Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation

• Published in: Mathematical problems in engineering 2022-04, Vol.2022, p.1-19
• Main Authors: Gao, Wei, Wu, Jian
• Format: Article
• Language: English
• Subjects: Cold; Collaboration; Factorization; Graphs; Information sources; Learning; Mathematical problems; Message passing; Neural networks; Quality of service; Recommender systems; Social networks; Software; Sparsity; Web mashups; Web services
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/3747033

With an increasing number of web services on the Web, selecting appropriate services to meet the developer’s needs for mashup development has become a difficult task. To tackle the problem, various service recommendation methods have been proposed. However, there are still challenges, including the sparsity and imbalance of features, as well as the cold-start of mashups and services. To tackle these challenges, in this paper, we propose a Multigraph Convolutional Network enhanced Neural Factorization Machine model (MGCN-NFM) for service recommendation. It first constructs three graphs, namely, the collaborative graph, the description graph, and the tag graph. Each graph represents a different type of relation between mashups and services. Next, graph convolution is performed on the three graphs to learn the feature embeddings of mashups, services, and tags. Each node iteratively aggregates the information from its higher-order neighbors through message passing in each graph. Finally, the feature embeddings as well as the description features learned by Doc2vec are modeled by the neural factorization machine model, which captures the nonlinear and higher-order feature interaction relations between them. We conduct extensive experiments on the ProgrammableWeb dataset, and demonstrate that our proposed method outperforms state-of-the-art factorization machine-based methods in service recommendation.