Attention-based Graph Convolutional Network for Recommendation System

Chenyuan Feng, Zuozhu Liu, Shaowei Lin, Tony Q.S. Quek

研究成果: Conference contribution

7 引文 斯高帕斯(Scopus)

摘要

Matrix completion with rating data and auxiliary information for users and items is a challenging task in recommendation systems. In this paper, we propose an end-to-end architecture named Attention-based Graph Convolutional Network (AGCN) to embed both rating data and auxiliary information in a unified space, and subsequently learn low-rank dense representations via graph convolutional networks and attention layers. Compared to previous work, AGCN reduces computational complexity with Chebyshev polynomial graph filters. The introduced attention layer, which encourages weighing the neighbor information to learn more expressive structural graph representations, can improve the prediction accuracy, and lead to faster and more stable convergence. Experimental results show that our model can perform better and converge faster than current state-of-the-art methods on the real-world MovieLens and Flixster datasets.

原文English
主出版物標題2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面7560-7564
頁數5
ISBN(電子)9781479981311
DOIs
出版狀態Published - 2019 五月
事件44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
持續時間: 2019 五月 122019 五月 17

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2019-May
ISSN(列印)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
國家/地區United Kingdom
城市Brighton
期間19-05-1219-05-17

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 電氣與電子工程

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