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Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13757))

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Abstract

Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user’s decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining.

In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users’ friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included (https://github.com/trantin84/WiGCN).

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VŠB -Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Tin T. Tran & Václav Snasel

  2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tin T. Tran

Authors
  1. Tin T. Tran
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  2. Václav Snasel
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Corresponding author

Correspondence to Tin T. Tran .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Tran, T.T., Snasel, V. (2022). Improvement Graph Convolution Collaborative Filtering with Weighted Addition Input. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_51

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  • DOI: https://doi.org/10.1007/978-3-031-21743-2_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21742-5

  • Online ISBN: 978-3-031-21743-2

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