Dual-Channel Convolutional Recurrent Networks for Session-Based Recommendation

Jing Jing Wang, Lap Kei Lee, Nga In Wu

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


Recommender systems assist a Web application user in satisfying their needs or interests based on the user profile and past activities. Yet due to privacy and other concerns, some applications and services only keep anonymous information. A session-based recommender system (SRS) predicts the next item by exploring only anonymous user-item behavior orders during ongoing sessions. Recurrent neural networks (RNNs) and their two variants have dominated the research on SRS. However, there are two shortcomings in these RNN-based methods: (1) RNNs easily generate false dependencies because RNNs assume all adjacent items are highly dependent on each other; (2) the sequentially connected architecture of RNNs can only capture the point-level dependencies but ignoring neglecting the union-level dependencies. This paper proposes a Dual-channel Convolutional Recurrent Neural Network (D-CRNN) model to address these problems. This hybrid model leverages RNN to explore complex long-term dependencies and combines CNN to extract the union-level context features, which help to reduce the noise. The hybrid model was evaluated on three commonly used real-world datasets. The experimental results on Diginetica dataset D-CRNN showed an improvement of 5.8% and 4.8% respectively in terms of Recall@10 and MRR@10, demonstrating the effectiveness of D-CRNN on the session-based recommendation.

Original languageEnglish
Title of host publicationCyber Security, Privacy and Networking - Proceedings of ICSPN 2021
EditorsDharma P. Agrawal, Nadia Nedjah, B. B. Gupta, Gregorio Martinez Perez
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9789811686634
Publication statusPublished - 15 May 2022
EventInternational Conference on Cyber Security, Privacy and Networking, ICSPN 2021 - Virtual, Online
Duration: 17 Sept 202119 Sept 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Cyber Security, Privacy and Networking, ICSPN 2021
CityVirtual, Online


  • Convolutional neural networks
  • Hybrid neural networks
  • Recurrent neural networks
  • Session-based recommendation


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