Web services recommendation based on Metapath-guided graph attention network

Li, Xiaoyü; Zhang, Xiuguo; Wang, Peipeng; Cao, Zhiying

doi:10.1007/s11227-022-04369-8

Cited by 13 publications

(2 citation statements)

References 32 publications

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“…7. WSR-MGAT (Li et al,2022): The Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. It uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors.…”

Section: Methods Comparisonmentioning

confidence: 99%

A Service Recommendation Algorithm Based on Self-Attention Mechanism and DeepFM

Deng,

Guo,

Zheng

2023

International Journal of Web Services Research

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This article proposes a recommendation model based on self-attention mechanism and DeepFM service, the model is SelfA-DeepFM. The method firstly constructs the service network with DTc-LDA model to mine the potential relationship between Mashup and API, which not only fully considers the text attributes but also combines the network structure information to effectively mitigate the sparsity of the service data. Secondly, service clustering to obtain numerical feature similarities. Finally, the self-attention mechanism is used to capture the different importance of feature interactions, and the DeepFM model is used to mine the complex interaction information between multidimensional features to predict and rank the quality score of API services to recommend suitable APIs. To verify the performance of the model, the authors use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the model significantly improves the accuracy of service recommendation.

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Section: Methods Comparisonmentioning

confidence: 99%

A Service Recommendation Algorithm Based on Self-Attention Mechanism and DeepFM

Deng,

Guo,

Zheng

2023

International Journal of Web Services Research

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“…It has advantages such as high efciency and portability [27]. For example, the graph attention network can complete the relationship weight between diferent nodes and improve the accuracy of recommendations according to users' intimacy and the interaction behavior of users participating in different activities [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph

Wan,

Wu,

Liu

et al. 2024

International Journal of Intelligent Systems

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Besides data sparsity and cold start, recommender systems often face the problems of selection bias and exposure bias. These problems influence the accuracy of recommendations and easily lead to overrecommendations. This paper proposes a recommendation approach based on heterogeneous network and dynamic knowledge graph (HN-DKG). The main steps include (1) determining the implicit preferences of users according to user’s cross-domain and cross-platform behaviors to form multimodal nodes and then building a heterogeneous knowledge graph; (2) Applying an improved multihead attention mechanism of the graph attention network (GAT) to realize the relationship enhancement of multimodal nodes and constructing a dynamic knowledge graph; and (3) Leveraging RippleNet to discover user’s layered potential interests and rating candidate items. In which, some mechanisms, such as user seed clusters, propagation blocking, and random seed mechanisms, are designed to obtain more accurate and diverse recommendations. In this paper, the public datasets are used to evaluate the performance of algorithms, and the experimental results show that the proposed method has good performance in the effectiveness and diversity of recommendations. On the MovieLens-1M dataset, the proposed model is 18%, 9%, and 2% higher than KGAT on F1, NDCG@10, and AUC and 20%, 2%, and 0.9% higher than RippleNet, respectively. On the Amazon Book dataset, the proposed model is 12%, 3%, and 2.5% higher than NFM on F1, NDCG@10, and AUC and 0.8%, 2.3%, and 0.35% higher than RippleNet, respectively.

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