Combining Social Balance Theory and Collaborative Filtering for Service Recommendation in Sparse Environment

Qi, Lianyong; Dou, Wanchun; Zhang, Xuyun

doi:10.1007/978-3-319-49178-3_28

Cited by 1 publication

(1 citation statement)

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“…We compare our Ser Rec S BT +CF approach with other three ones, i.e., WSRec [9], Rec S BT +CF [13] and coreusers-NBI [11]. Concretely, in WSRec, r * (user target ) (i.e., user target 's average rating over all his/her invoked services) and r * (ws j ) (i.e., ws j 's average rating from all the users who invoked ws j ) are considered.…”

Section: Experiments Dataset and Deploymentmentioning

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: Experiments Dataset and Deploymentmentioning

confidence: 99%