Service recommendation through graph attention network in heterogeneous information networks
by Fenfang Xie; Yangjun Xu; Angyu Zheng; Liang Chen; Zibin Zheng
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 6, 2022

Abstract: Recommending suitable services to users autonomously has become the key to solve the problem of service information overload. Existing recommendation algorithms have some limitations, and discard the side information of the node, or ignore the information of the intermediate node, or omit the feature information of the neighbouring nodes, or not model the pairwise attentive interactions between users and services. To solve the above-mentioned limitations, this paper proposes a service recommendation approach by leveraging the graph attention network (GAT) and co-attention mechanism in heterogeneous information networks (HINs). Specifically, different types of meta-paths are first constructed, and a feature expression is learned for each node in HINs. Then, the feature information of mashups/services is aggregated by the co-attention mechanism. Finally, the multi-layer perceptron (MLP) is applied to recommend suitable services for users. Experiments on a real-world dataset illustrate that the proposed method outperforms other state-of-the-art comparison methods.

Online publication date: Fri, 25-Nov-2022

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