Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3090-3096.DOI: 10.11772/j.issn.1001-9081.2023091352
• Data science and technology • Previous Articles Next Articles
Xingyao YANG1(), Hongtao SHEN1, Zulian ZHANG2, Jiong YU1, Jiaying CHEN1, Dongxiao WANG1
Received:
2023-10-07
Revised:
2023-12-04
Accepted:
2023-12-05
Online:
2023-12-20
Published:
2024-10-10
Contact:
Xingyao YANG
About author:
SHEN Hongtao, born in 2001, M. S. candidate. His research interests include recommender system.Supported by:
杨兴耀1(), 沈洪涛1, 张祖莲2, 于炯1, 陈嘉颖1, 王东晓1
通讯作者:
杨兴耀
作者简介:
杨兴耀(1984—),男,湖北襄阳人,副教授,博士,CCF会员,主要研究方向:推荐系统、大数据、信任计算 yangxy@xju.edu.cn基金资助:
CLC Number:
Xingyao YANG, Hongtao SHEN, Zulian ZHANG, Jiong YU, Jiaying CHEN, Dongxiao WANG. Sequential recommendation based on hierarchical filter and temporal convolution enhanced self-attention network[J]. Journal of Computer Applications, 2024, 44(10): 3090-3096.
杨兴耀, 沈洪涛, 张祖莲, 于炯, 陈嘉颖, 王东晓. 基于层级过滤器和时间卷积增强自注意力网络的序列推荐[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3090-3096.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023091352
子数据集 | 用户数 | 项目数 | 交互数 | 稀疏性/% |
---|---|---|---|---|
Beauty | 22 363 | 12 101 | 198 502 | 0.07 |
Clothing | 39 387 | 23 033 | 278 677 | 0.30 |
Sports | 35 598 | 18 357 | 296 337 | 0.05 |
Tab. 1 Statistics of experimental datasets
子数据集 | 用户数 | 项目数 | 交互数 | 稀疏性/% |
---|---|---|---|---|
Beauty | 22 363 | 12 101 | 198 502 | 0.07 |
Clothing | 39 387 | 23 033 | 278 677 | 0.30 |
Sports | 35 598 | 18 357 | 296 337 | 0.05 |
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.012 0 | 0.029 9 | 0.004 0 | 0.005 3 |
GRU4Rec | 0.016 4 | 0.036 5 | 0.008 6 | 0.014 2 |
SASRec | 0.036 5 | 0.062 7 | 0.023 6 | 0.028 1 |
BERT4Rec | 0.019 3 | 0.040 1 | 0.018 7 | 0.025 4 |
FMLP-Rec | 0.039 8 | 0.063 2 | 0.025 8 | 0.033 3 |
CL4Rec | 0.040 1 | 0.068 3 | 0.022 3 | 0.031 7 |
DuoRec | ||||
FTARec | 0.058 1 | 0.090 0 | 0.035 7 | 0.045 9 |
Tab. 2 Experimental results of different models on Beauty dataset
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.012 0 | 0.029 9 | 0.004 0 | 0.005 3 |
GRU4Rec | 0.016 4 | 0.036 5 | 0.008 6 | 0.014 2 |
SASRec | 0.036 5 | 0.062 7 | 0.023 6 | 0.028 1 |
BERT4Rec | 0.019 3 | 0.040 1 | 0.018 7 | 0.025 4 |
FMLP-Rec | 0.039 8 | 0.063 2 | 0.025 8 | 0.033 3 |
CL4Rec | 0.040 1 | 0.068 3 | 0.022 3 | 0.031 7 |
DuoRec | ||||
FTARec | 0.058 1 | 0.090 0 | 0.035 7 | 0.045 9 |
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.006 7 | 0.009 4 | 0.005 2 | 0.006 9 |
GRU4Rec | 0.009 5 | 0.016 5 | 0.006 1 | 0.008 3 |
SASRec | 0.016 8 | 0.027 2 | 0.009 1 | 0.012 4 |
BERT4Rec | 0.012 5 | 0.020 8 | 0.007 5 | 0.010 2 |
FMLP-Rec | 0.012 6 | 0.020 6 | 0.008 2 | 0.010 7 |
CL4Rec | 0.016 8 | 0.026 6 | 0.009 0 | 0.012 1 |
DuoRec | ||||
FTARec | 0.020 8 | 0.033 3 | 0.011 6 | 0.015 6 |
Tab. 3 Experimental results of different models on Clothing dataset
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.006 7 | 0.009 4 | 0.005 2 | 0.006 9 |
GRU4Rec | 0.009 5 | 0.016 5 | 0.006 1 | 0.008 3 |
SASRec | 0.016 8 | 0.027 2 | 0.009 1 | 0.012 4 |
BERT4Rec | 0.012 5 | 0.020 8 | 0.007 5 | 0.010 2 |
FMLP-Rec | 0.012 6 | 0.020 6 | 0.008 2 | 0.010 7 |
CL4Rec | 0.016 8 | 0.026 6 | 0.009 0 | 0.012 1 |
DuoRec | ||||
FTARec | 0.020 8 | 0.033 3 | 0.011 6 | 0.015 6 |
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.009 2 | 0.018 8 | 0.004 0 | 0.005 1 |
GRU4Rec | 0.013 7 | 0.027 4 | 0.009 6 | 0.013 7 |
SASRec | 0.021 8 | 0.033 6 | 0.012 7 | 0.016 9 |
BERT4Rec | 0.017 6 | 0.032 6 | 0.010 5 | 0.015 3 |
FMLP-Rec | 0.021 8 | 0.034 4 | 0.014 4 | 0.018 5 |
CL4Rec | 0.022 7 | 0.037 4 | 0.012 9 | 0.019 7 |
DuoRec | ||||
FTARec | 0.034 0 | 0.053 6 | 0.020 5 | 0.026 7 |
Tab. 4 Experimental results of different models on Sports dataset
模型 | HR@5 | HR@10 | NDCG@5 | NDCG@10 |
---|---|---|---|---|
BPR-MF | 0.009 2 | 0.018 8 | 0.004 0 | 0.005 1 |
GRU4Rec | 0.013 7 | 0.027 4 | 0.009 6 | 0.013 7 |
SASRec | 0.021 8 | 0.033 6 | 0.012 7 | 0.016 9 |
BERT4Rec | 0.017 6 | 0.032 6 | 0.010 5 | 0.015 3 |
FMLP-Rec | 0.021 8 | 0.034 4 | 0.014 4 | 0.018 5 |
CL4Rec | 0.022 7 | 0.037 4 | 0.012 9 | 0.019 7 |
DuoRec | ||||
FTARec | 0.034 0 | 0.053 6 | 0.020 5 | 0.026 7 |
1 | 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述[J].计算机学报,2018,41(7):1619-1647. |
HUANG L W, JIANG B T, LYU S Y, et al. Survey on deep learning based recommender systems [J]. Chinese Journal of Computers, 2018, 41(7): 1619-1647. | |
2 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 6000-6010. |
3 | KANG W-C, McAULEY J. Self-attentive sequential recommendation [C]// Proceedings of the 2018 IEEE International Conference on Data Mining. Piscataway: IEEE, 2018: 197-206. |
4 | SUN F, LIU J, WU J, et al. BERT4Rec: sequential recommendation with bidirectional encoder representations from Transformer [C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 1441-1450. |
5 | FAN X, LIU Z, LIAN J, et al. Lighter and better: low-rank decomposed self-attention networks for next-item recommendation [C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development of Information Retrieval. New York: ACM, 2021: 1733-1737. |
6 | LI W, GOU J, FAN Z. Session-based recommendation with temporal convolutional network to balance numerical gaps [J]. Neurocomputing, 2022, 493: 166-175. |
7 | BAI S, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling [EB/OL]. [2023-08-23]. . |
8 | FENG S, LI X, ZENG Y, et al. Personalized ranking metric embedding for next new POI recommendation [C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2069-2075. |
9 | HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks [EB/OL]. [2021-06-30]. . |
10 | TANG J, WANG K. Personalized top-N sequential recommendation via convolutional sequence embedding [C]// Proceedings of the 11th ACM International Conference on Web Search and Data Mining. New York: ACM, 2018: 565-573. |
11 | XIE X, SUN F, LIU Z, et al. Contrastive learning for sequential recommendation [C]// Proceedings of the 2022 IEEE 38th International Conference on Data Engineering. Piscataway: IEEE, 2022: 1259-1273. |
12 | QIU R, HUAN Z, YIN, H, et al. Contrastive learning for representation degeneration problem in sequential recommendation [C]// Proceedings of the 15th ACM International Conference on Web Search and Data Mining. New York: ACM, 2022: 813-823. |
13 | YANG X-Y, XU F, YU J, et al. Graph neural network-guided contrastive learning for sequential recommendation [J]. Sensors, 2023, 23(12): 5572. |
14 | 夏玉杰,时永鹏,高雅,等.降低滤波器组多载波信号峰均比的边信息嵌入选择性映射方法[J].计算机应用,2021,41(5):1425-1431. |
XIA Y J, SHI Y P, GAO Y, et al. Selected mapping method with embedded side information to reduce PAPR of FBMC signals [J]. Journal of Computer Applications, 2021, 41(5): 1425-1431. | |
15 | 张少东,杨兴耀,于炯,等.基于对比学习和傅里叶变换的序列推荐算法[J].电子科技大学学报,2023,52(4):610-619. |
ZHANG S D, YANG X Y, YU J, et al. Sequence recommendation based on contrast learning and Fourier transform [J]. Journal of University of Electronic Science and Technology of China, 2023, 52(4): 610-619. | |
16 | CHEN B, LUO W, LUO D. Identification of audio processing operations based on convolutional neural network [C]// Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security. New York: ACM, 2018: 73-77. |
17 | ZHOU K, YU H, ZHAO W X, et al. Filter-enhanced MLP is all you need for sequential recommendation [C]// Proceedings of the ACM Web Conference 2022. New York: ACM, 2022: 2388-2399. |
18 | DU X, YUAN H, ZHAO P, et al. Contrastive enhanced slide filter mixer for sequential recommendation [C]// Proceedings of the 2023 IEEE 39th International Conference on Data Engineering. Piscataway: IEEE, 2023: 2673-2685. |
19 | ZHOU K, WANG, H, ZHAO W X, et al. S3-Rec: self-supervised learning for sequential recommendation with mutual information maximization [C]// Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York: ACM, 2020: 1893-1902. |
20 | BIAN Y, HUANG J, CAI X, et al. On attention redundancy: a comprehensive study [C]// Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2021: 930-945. |
21 | BA J L, KIROS J R, HINTON G E. Layer normalization [EB/OL]. [2023-09-01]. . |
22 | SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et.al. Dropout: a simple way to prevent neural networks from overfitting [J]. The Journal of Machine Learning Research, 2014, 15: 1929-1958. |
23 | HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition [C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. |
24 | 刘华锋,景丽萍,于剑.融合社交信息的矩阵分解推荐方法研究综述[J].软件学报,2018,29(2):340-362. |
LIU H F, JING L P, YU J. Survey of matrix factorization based recommendation methods by integrating social information [J]. Journal of Software, 2018, 29(2): 340-362. | |
25 | RENDLE S, FREUDENTHALER C, GANTER Z, et al. BPR: Bayesian personalized ranking from implicit feedback [C]// Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. Arlington, VA: AUAI Press, 2009: 452-461. |
26 | ZHAO W X, MU S, HOU Y, et al. RecBole: towards a unified, comprehensive and efficient framework for recommendation algorithms [C]// Proceedings of the 30th ACM International Conference on Information & Knowledge Management. New York: ACM, 2021: 4653-4664. |
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