0.002 9 | 0.664 5 | 0.001 2 | 0.786 8 | 0.007 7 | 0.608 3 | 0.007 0 | 0.939 5 | 0.004 9 | 0.725 3 | 0.005 8 |
SVD++ | 0.910 5 | 0.006 0 | 0.714 5 | 0.005 4 | 0.857 2 | 0.003 0 | 0.656 0 | 0.001 9 | 0.783 8 | 0.006 1 | 0.602 5 | 0.006 3 | 0.934 6 | 0.002 8 | 0.717 8 | 0.004 4 |
CFCTB | 0.898 5 | 0.004 8 | 0.702 4 | 0.005 0 | 0.845 2 | 0.004 0 | 0.643 2 | 0.002 6 | 0.772 9 | 0.008 9 | 0.588 5 | 0.008 4 | 0.919 4 | 0.004 4 | 0.699 9 | 0.005 2 |
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD | 0.932 7 | 0.002 6 | 0.735 2 | 0.003 0 | 0.866 9 | 0.011 6 | 0.666 0 | 0.008 0 | 0.793 9 | 0.007 5 | 0.612 4 | 0.006 0 | 0.930 6 | 0.007 8 | 0.718 5 | 0.007 1 |
KNNBaseline | 0.931 5 | 0.001 8 | 0.732 8 | 0.002 3 | 0.869 7 | 0.010 5 | 0.665 4 | 0.007 5 | 0.816 1 | 0.007 1 | 0.634 1 | 0.002 6 | 0.963 4 | 0.007 3 | 0.729 0 | 0.005 9 |
CFCTB | 0.915 0 | 0.002 7 | 0.719 9 | 0.004 0 | 0.851 7 | 0.006 6 | 0.650 8 | 0.008 5 | 0.784 8 | 0.007 1 | 0.599 1 | 0.003 9 | 0.918 8 | 0.006 9 | 0.701 5 | 0.005 5 |
Tab. 3 Performance comparison of three recommendation algorithms under combination of SVD and KNNBaseline
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD | 0.932 7 | 0.002 6 | 0.735 2 | 0.003 0 | 0.866 9 | 0.011 6 | 0.666 0 | 0.008 0 | 0.793 9 | 0.007 5 | 0.612 4 | 0.006 0 | 0.930 6 | 0.007 8 | 0.718 5 | 0.007 1 |
KNNBaseline | 0.931 5 | 0.001 8 | 0.732 8 | 0.002 3 | 0.869 7 | 0.010 5 | 0.665 4 | 0.007 5 | 0.816 1 | 0.007 1 | 0.634 1 | 0.002 6 | 0.963 4 | 0.007 3 | 0.729 0 | 0.005 9 |
CFCTB | 0.915 0 | 0.002 7 | 0.719 9 | 0.004 0 | 0.851 7 | 0.006 6 | 0.650 8 | 0.008 5 | 0.784 8 | 0.007 1 | 0.599 1 | 0.003 9 | 0.918 8 | 0.006 9 | 0.701 5 | 0.005 5 |
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD++ | 0.913 0 | 0.003 7 | 0.716 3 | 0.003 4 | 0.867 3 | 0.009 7 | 0.655 2 | 0.005 9 | 0.790 2 | 0.011 6 | 0.607 7 | 0.009 2 | 0.938 4 | 0.008 2 | 0.721 9 | 0.007 1 |
KNNBaseline | 0.925 3 | 0.003 2 | 0.729 8 | 0.002 7 | 0.867 8 | 0.008 2 | 0.662 6 | 0.004 6 | 0.811 5 | 0.015 0 | 0.616 8 | 0.010 7 | 0.975 2 | 0.007 6 | 0.737 6 | 0.005 8 |
CFCTB | 0.897 3 | 0.004 1 | 0.702 9 | 0.003 4 | 0.853 6 | 0.008 9 | 0.645 5 | 0.004 6 | 0.811 5 | 0.015 0 | 0.596 6 | 0.010 4 | 0.925 9 | 0.009 0 | 0.700 5 | 0.008 9 |
Tab. 4 Performance comparison of three recommendation algorithms under combination of SVD++ and KNNBaseline
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
SVD++ | 0.913 0 | 0.003 7 | 0.716 3 | 0.003 4 | 0.867 3 | 0.009 7 | 0.655 2 | 0.005 9 | 0.790 2 | 0.011 6 | 0.607 7 | 0.009 2 | 0.938 4 | 0.008 2 | 0.721 9 | 0.007 1 |
KNNBaseline | 0.925 3 | 0.003 2 | 0.729 8 | 0.002 7 | 0.867 8 | 0.008 2 | 0.662 6 | 0.004 6 | 0.811 5 | 0.015 0 | 0.616 8 | 0.010 7 | 0.975 2 | 0.007 6 | 0.737 6 | 0.005 8 |
CFCTB | 0.897 3 | 0.004 1 | 0.702 9 | 0.003 4 | 0.853 6 | 0.008 9 | 0.645 5 | 0.004 6 | 0.811 5 | 0.015 0 | 0.596 6 | 0.010 4 | 0.925 9 | 0.009 0 | 0.700 5 | 0.008 9 |
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
GraphRec | 0.908 6 | 0.002 2 | 0.720 6 | 0.002 3 | 0.873 2 | 0.009 1 | 0.679 9 | 0.010 6 | 0.821 3 | 0.005 9 | 0.637 1 | 0.004 7 | 0.953 2 | 0.008 7 | 0.719 7 | 0.007 4 |
ECoRec | 0.916 1 | 0.007 2 | 0.719 1 | 0.008 9 | 0.840 1 | 0.004 9 | 0.645 9 | 0.003 1 | 0.783 6 | 0.009 8 | 0.613 2 | 0.009 2 | 0.993 1 | 0.006 9 | 0.734 1 | 0.008 1 |
GLocal-K | 0.870 6 | 0.004 9 | 0.679 9 | 0.006 2 | 0.863 4 | 0.011 3 | 0.674 3 | 0.009 4 | 0.788 0 | 0.007 0 | 0.614 9 | 0.003 8 | 0.995 8 | 0.008 2 | 0.734 7 | 0.007 3 |
SSEF | 0.899 9 | 0.006 6 | 0.703 6 | 0.007 0 | 0.845 7 | 0.010 2 | 0.644 2 | 0.008 7 | 0.758 4 | 0.013 6 | 0.589 8 | 0.012 1 | 0.932 3 | 0.009 1 | 0.932 3 | 0.009 1 |
CFCTB | 0.898 5 | 0.004 8 | 0.702 4 | 0.005 0 | 0.845 2 | 0.004 0 | 0.643 2 | 0.002 6 | 0.772 9 | 0.008 9 | 0.588 5 | 0.008 4 | 0.919 4 | 0.004 4 | 0.699 9 | 0.005 2 |
Tab. 5 Performance comparison between the proposed algorithm and semi-supervised integration based recommendation algorithms
算法 | ML-100K | ml-latest-small | Filmtrust | CiaoDVD | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | |||||||||
均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | 均值 | 标准差 | |
GraphRec | 0.908 6 | 0.002 2 | 0.720 6 | 0.002 3 | 0.873 2 | 0.009 1 | 0.679 9 | 0.010 6 | 0.821 3 | 0.005 9 | 0.637 1 | 0.004 7 | 0.953 2 | 0.008 7 | 0.719 7 | 0.007 4 |
ECoRec | 0.916 1 | 0.007 2 | 0.719 1 | 0.008 9 | 0.840 1 | 0.004 9 | 0.645 9 | 0.003 1 | 0.783 6 | 0.009 8 | 0.613 2 | 0.009 2 | 0.993 1 | 0.006 9 | 0.734 1 | 0.008 1 |
GLocal-K | 0.870 6 | 0.004 9 | 0.679 9 | 0.006 2 | 0.863 4 | 0.011 3 | 0.674 3 | 0.009 4 | 0.788 0 | 0.007 0 | 0.614 9 | 0.003 8 | 0.995 8 | 0.008 2 | 0.734 7 | 0.007 3 |
SSEF | 0.899 9 | 0.006 6 | 0.703 6 | 0.007 0 | 0.845 7 | 0.010 2 | 0.644 2 | 0.008 7 | 0.758 4 | 0.013 6 | 0.589 8 | 0.012 1 | 0.932 3 | 0.009 1 | 0.932 3 | 0.009 1 |
CFCTB | 0.898 5 | 0.004 8 | 0.702 4 | 0.005 0 | 0.845 2 | 0.004 0 | 0.643 2 | 0.002 6 | 0.772 9 | 0.008 9 | 0.588 5 | 0.008 4 | 0.919 4 | 0.004 4 | 0.699 9 | 0.005 2 |
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