TY - GEN
T1 - Attention and convolution enhanced memory network for sequential recommendation
AU - Liu, Jian
AU - Zhao, Pengpeng
AU - Liu, Yanchi
AU - Xu, Jiajie
AU - Fang, Junhua
AU - Zhao, Lei
AU - Sheng, Victor S.
AU - Cui, Zhiming
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. Conventionally, user general taste and recent demand are combined to promote recommendation performance. However, existing methods usually neglect that user long-term preference keeps evolving over time and only use a static user embedding to model the general taste. Moreover, they often ignore the feature interactions when modeling short-term sequential patterns and integrate user-item or item-item interactions through a linear way, which limits the capability of model. To this end, we propose an Attention and Convolution enhanced memory network for Sequential Recommendation (ACSR) in this paper. Specifically, an attention layer learns user’s general preference, while the convolutional layer searches for feature interactions and sequential patterns to capture user’s sequential preference. Moreover, the outputs of the attention layer and the convolutional layer are concatenated and fed into a fully-connected layer to generate the recommendation. This approach provides a unified and flexible network structure for capturing both general taste and sequential preference. Finally, we evaluate our model on two real-world datasets. Extensive experimental results show that our model ACSR outperforms the state-of-the-art approaches.
AB - The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. Conventionally, user general taste and recent demand are combined to promote recommendation performance. However, existing methods usually neglect that user long-term preference keeps evolving over time and only use a static user embedding to model the general taste. Moreover, they often ignore the feature interactions when modeling short-term sequential patterns and integrate user-item or item-item interactions through a linear way, which limits the capability of model. To this end, we propose an Attention and Convolution enhanced memory network for Sequential Recommendation (ACSR) in this paper. Specifically, an attention layer learns user’s general preference, while the convolutional layer searches for feature interactions and sequential patterns to capture user’s sequential preference. Moreover, the outputs of the attention layer and the convolutional layer are concatenated and fed into a fully-connected layer to generate the recommendation. This approach provides a unified and flexible network structure for capturing both general taste and sequential preference. Finally, we evaluate our model on two real-world datasets. Extensive experimental results show that our model ACSR outperforms the state-of-the-art approaches.
KW - Attention mechanism
KW - Neural network
KW - Sequential recommendation
UR - http://www.scopus.com/inward/record.url?scp=85065497055&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-18579-4_20
DO - 10.1007/978-3-030-18579-4_20
M3 - Conference contribution
AN - SCOPUS:85065497055
SN - 9783030185787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 333
EP - 349
BT - Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
A2 - Tong, Yongxin
A2 - Yang, Jun
A2 - Gama, Joao
A2 - Natwichai, Juggapong
A2 - Li, Guoliang
PB - Springer-Verlag
T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Y2 - 22 April 2019 through 25 April 2019
ER -