Attention and convolution enhanced memory network for sequential recommendation

Jian Liu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Junhua Fang, Lei Zhao, Victor S. Sheng, Zhiming Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
EditorsYongxin Tong, Jun Yang, Joao Gama, Juggapong Natwichai, Guoliang Li
Number of pages17
ISBN (Print)9783030185787
StatePublished - 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: Apr 22 2019Apr 25 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11447 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
CityChiang Mai


  • Attention mechanism
  • Neural network
  • Sequential recommendation


Dive into the research topics of 'Attention and convolution enhanced memory network for sequential recommendation'. Together they form a unique fingerprint.

Cite this