Strategies of similarity propagation in web service recommender systems

Kai Su; Xuan Zhang; Qing Liu; Bin Xiao; Kai Su; Xuan Zhang; Qing Liu; Bin Xiao

doi:10.3934/mbe.2021029

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 1: 530-550. doi: 10.3934/mbe.2021029

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Strategies of similarity propagation in web service recommender systems

1.
Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China
2.
National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan 430033, China

Received: 18 October 2020 Accepted: 08 December 2020 Published: 11 December 2020

Recently, web service recommender systems have attracted much attention due to the popularity of Service-Oriented Computing and Cloud Computing. Memory-based collaborative filtering approaches which mainly rely on the similarity calculation are widely studied to realize the recommendation. In these research works, the similarity between two users is computed based on the QoS data of their commonly-invoked services and the similarity between two services is computed based on the common users who invoked them. However, most approaches ignore that the similarity calculation is not always accurate under a sparse data condition. To address this problem, we propose a similarity propagation method to accurately evaluate the similarities between users or services. Similarity propagation means that "if A and B are similar, and B and C are similar, then A and C will be similar to some extent". Firstly, the similarity graph of users or services is constructed according to the QoS data. Then, the similarity propagation paths between two nodes on the similarity graph are discovered. Finally, the similarity along each propagation path is measured and the indirect similarity between two users or services is evaluated by aggregating the similarities of different paths connecting them. Comprehensive experiments on real-world datasets demonstrate that our similarity propagation method can outstandingly improve the QoS prediction accuracy of memory-based collaborative filtering approaches.
- web service,
- recommender systems,
- collaborative filtering,
- QoS,
- similarity propagation,
- sparse data
Citation: Kai Su, Xuan Zhang, Qing Liu, Bin Xiao. Strategies of similarity propagation in web service recommender systems[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 530-550. doi: 10.3934/mbe.2021029

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Abstract

Recently, web service recommender systems have attracted much attention due to the popularity of Service-Oriented Computing and Cloud Computing. Memory-based collaborative filtering approaches which mainly rely on the similarity calculation are widely studied to realize the recommendation. In these research works, the similarity between two users is computed based on the QoS data of their commonly-invoked services and the similarity between two services is computed based on the common users who invoked them. However, most approaches ignore that the similarity calculation is not always accurate under a sparse data condition. To address this problem, we propose a similarity propagation method to accurately evaluate the similarities between users or services. Similarity propagation means that "if A and B are similar, and B and C are similar, then A and C will be similar to some extent". Firstly, the similarity graph of users or services is constructed according to the QoS data. Then, the similarity propagation paths between two nodes on the similarity graph are discovered. Finally, the similarity along each propagation path is measured and the indirect similarity between two users or services is evaluated by aggregating the similarities of different paths connecting them. Comprehensive experiments on real-world datasets demonstrate that our similarity propagation method can outstandingly improve the QoS prediction accuracy of memory-based collaborative filtering approaches.

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