Recurrent convolutional neural network for sequential recommendation

Chengfeng Xu, Jiajie Xu, Pengpeng Zhao, Victor S. Sheng, Yanchi Liu, Zhiming Cui, Xiaofang Zhou, Hui Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and non-monotone short-term sequential patterns, which are essential for user behavior modeling in sparse sequence data. In this paper, we propose a novel Recurrent Convolutional Neural Network model (RCNN). It not only utilizes the recurrent architecture of RNN to capture complex long-term dependencies, but also leverages the convolutional operation of Convolutional Neural Network (CNN) model to extract short-term sequential patterns among recurrent hidden states. Specifically, we first generate a hidden state at each time step with the recurrent layer. Then the recent hidden states are regarded as an “image”, and RCNN searches non-linear feature interactions and non-monotone local patterns via intra-step horizontal and inter-step vertical convolutional filters, respectively. Moreover, the output of convolutional filters and the hidden state are concatenated and fed into a fully-connected layer to generate the recommendation. Finally, we evaluate the proposed model using four real-world datasets from various application scenarios. The experimental results show that our model RCNN significantly outperforms the state-of-the-art approaches on sequential recommendation.

Original languageEnglish
Title of host publicationThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PublisherAssociation for Computing Machinery, Inc
Pages3398-3404
Number of pages7
ISBN (Electronic)9781450366748
DOIs
StatePublished - May 13 2019
Event2019 World Wide Web Conference, WWW 2019 - San Francisco, United States
Duration: May 13 2019May 17 2019

Publication series

NameThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference2019 World Wide Web Conference, WWW 2019
CountryUnited States
CitySan Francisco
Period05/13/1905/17/19

Keywords

  • Convolutional Neural Network
  • Recurrent Neural Network
  • Sequential Recommendation

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  • Cite this

    Xu, C., Xu, J., Zhao, P., Sheng, V. S., Liu, Y., Cui, Z., Zhou, X., & Xiong, H. (2019). Recurrent convolutional neural network for sequential recommendation. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (pp. 3398-3404). (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313408