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QoS prediction for web services based on user-trust propagation model
New Review of Hypermedia and Multimedia ( IF 1.2 ) Pub Date : 2017-10-02 , DOI: 10.1080/13614568.2017.1416681
Le-Van Thinh 1 , Truong-Dinh Tu 2
Affiliation  

ABSTRACT There is an important online role for Web service providers and users; however, the rapidly growing number of service providers and users, it can create some similar functions among web services. This is an exciting area for research, and researchers seek to to propose solutions for the best service to users. Collaborative filtering (CF) algorithms are widely used in recommendation systems, although these are less effective for cold-start users. Recently, some recommender systems have been developed based on social network models, and the results show that social network models have better performance in terms of CF, especially for cold-start users. However, most social network-based recommendations do not consider the user’s mood. This is a hidden source of information, and is very useful in improving prediction efficiency. In this paper, we introduce a new model called User-Trust Propagation (UTP). The model uses a combination of trust and the mood of users to predict the QoS value and matrix factorisation (MF), which is used to train the model. The experimental results show that the proposed model gives better accuracy than other models, especially for the cold-start problem.

中文翻译:

基于用户信任传播模型的Web服务QoS预测

摘要 Web 服务提供者和用户在网上扮演着重要的角色。然而,随着服务提供者和用户数量的迅速增长,它可以在网络服务之间创建一些类似的功能。这是一个令人兴奋的研究领域,研究人员寻求为用户提供最佳服务的解决方案。协同过滤 (CF) 算法广泛用于推荐系统,尽管这些算法对冷启动用户的效果较差。最近,基于社交网络模型开发了一些推荐系统,结果表明社交网络模型在CF方面有更好的性能,尤其是对于冷启动用户。然而,大多数基于社交网络的推荐并没有考虑用户的情绪。这是一个隐藏的信息源,对于提高预测效率非常有用。在本文中,我们引入了一种称为用户信任传播 (UTP) 的新模型。该模型使用信任和用户情绪的组合来预测 QoS 值和矩阵分解 (MF),用于训练模型。实验结果表明,所提出的模型比其他模型具有更好的精度,特别是对于冷启动问题。
更新日期:2017-10-02
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