Article: Service recommendation through graph attention network in heterogeneous information networks Journal: International Journal of Computational Science and Engineering (IJCSE) 2022 Vol.25 No.6 pp.643 - 656 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. Inderscience Publishers - linking academia, business and industry through research

Title: Service recommendation through graph attention network in heterogeneous information networks

Authors: Fenfang Xie; Yangjun Xu; Angyu Zheng; Liang Chen; Zibin Zheng

Addresses: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China ' School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China ' School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China ' School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China ' School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

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.

Keywords: service recommendation; graph attention network; GAT; co-attention mechanism; heterogeneous information network; HIN.

DOI: 10.1504/IJCSE.2022.127186

International Journal of Computational Science and Engineering, 2022 Vol.25 No.6, pp.643 - 656

Received: 09 Aug 2021
Accepted: 01 Sep 2021
Published online: 25 Nov 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Service recommendation through graph attention network in heterogeneous information networks

Keep up-to-date