research-article

Motif-based Graph Attention Network for Web Service Recommendation

Authors:

Guiling WangAuthors Info & Claims

ACSW '23: Proceedings of the 2023 Australasian Computer Science Week

Pages 143 - 146

https://doi.org/10.1145/3579375.3579393

Published: 13 March 2023 Publication History

Abstract

Deep learning on graphs and especially graph convolutional networks (GCNs) have shown superior performance in collaborative filtering. Most GCNs have a message-passing architecture that enables nodes aggregate information from neighbours continuously through multiple layers. This also leads to a common issue called over-smoothing: as the number of layers increases, the node embeddings become similar and the performance of tasks such as link prediction degrades severely. In this work, we propose motif-based graph attention network for web service recommendation (MGSR) that alleviates the over-smoothing issue by incorporate network motifs in layer propagation. Extensive experiments show that our model outperforms state-of-the-art approaches.

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Cited By

Xiao LMadanian SLi WXiao Y(2023)Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven ApproachesData Science and Machine Learning10.1007/978-981-99-8696-5_16(226-238)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-981-99-8696-5_16

Index Terms

Motif-based Graph Attention Network for Web Service Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web services
      1. Service discovery and interfaces

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Graphical abstract

Display Omitted
Highlights
- We report a Motif-based Graph Attention Network for service recommendation (MGSR).
- The model aggregates high-order information of motif-based neighbors.
- The model identifies all the seven up-to-four-node motifs in bipartite graphs.
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ACSW '23: Proceedings of the 2023 Australasian Computer Science Week

January 2023

272 pages

ISBN:9798400700057

DOI:10.1145/3579375

Copyright © 2023 ACM.

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New York, NY, United States

Publication History

Published: 13 March 2023

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International Cooperation and Exchanges NSFC
National Natural Science Foundation of China

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ACSW 2023

ACSW 2023: 2023 Australasian Computer Science Week

January 30 - February 3, 2023

VIC, Melbourne, Australia

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Overall Acceptance Rate 61 of 141 submissions, 43%

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Cited By

Xiao LMadanian SLi WXiao Y(2023)Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven ApproachesData Science and Machine Learning10.1007/978-981-99-8696-5_16(226-238)Online publication date: 5-Dec-2023
https://doi.org/10.1007/978-981-99-8696-5_16

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