Wang, GuilingYu, JianNguyen, MoZhang, YuqiYongchareon, SiraHan, Yanbo2023-04-122023-04-122023-03-27Knowledge-Based Systems, ISSN: 0950-7051 (Print), Elsevier BV, 110512-110512. doi: 10.1016/j.knosys.2023.1105120950-7051https://hdl.handle.net/10292/16076Deep 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.http://creativecommons.org/licenses/by-nc-nd/4.0/46 Information and Computing Sciences4611 Machine LearningNeurosciencesNetworking and Information Technology R&D (NITRD)08 Information and Computing Sciences15 Commerce, Management, Tourism and Services17 Psychology and Cognitive SciencesArtificial Intelligence & Image Processing4602 Artificial intelligence4605 Data management and data science4611 Machine learningJournal ArticleOpenAccess10.1016/j.knosys.2023.110512