TY - GEN
T1 - Modeling Periodic Pattern with Self-Attention Network for Sequential Recommendation
AU - Ma, Jun
AU - Zhao, Pengpeng
AU - Liu, Yanchi
AU - Sheng, Victor S.
AU - Xu, Jiajie
AU - Zhao, Lei
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Repeat consumption is a common phenomenon in sequential recommendation tasks, where a user revisits or repurchases items that (s)he has interacted before. Previous researches have paid attention to repeat recommendation and made great achievements in this field. However, existing studies rarely considered the phenomenon that the consumers tend to show different behavior periodicities on different items, which is important for recommendation performance. In this paper, we propose a holistic model, which integrates Graph Convolutional Network with Periodic-Attenuated Self-Attention Network (GPASAN) to model user’s different behavior patterns for a better recommendation. Specifically, we first process all the users’ action sequences to construct a graph structure, which captures the complex item connection and obtains item representations. Then, we employ a periodic channel and an attenuated channel that incorporate temporal information into the self-attention mechanism to model the user’s periodic and novel behaviors, respectively. Extensive experiments conducted on three public datasets show that our proposed model outperforms the state-of-the-art methods consistently.
AB - Repeat consumption is a common phenomenon in sequential recommendation tasks, where a user revisits or repurchases items that (s)he has interacted before. Previous researches have paid attention to repeat recommendation and made great achievements in this field. However, existing studies rarely considered the phenomenon that the consumers tend to show different behavior periodicities on different items, which is important for recommendation performance. In this paper, we propose a holistic model, which integrates Graph Convolutional Network with Periodic-Attenuated Self-Attention Network (GPASAN) to model user’s different behavior patterns for a better recommendation. Specifically, we first process all the users’ action sequences to construct a graph structure, which captures the complex item connection and obtains item representations. Then, we employ a periodic channel and an attenuated channel that incorporate temporal information into the self-attention mechanism to model the user’s periodic and novel behaviors, respectively. Extensive experiments conducted on three public datasets show that our proposed model outperforms the state-of-the-art methods consistently.
KW - Periodic pattern
KW - Self-attention network
KW - Sequential recommendation
UR - http://www.scopus.com/inward/record.url?scp=85092116745&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59419-0_34
DO - 10.1007/978-3-030-59419-0_34
M3 - Conference contribution
AN - SCOPUS:85092116745
SN - 9783030594183
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 557
EP - 572
BT - Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
A2 - Nah, Yunmook
A2 - Cui, Bin
A2 - Lee, Sang-Won
A2 - Yu, Jeffrey Xu
A2 - Moon, Yang-Sae
A2 - Whang, Steven Euijong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Y2 - 24 September 2020 through 27 September 2020
ER -