Common Topic Group Mining for Web Service Discovery

Wang, Jian; Gao, Panpan; Ma, Yutao; He, Ke

doi:10.1007/978-3-319-26979-5_7

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…With the ever-increasing popularity of SOA (Service Oriented Architecture), many enterprises or organizations turn to encapsulate their complex business processes into lightweight and easy-to-access web services, which significantly facilitate the construction of complex serviceoriented business applications of various users [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Zhou

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., "enemy's enemy is a friend" rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser Rec S BT +CF . During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser Rec S BT +CF approach outperforms other up-to-date approaches.

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Section: Introductionmentioning

confidence: 99%