Enhanced similarity measure for personalized cloud services recommendation
Enhanced similarity measure for personalized cloud services recommendation
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DOI：
10.1002/cpe.4020
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Yasmine M. Afify;Ibrahim F. Moawad;N. Badr;M. Tolba
中科院分区：
文献类型：
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作者：
Yasmine M. Afify;Ibrahim F. Moawad;N. Badr;M. Tolba
云用户被海量的云服务淹没了。服务推荐系统根据用户需求对提供相同功能的服务进行评价。在推荐系统中准确推荐的一个关键因素是对相似用户的适当确定。本文对个性化云服务推荐领域做出了贡献。具体地说，我们引入了一种基于用户的相似性度量，它综合了用户人口统计信息、服务评级和用户兴趣等相关方面的相似性。提出的相似性度量被用于混合协同过滤(CF)方法，该方法利用基于模型和基于记忆的方法的优点来改进推荐过程。在真实服务数据集上的实验评估表明，该方法在保持较好或相同覆盖率的情况下，在预测准确率和推荐时间方面均优于其他预测方法。
Cloud users are overwhelmed with great numbers of cloud services. Service recommender systems evaluate the services that provide same functionalities according to the user requirements. A key enabler to accurate recommendation in recommender systems is the appropriate determination of similar users. This paper contributes to the personalized cloud services recommendation area. In specific, we introduce a user‐based similarity measure that integrates relevant similarity aspects: user demographic information, service ratings, and user interest. The proposed similarity measure is used in a hybrid collaborative filtering (CF) approach that leverages the advantages of both model‐ and memory‐based approaches to improve the recommendation process. Experimental evaluation on real‐world services data set shows that the proposed approach outperforms other CF approaches in respect of the prediction accuracy and recommendation time while maintaining better or same coverage.