Motif-Based Graph Attentional Neural Network for Web Service Recommendation

Wang, Guiling
Yu, Jian
Nguyen, Mo
Zhang, Yuqi
Yongchareon, Sira
Han, Yanbo
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Journal Article
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Elsevier BV

Deep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent approaches have utilized high-order connectivity, they still limit themselves to simple interactions and ignore the pattern of structural sub-graphs/motifs. In this study, we first explore the commonly used motifs in the Mashup-API interaction bipartite graph and propose a dedicated algorithm to generate the motif adjacency matrix. We then propose a Motif-based Graph Attention Network for service recommendation (MGSR) that utilizes a motif-based attention mechanism to capture the high-order information of various motifs, and a Collaborative Filtering model to generate the recommendation prediction. We have conducted extensive experiments on ProgrammableWeb dataset and our results demonstrate the superior performance of our proposed framework over some state-of-the-art approaches.

46 Information and Computing Sciences , 4611 Machine Learning , Neurosciences , Networking and Information Technology R&D (NITRD) , 08 Information and Computing Sciences , 15 Commerce, Management, Tourism and Services , 17 Psychology and Cognitive Sciences , Artificial Intelligence & Image Processing , 4602 Artificial intelligence , 4605 Data management and data science , 4611 Machine learning
Knowledge-Based Systems, ISSN: 0950-7051 (Print), Elsevier BV, 110512-110512. doi: 10.1016/j.knosys.2023.110512
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