TY - GEN
T1 - CFSeRec
T2 - 42nd Chinese Control Conference, CCC 2023
AU - Wang, Tong
AU - Dai, Yaping
AU - Shao, Shuai
N1 - Publisher Copyright:
© 2023 Technical Committee on Control Theory, Chinese Association of Automation.
PY - 2023
Y1 - 2023
N2 - In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks.
AB - In sequential recommender systems, the main problems are the long-tailed distribution of data and noise interference. A Contrastive Framework for Sequential Recommendation (CFSeRec) is proposed to solve these two problems respectively. Token shuffling and adversarial attack data augmentation methods are used in the framework to improve the quality and quantity of training data, so that the long-tailed problem is mitigated. Through the application of projection head method, the sequence representation becomes more general and robust, rather than just adapted to the task of contrastive learning. Therefore, the impact of noise on sequence recommender systems is effectively alleviated. Experiments on four public datasets show that CFSeRec achieves state-of-the-art performance in the metrics of hit ratio and normalized discounted cumulative gain, when comparing to the seven previous frameworks.
KW - Attention Mechanism
KW - Contrastive Learning
KW - Self-supervised Learning
KW - Sequential Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85175570654&partnerID=8YFLogxK
U2 - 10.23919/CCC58697.2023.10240619
DO - 10.23919/CCC58697.2023.10240619
M3 - Conference contribution
AN - SCOPUS:85175570654
T3 - Chinese Control Conference, CCC
SP - 8211
EP - 8216
BT - 2023 42nd Chinese Control Conference, CCC 2023
PB - IEEE Computer Society
Y2 - 24 July 2023 through 26 July 2023
ER -