Web services recommendation leveraging semantic similarity computing

Hu, Boran; Cheng, Zehui; Zhou, Zhangbing

doi:10.3934/mfc.2018006

Cited by 2 publications

References 32 publications

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Personalized Service Recommendation With Mashup Group Preference in Heterogeneous Information Network

et al. 2019

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In the service network, there exist various objects and rich relations among them. These various objects and rich relations naturally form a heterogeneous information network. Service recommendations can help users to locate their desired services. Previous service recommendation studies mainly aim at homogeneous networks or consider few kinds of relations rather than using the rich heterogeneous information. In this paper, we propose a mashup group preference-based service recommendation method in the heterogeneous information network for mashup creation. First of all, we analyze the historical invocation records between mashups and services and exploit the heterogeneous information to construct diverse meta paths with different semantic meanings. Then, we measure the similarity between the starting object and the ending object from different perspectives and integrate different similarity measures to obtain the hybrid similarity. Next, we introduce group preference to capture the rich interactions among mashups and apply a group preference-based Bayesian personalized ranking algorithm to learn the model. Finally, we recommend a list of personalized ranking services for mashup developers. A series of experiments conducted on a realworld dataset demonstrate the superiority of our proposed approach over other baseline approaches. INDEX TERMS Heterogeneous information network, meta path, service recommendation, group preference.

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